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The global generative AI (GenAI) market is surging, with major players like Google, Microsoft, and Anthropic driving rapid innovation in large language models (LLMs). Open-source LLM builders such as Meta (with Llama) and Mistral are democratizing access to this technology, making it more attainable for smaller companies and researchers.
This growth isn’t just hype. The capabilities of LLMs—whether used to generate content, automate tasks, or enhance customer experiences—are real and transformative. But the question remains:
Will your company be able to benefit from these advancements?
While the potential of GenAI is undeniable, actual enterprise adoption is lagging. A 2023 survey revealed that only 33% of organizations are using generative AI1. This gap in adoption stems not from a lack of interest but from significant challenges.
The good news is that these challenges aren’t insurmountable. Companies that succeed in adopting LLMs do so by focusing on strategic, thoughtful implementation rather than chasing the hype.
As LLMs continue to evolve, the gap between early adopters and laggards will widen. To stay ahead, companies must prepare for the next wave of technological advancements:
In addition to technological advancements, costs are dropping rapidly, making it easier for companies to adopt LLMs. Cloud providers are reducing prices, with some models now available at up to 80% lower costs than a year ago. Additionally, more efficient, smaller LLMs are becoming available, allowing businesses to deploy these technologies without requiring massive computational power.
Open-source models and fine-tuning techniques also enable companies to customize LLMs at a fraction of the cost of building models from scratch. While these cost savings are significant, companies still need the right strategy to extract meaningful value from LLM integration.
The rise of GenAI and LLMs offers tremendous opportunities for businesses ready to harness their potential. However, only those who strategically invest in overcoming technical hurdles and aligning AI initiatives with business objectives will truly benefit.
The real question you need to ask is: Is your business prepared to capitalize on the coming wave of LLM advancements? As costs drop and capabilities increase, those who are ready will see gains in efficiency, productivity, and profitability. Those who aren’t may find themselves left behind.
Now is the time to act. To discover how you can work with Ushur to implement generative AI capabilities into your business for better customer experience, contact us at https://ushur.com/request-demo.
1. https://aiindex.stanford.edu/report/
The EU AI Act entered into force last week on August 1st, 2024 and it marks a substantial milestone of governmental bodies trying to maintain the safety of their citizens and businesses when both the private and public sector use artificial intelligence. This post details the scope and consequences of the act, especially for the regulated businesses for which Ushur provides automation applications. Ushur has an international presence and so has been considering its consequences and thinking of how best our customers can automate their CX in this context — where automation is more often driven by AI than not.
The EU AI Act (AI Act) deploys a risk-based approach in which it categorizes the use case in the application of AI into one of four groups: unacceptable risk, high risk, limited risk, and minimal risk.
The application of technology systems like social scoring, and behavioral manipulation towards dangerous behavior represent an unacceptable risk. There is no appetite in the EU for having AI systems emotionally manipulate people or businesses.
The next group, high risk applications, includes examples like technological systems that support critical infrastructure or law enforcement systems. There are not specific examples provided in the act itself, but it’s not hard to intuit the kinds of system it intends to regulate closely.
A more common consideration, however, is how the AI Act affects applications like chatbots; categorized within the limited risk group. Chatbots look like many of the deployments Ushur clients are familiar with because the Ushur CXA Platform utilizes automation technologies, machine learning and generative AI to make customer experiences more fluid, flexible and powerful. So what does the EU AI Act say about them?
Chatbots are increasingly common as an interface between brands and their customers, and the EU AI Act says that those engagements need to make it clear and transparent when a customer is interacting with a machine. In other words, AI Agents deployed to engage with customers can’t hide the fact that they are not real humans!
The implications of the EU AI Act are interesting as consumers, but significant as businesses consider which technology partners can or cannot help them navigate these varieties of novel regulation. At Ushur, we build our chat interfaces and AI features to comply and according to best practices so customers always understand with whom they’re engaging.
Ushur has been thinking about the risks of AI services since the company’s inception, and we consider more than just the categories of risk included in the EU AI Act.
There are also concerns to remember like reputational risk associated with misdirected AI solutions. What does your business stand to lose reputationally if its GenAI chatbot interface is convinced by a bad actor to speak in hateful language? Or to show up in the news as having been manipulated for commercial gain?
Consider also the dangers of operational risks like having long-building AI projects stall. Businesses spend months and millions of dollars to deploy responsive and flexible AI-first solutions, and the wrong partners, with insufficient understanding of how to deploy enterprise-grade applications, put their deployment in jeopardy.
Ushur has spent a decade operationalizing enterprise-quality automated customer experiences. We have deployed document processing models to accelerate work that depends on document-bound data, and leveraged conversational automation to better understand the people engaging with its platform. The company was founded as an automation and AI platform from day 1 and has this built into our DNA, from the models we train and place guardrails around, to the security and compliance of our platform.
It’s exciting to think about customer experiences driven by AI-first capabilities, and troubling to business leaders who feel like they are missing out.
When evaluating whether AI-driven projects are the right fit for your business, while it’s important to think about the risks that arise during and post implementation, don’t forget the danger associated with choosing not to use AI. Internal and external processes will eventually grow to be unwieldy without the support of automation, and AI-first automation solutions are more flexible than deterministic solutions alone.
Compared to some technologies, like data storage, artificial intelligence-based projects are relatively new. The risks feel more substantial but they are largely understood. The quality of the outcome of the projects depends on the cleanliness and quality of the data associated with the project, and having advisors with expertise. Expertise and technology with proven success make implementation and utilization of AI technologies possible.
At Ushur, we believe AI built and implemented to improve the lives of everyone should always be implemented in safe, compliant ways — and we take the necessary steps to ensure all organizations can implement CXA solutions to provide their customers with frictionless, engaging, and fulfilling experiences.
The EU AI Act which has just gone into full force is an example of how the world is responding with caution to the newest and most exciting developments of artificial intelligence. Technology is advancing quickly, so legislators are developing frameworks to respond accordingly.
Businesses around the world with an international presence must know how to respond to changes in the legal landscape, and at the least must have technology partners they’re close with who can help guide them through the evolving implications. If the risks associated with AI that drive your customer experiences are critical to strategize around, Ushur is here to support you. With a decade of experience building, designing, and automating customer experiences in regulated enterprises to be secure and compliant, Ushur can serve as your close technology and digital transformation partner. Get in touch with us at ushur.com/request-demo.
Generative AI for the EnterpriseThe week before last, Ushur attended the inaugural GenAI Summit at the Palace of the Fine Arts in San Francisco, CA. It was an incredible forum for speaking with and listening to the industry’s experts who are defining the future of Generative AI. At the summit, Ushur’s attendees also got the chance to ask questions about the latest release of the Ushur Customer Experience Automation (CXA) platform and its introduction of Ushur ExperienceOS (XOS) — an entirely new way to build customer experiences. XOS is the generative AI (GenAI) services backbone of the Ushur platform with a specific focus on using large language model capabilities to design engaging AI Agents for regulated industries, and to help Ushur users in the leading CXA platform.
XOS is the Generative AI (GenAI) service for launching AI Agents that help customers across several core competencies. It’s the backbone to GenAI services in the Ushur platform designed to bring the power of large language models (LLMs) to enterprise customer experiences. XOS hosts Ushur LLMs, open-source LLMs, or models trained and deployed by customers. Business users can use XOS to launch AI Agents — digital counterparts to the customer service representatives that customers love. Ushur AI Agents are trained to help customers and users in conversations over chat or voice, document processing, data report generation, and decision assistance for employees.
If Ushur users don’t want to rely on XOS to deploy AI Agents, they can leverage the power of LLMs via XOS in Ushur Studio. GenAI services help users as well as customers with tasks like workflow creation, phrase suggestions, and tone improvements to meet CX best practices. XOS is the operating system for all customer experiences that demand the fluidity of Generative AI, created with the ease of drag-and-drop. And, it’s available today!
One of the most common questions asked at the Ushur booth at the GenAI Summit in SF was “What are AI Agents?”
Artificial Intelligence Agents (AI Agents) by Ushur are digital customer service specialists that can be trained to adhere to a company’s industry or business use cases and automate processes for customer requests conversationally via chat, email, or voice, and securely gather and process documents for secure/PII customer experiences. These AI Agents are also able to help customer service agents make decisions via business rules and generate insights via data querying. Each of these capabilities are deployable out of the new Ushur GenAI service and accessible to customers via a conversational interface. AI Agents are the ultimate manifestation of GenAI in the Ushur CXA ecosystem.
To better understand AI Agents, it’s useful to think of them by comparison to their predecessor the chatbot. AI Agents powered by genAI can be trained and skilled to deliver insights, make decisions, and dynamically communicate at a higher order of reasoning and intelligence. Chatbots are generally pre-directed to respond with preset responses to specific FAQs that were accounted for. Reasoning and understanding are limited with traditional chatbots.
AI Agents have to complete end-to-end automation and drive towards straight through processing. They do of course also support agent escalation or diversion to a person if the customer needs (or prefers) it.
An AI Agent is as much defined by preconceived notions of related technologies as it is by anything else. They exist on a gradient of capabilities and are generally understood as the next iteration of intelligent assistants that have occupied the market for a decade. They’re more than rules-based chatbots in front of an FAQ set. Now, enterprises must decide what level of autonomy to give their AI Agents, and what skills their agents need in order to best deliver self-service to their customers.
Conversational capabilities are critically important for any agent that’s required to assist customers with self-service. Customers want to engage in the language or interface of their choice, and chatting has emerged as the de facto engagement style users prefer across all demographics. Chatting can be via text or web chat, but what’s clear is customers want to be able to speak to their service providers at their leisure and in their own verbiage.
Especially with the emergence of conversational capabilities of ChatGPT, the tolerance for poor chat interfaces has shrunk dramatically in the last two years. Fortunately, Ushur AI Agents skilled with chat use the power of LLM-based conversations to deliver a fluid and dynamic engagement but with the safety and compliance of Ushur’s industry-specific models and GenAI guardrails.
Customer experiences often depend on information already locked into a document-based format, and customers don’t want to repeatedly provide their data to companies ad nauseam. That’s why document processing skills for AI Agents are a requisite for any customer experience in the enterprise. Customers demand they be able to engage friction-free, and waiting for a customer service member to extract data from documents introduces immense friction.
With the advent of large language models, Ushur’s AI Agents are now better suited than ever to understand and correct the information included in the documents that customers share with their service providers. Forms (like pdfs, faxes, surveys etc.) are a mainstay of enterprise processes, and Ushur’s LLMs can be used to review and confirm the data extracted for those processes is accurate. Ushur can also use the power of LLMs to reach back out to customers when data in a document looks incorrect and ask for an update.
AI Agents are not just an improvement to existing SaaS platforms — like traditional machine learning projects have been – but are instead valuable in the ways adding another teammate to a customer service team would be. That’s why AI Agents that support self-service require skills like the ability to share data reports with other team members, to help other team members make decisions based on business practices, and to hold an initial dialogue with a customer over a channel like voice.
These skills are impactful because:
The news and internet are awash in examples of GenAI deployed without the kinds of bumpers that keep enterprise experiences on target. It’s a technology stack that is proving to be exceptionally compelling, while also a concern if bad actors can prompt-engineer those services into dangerous behaviors.
Ushur is specifically purpose-built to deliver secure, compliant, and closely guided experiences. Ushur services act as guardrails by validating all inbound prompts, and reviewing all generated answers to ensure they’re not off topic or serving up information via prompt attacks.
The unique value in the design of Ushur customer experiences is how they are goal oriented. That makes it easy for Ushur XOS and its GenAI capabilities to know when engagements are off topic, and then to guide users back through the experiences they’re intended to complete.
Ushur XOS has fundamentally changed the way that consumers engage with Ushur customers, and reimagined the process Ushur users have to go through to build customer experiences. The GenAI Summit was the perfect opportunity to validate those viewpoints, and show excited attendees that Ushur is thinking about how GenAI can make their customer experiences more powerful while preserving safety and security. If you want to see how AI Agents built via XOS are changing the way customers engage in the enterprise, come schedule time with us at ushur.com/xos.
AI Agents have been making a ton of headlines over the past few weeks, but what exactly are AI agents and how should we be thinking about them? It’s helpful to start by going back to AI Assistants, which were all the rage before AI agents took over. Let's explore the upgrades that transformed AI Assistants into the AI Agents we have today.
When ChatGPT launched in 2022, it took the world by storm. Its conversational style, ability to understand user intent, and creative outputs were revolutionary. However, there were clear limitations in this early AI assistant framework that have since been addressed, leading us to the development of AI agents.
One of the first improvements was incorporating better instructions and direction for AI assistants. This process, known as prompt engineering or custom instructions, involves being clear about what we expect from these AI assistants, including the personas they should assume and behaviors to avoid.
Another significant breakthrough was enabling AI assistants to reference documents before responding to a user. Initially, AI assistants could confidently produce incorrect outputs and lacked domain-specific or company knowledge. Now, with access to source material, they can incorporate relevant information into their responses, reducing errors and increasing expertise in specific fields.
Originally, ChatGPT had a gap in its training dataset, meaning it couldn’t provide information on recent events. By integrating search capabilities, AI assistants can now look up and incorporate current information, ensuring they stay relevant and up-to-date.
AI assistants have also gained access to tools like calculators and programming environments. Large language models are inherently text-based, so adding mathematical engines like Wolfram Alpha or dedicated compute environments allows them to handle complex tasks more effectively.
Using advanced prompt engineering and orchestration techniques, AI assistants can now plan steps in advance to solve more complex problems. They also have enhanced troubleshooting capabilities, allowing them to manage more complicated projects seamlessly.
A major upgrade is the ability of AI assistants to interact with external systems. They can now pull data from business systems and push tasks to external systems for completion, significantly expanding their utility.
Recent advancements have enabled AI assistants to interact with users beyond just text. Users can now upload images and, soon, leverage cameras and video, opening up a range of new use cases.
These advancements have culminated in the creation of AI agents, which are better equipped to handle complex problems. They can plan ahead, troubleshoot effectively, use advanced tools, and interact with users in innovative ways. AI agents also perform actions in the real world by integrating with other systems.
Despite their promise, AI agents present a few challenges:
The AI community is excited about AI agents, and major players like OpenAI and Google are launching their versions. Companies like Ushur are developing AI agents tailored for customer experience automation in sensitive industries such as healthcare, insurance, and financial services.
Thank you to Amanda Dall’Occhio, Healthcare Sr. Director Analyst at Gartner, for shedding light on the myriad of ways in which AI can enhance care management in: U.S. Payer and Provider CIOs: Apply AI in Care Management Programs. We agree that opportunities are prevalent throughout the value chain for both internal and external tasks. A thoughtful, intentional approach to AI is key, which was underscored insightfully with your guidance on weighing the value-add against potential risks. We appreciate your nod to Ushur as a sample vendor driving progress in this space.
At Ushur, our objective is to elevate member engagement and streamline care navigation through AI-powered digital experiences. This includes:
Ushur’s platform supports enterprise-class security and compliance, ensuring a smooth Infosec process for data privacy and handling. Our No-Code Integrations connect critical systems and channels for end-to-end workflows. For greater precision and time-to-value, our Healthcare solution comes pre-trained with language and document intelligence for industry-specific skills.
Providing a convenient, empathetic and flexible experience makes all the difference during member and patient times of need. At Ushur, we strive to make all self-service proactive, personalized, and seamlessly integrated into the fabric of daily life.
For more information on how to leverage AI for Healthcare customer experiences, please visit: https://ushur.com/resources/healthcare-ebook.
In the last major release of the Ushur platform, Ushur included a key feature to make it easier for citizen developers to build, deploy, and connect customer experiences – the Ushur launch module.
The Ushur Customer Experience Automation platform uses a no-code platform and pre-trained (and easily retrainable) machine learning models to shorten the time it takes to design customer experiences. The no-code components in the platform are called modules, and the newest module (the launch module) gives finer grain control of starting or linking workflows from, or in, the Ushur platform to each other.
In previous deployments, if an Ushur workflow matched the conditions in the logic routing, it would move onto the next step in the customer experience journey. In reality, there are some customer experiences with an element of time inherent to them that IT or customer experience/customer service (CX/CS) teams may want to tightly manage.
For example, if a healthcare company sends a patient, or member, an invitation to fill out a survey on their current health status and in it they indicate they don’t feel confident about their current state of health with their current provider (PCP), their healthcare plan may want them to find a new provider. If after some amount of time, they don’t elect a new PCP, the next step in their customer experience could be to send them to a care advocate team, or it could be to send them educational content on their benefits. There are a litany of options, and time is a key consideration.
Another improvement the Ushur launch module affords citizen developers is the ability to discreetly decide what information to pass from one workflow to second when one launches another. When a workflow has collected data in flight, an optimized customer experience shouldn’t ask the customer to repeatedly input data. If the workflow hasn’t passed that information back to a core system, via one of the many integrations configured via the Ushur no-code configurator, a CXA workflow can pass data to the next one and keep the customer experience seamless to the end user.
The release introducing Ushur Studio has changed the way that the Ushur user-base works with the leading CXA platform. It’s easier than ever to build customer experiences, link them, and control their execution. To see a demo of the new and improved experience in Ushur Studio, sign up for a free trial or schedule time for a demo.
Enterprise Content Management, Production Capture, and Document Transformation have been around for 35+ years. Their origins center around scanning, manually indexing and publishing documents to content management systems for archival and shared retention.
Over time, the industry has focused on reducing human touch points, associated labor cost, and increasing visibility into the content contained within documents. Production optical character recognition (OCR) became mainstream, as did forms recognition (classification), field level data extraction, text analytics, and natural language processing (NLP). The capture market has leveraged various forms of AI for 25+ years, long before AI was popular.
The emergence of Digital Transformation has caused the capture market to shift from an autonomous, batch oriented, back-office focus to an integrated, transactional, in-process service focus. This shift was the catalyst for the emergence of what we now know as the $5B+ Intelligent Document Processing (IDP) market.
Up until recently, the service to license ratio associated with deploying IDP solutions has been high… The cost associated with setting up, training, fine-tuning, and scripting document transformation-related support tasks has been daunting. Fast forward to today and recent advancements in AI are revolutionizing the IDP industry. The service dependencies associated with IDP deployments are being shattered by advancements in multi-modal transformation, key-value-pairing, machine learning, and generative AI.
IDP’s back-office, document-centric focus has influenced the legacy architectures for many of the dominant IDP providers. As a result, the shift from back-office automation to front-office, customer experience automation use cases requires a significant change in approach. New entrants to the market (post the back-office to front-office shift) do not suffer from these legacy IDP architecture constraints.
Enter Customer Experience Automation (CXA): The next generation of AI driven platforms solely focused on delivering exceptional customer experiences by automating customer engagements in all shapes and forms and through any and every channel of engagement. An interesting dynamic occurs as the shift from back-office process automation to front-office experience automation unfolds. The historic IDP focus on documents must expand to include a much broader variety of content. It’s not just about capturing business documents. It’s about capturing and understanding all engagement related content such as email, texts, spreadsheets, videos, and audio. It’s about having an interactive dialog with the customer and asking them questions to gather the information needed to complete the engagement.
IDP vendors have been so document focused (and to a large extent back-office centric) the transition to front-office, content centric, experience automation is likely going to require material retooling and a significant investment in time and money.
The next generation of Customer Experience Automation (CXA) vendors have built their platforms with this knowledge and foresight in mind. Leveraging AI to generate optimal and satisfying customer experiences along with the ability to capture and process any sort of experience related content.
Here’s just a few examples of how Intelligent Experience Automation differs from Intelligent Document Processing:
The emerging customer experience automation market segment requires a broader definition of IDP to satisfy its automation objectives. As a result, Intelligent Experience Automation is poised to surpass IDP and become the new bar for processing customer experience related content, enabling CXA platforms to automate customer engagements. Designing your customer experience automation strategy is essential to success in digital transformation. Making sure your engagement automation strategy is capable of supporting your CXA initiatives will be key to any future success.
Customer Experience (CX) is the collective impact of a customer's interactions with a brand. It's pivotal for businesses in the digital era, as 86% of buyers pay more for superior CX, (according to PwC), and CX leaders generate 5.7x more revenue. Customer Experience Transformation, which involves revamping the customer journey using data, technology, and feedback, is key to exceeding expectations and gaining an edge. Here I provide critical insights into understanding and implementing effective CX transformation strategies, overcoming challenges, and staying abreast of future trends.
Understanding customer expectations is the cornerstone of Customer Experience (CX) Transformation. These expectations, influenced by various factors like past experiences and market trends, serve as a benchmark for evaluating brand interactions. To grasp these dynamic standards, businesses should leverage feedback channels and analytical tools, spotting gaps for better customer experiences.
However, mere understanding isn't enough. For effective CX Transformation, the best companies utilize a systematic approach, encompassing a customer-centric culture across the entire organization, technology integration, and success measurement.
This includes mapping customer journeys, creating personas, identifying pain points, and establishing a CX framework. Identifying pain points is crucial in eliminating the obstacles in a customer’s journey, facilitating easier and more pleasant interactions. The best customer-centric strategies are empathetic, and putting yourself in your customers’ shoes is the best way to foster this.
Leveraging cutting-edge technologies like artificial intelligence (AI), machine learning (ML), chatbots, and data analytics can revolutionize customer experiences. AI-driven insights enable personalized interactions, while chatbots provide round-the-clock support. Data analytics refines strategies, enhancing customer engagement and loyalty. These tools collectively empower businesses to transform operations and drive sustainable growth. Many organizations are undergoing digital transformation initiatives, the process of using digital tools and methods to improve or innovate their products, services, processes, and customer interactions.
CX transcends mere departmental boundaries; it embodies an organizational ethos. This cultural shift necessitates resolute leadership, fervent employee involvement, and seamless inter-departmental collaboration—sometimes necessitating altering or creating business processes. It demands a collective commitment to placing the customer at the core of every decision and action, fostering an environment where exceptional experiences are not just a goal, but a way of doing business for the entire organization.
Personalization, rooted in understanding individual customer behaviors and preferences, lets businesses offer tailored interactions, fostering a sense of appreciation and loyalty in customers. Customization, on the other hand, empowers customers to modify products or services as per their needs, adding unique value and elevating satisfaction levels. The burgeoning technology of Generative AI has the potential to truly revolutionize this personalization, not just in details, but the information and experiences delivered as a whole.
Omni-channel engagement is a vital element of customer experience transformation. It refers to offering a seamless, unified experience across all channels — be it online platforms, physical stores, or customer service. By integrating multiple touchpoints, businesses can ensure consistency and continuity in customer interactions. By leveraging technologies that are built from the ground up with an omni-channel approach, enabling customers to pause an interaction in one channel, then pick back up when they are able in another channel, pushes the quality of customer experiences to a higher level, especially across digital channels.
Data-driven insights and analytics are integral to customer experience initiatives. They provide in-depth understanding of customer behaviors, preferences, and trends in real-time. This valuable data can inform business strategies, drive personalization, enhance customer interactions, and predict future behaviors, ultimately leading to an elevated customer experience and increased business growth.
Customer experience transformation is an ongoing process requiring continuous enhancement. To effectively execute this, businesses should adhere to certain best practices:
Customer experience (CX) is the aggregate sum of all the interactions between a person and a brand. Good customer experience implies that a brand has comfortably met or exceeded the needs of the people that engage with it, and bad customer experiences (the unfortunately more common outcome) leave a person grasping for direction and outcomes.
Good customer experience (CX) is pivotal for business in the digital era, as 86% of buyers pay more for superior customer experiences (according to PwC), and CX leaders generate 5.7x more revenue. As a result, many organizations have to transform their customer experience programs and offerings to better serve their customers. Customer Experience Transformation is the process of revamping the interactions in that customer journey using data, technology, and individual feedback. Here I provide critical insights into customer expectations and how to implement effective CX transformation projects while planning for future trends.
Understanding customer expectations is the cornerstone of successful Customer Experience (CX) Transformations. These expectations, influenced by various factors like past experiences and market trends, serve as the barometer against which brand interactions are evaluated. To understand and respond to the expectations that define “good” customer experiences, businesses must leverage feedback channels and analytical tools, spotting gaps for better customer experiences.
It’s not enough just to understand. For CX Transformation projects to be effective, the best companies utilize a systematic approach, build a customer-centric culture across the entire organization, integrate the right technology, and find the right success measures.
Customer experience transformation efforts depend on mapping customer journeys, creating personas, identifying pain points, and establishing a CX framework. Identifying pain points for a set of well-researched personas is crucial for any business that wants to eliminate the obstacles in a customer’s journey and facilitate easier and more satisfactory interactions. The best customer-centric strategies are not only proactive, they’re empathetic.
Customer experiences for digital-first businesses are defined by the quality of their technological deployments like artificial intelligence (AI), process automation, and comprehensive data analytics. This combination can help businesses build better chatbots, personalize interactions, and offer round-the-clock support. Data analytics can also be useful when refining strategies, enhancing customer engagement and loyalty. Automation ties all the efforts together and accelerates customer-centric processes.
When these technologies are deployed strategically, they collectively empower businesses to transform operations and drive sustainable growth. Many organizations are undergoing digital transformation initiatives to improve or innovate their products, services, processes, and customer interactions with this consortium of digital tools and methods.
CX transcends mere departmental boundaries; it embodies an organizational ethos that businesses have to cultivate. The approach of customer-first culture necessitates resolute leadership, fervent employee involvement, and seamless inter-departmental collaboration. It demands a collective commitment to placing the customer at the core of every decision and action, fostering an environment where exceptional experiences are not just a goal, but a way of doing business for the entire organization.
Personalization, rooted in understanding individual customer behaviors and preferences, lets businesses offer tailored interactions, fostering a sense of appreciation and loyalty in customers. Customization, on the other hand, empowers customers to modify products or services as per their needs, adding unique value and elevating satisfaction levels. The burgeoning technology of Generative AI has the potential to truly revolutionize this personalization, not just in details, but the information and experiences delivered as a whole.
Omni-channel engagement is now a table-stakes standard of modern customer experience, and transformation projects often center around filling in gaps in existing engagement channels. It refers to offering a seamless, unified experience across all channels — be it online platforms or customer service. By integrating multiple touchpoints, businesses can ensure consistency and continuity in customer interactions. By leveraging technologies that are built from the ground up with an omni-channel approach–enabling customers to pause an interaction in one channel and then pick back up when they are able in another channel–businesses can drive the quality of customer experiences to the highest level, especially when switching contexts across channels.
Data-driven insights and analytics are integral to customer experience initiatives. They provide in-depth understanding of current customer behaviors, preferences, and trends while also affording a retrospective view on CX failures and a view towards desired state in the future. This valuable data can inform business strategies, drive personalization, enhance customer interactions, and predict future behaviors, ultimately leading to an elevated customer experience and increased business growth.
Despite conceptions to the contrary, customer experience transformation is an ongoing process and it requires continuous enhancement. To effectively implement customer experience transformation culture and practices, businesses should adhere to certain best practices:
Customer experience transformation projects that seem daunting often inspire companies to work with vendors that have decades of experience executing transformations. While consulting engagements can address some of the challenges with customer experience transformation, other components are cultural and must be owned internally.
There are implicit costs, complexities, and obstacles in the best practices for implementing CX transformation projects that can hinder successful outcomes. The biggest obstacles are often internal: organizational silos limit impact, data is too fragmented to support CX improvements, and companies may not be prepared with the right mindset. Transformation projects can also include limited investment funding, inadequate talent, and quickly changing technology landscapes.
To overcome these challenges, companies should start with a leader-first approach to establishing a customer-centric culture, and an obvious set of clear goals. Leadership should invest in the right talent, technology, and establish an environment that fosters collaboration and experimentation. After all, if every company knew the perfect recipe for success in CX, and didn’t need to experiment, they probably would have already tried those key projects.
The final and most critical element of customer experience transformation is to centralize around a strategy that includes proactive customer experience. By most every measure of customer experiences, responsiveness and ease-of-use will make or break whether they are poor, adequate, or exceptional. The easiest way to seem attentive and easy to work with is answering questions before they’re asked.
Measuring success in CX is essential to demonstrate ROI and fine-tune strategies over time. Some common metrics for CX measurement include customer satisfaction, net promoter scores, customer effort scores, and customer lifetime value. However, companies should also consider business-specific metrics that align with their CX goals.
Moreover, customer feedback is valuable for identifying areas for improvement and making data-driven decisions. Customer satisfaction surveys, social media feedback, and customer reviews are great sources of feedback. Companies can also use analysis tools like sentiment analysis and text analytics to extract insights from unstructured data.
Finally, it is worth exploring the impact of customer experience initiatives across different industries. Industries like retail, hospitality, F&B have been at the forefront of customer experience innovation as they have a high degree of customer interaction and have historically differentiated themselves with brand elements like their CX quality. However, other sectors like healthcare, financial services, and even government are now embarking on CX transformation journeys to improve customer satisfaction and loyalty because they understand that it can similarly grow their top and bottom line revenue.
In the insurance industry, customer experience transformation is crucial because customer experience is often a carrier's best differentiator. The industry is shifting its focus from product-centric to customer-centric strategies. By leveraging technology, insurers can personalize policies, simplify claim processes, and offer 24/7 customer service, leading to improved satisfaction, brand loyalty, retention, and ultimately, business growth.
In the healthcare industry, customer experience is pivotal for commercial success because it directly influences patient outcomes and satisfaction. A patient-centric approach, facilitated by technology, ensures personalized care, clear communication, and prompt service. Moreover, a positive customer experience in healthcare can enhance patient adherence to treatment plans and foster trust in healthcare providers. Therefore, efficient and effective customer experiences are essential for healthcare providers and payerspayors aiming to deliver high-quality, efficient, and patient-centered care and service.
In the financial sector, customer experience transformation is central to building a competitive advantage. Great customer experience in the consumer-focused financial sector leverages technology to streamline services, ensure seamless transactions, and offer personalized financial advice. Great customer experience in the commercial-facing segment of financial services deploys tech to be responsive, make critical decisions quickly, and generally be easiest to do business with.
A customer-centric mindset in finance can boost client loyalty, enhance satisfaction and attract new customers. As such, prioritizing customer experience is pivotal for financial institutions striving to offer superior services, foster trust, and deliver value in today's highly competitive and digital financial landscape.
Customer experiences today are a critical value driver for businesses thriving in a highly competitive enterprise landscape. The easiest, fastest, and most effective customer experience breeds today's digital winners, and it's difficult to build without substantial levels of automation. Ushur, an industry leader in customer engagement with its Customer Experience Automation™ (CXA) platform, blends conversational AI, document AI, and process automation to add customer experiences on top of existing platforms. Ushur elevates customer interactions and is uniquely engineered to help businesses in regulated industries deploy, improve, and update mission-critical customer experiences.
Learn more at ushur.com.
Low Code No Code (LCNC) software platforms are designed to help individuals with limited or no prior coding experience create applications and experiences through intuitive drag-and-drop interfaces (often referred to as Graphical User Interfaces, or GUI’s). The two require differentiation. Low Code platforms require minimal coding but often support more complex or customizable features by being code-friendly. No Code software is similar, but is a type of software development tooling that allows users to build applications without writing any code. No Code platforms exclusively use visual tools, drag-and-drop interfaces, templates, and pre-built components.
Businesses often struggle to create and implement software applications that align with their specific needs and objectives, but staying competitive in today’s landscape necessitates a digital-first approach.
One of the main reasons for this struggle is the scarcity of IT resources and expertise. As per an Appian study, “82% of companies can’t attract and retain the software engineers” needed to build solutions in-house. Another study reveals that an estimated 377,500 IT jobs will remain unfilled annually from 2022 to 2032. In an era of rapid digital transformation, Low Code No Code software stands at the forefront as an innovative solution that empowers users to create applications without the need for extensive coding knowledge. These revolutionary platforms pave the way for non-technical individuals to devise and deploy applications tailored to various business needs, from workflow automation and customer experience enhancement to data-driven insight generation. In this blog post, I delve into the fundamental aspects of No Code Low Code software, exploring their key components, potential business impacts, implementation strategies and evaluation metrics to measure their success.
Low Code No Code software uses a multifaceted approach for software development. There are three crucial components of Low Code No Code software I’ll delve into. Understanding these elements is essential before selecting and implementing LCNC software. The key components are:
Each of these components plays a pivotal role in leveraging Low Code No Code software effectively for your business.
As alluded to above, Low Code No Code (LCNC) represents two distinct types of software development, both designed to facilitate application building with minimal or even zero coding. Low Code software necessitates some degree of coding, especially when it comes to intricate or tailored features and is particularly beneficial for developers or advanced users who desire greater control and flexibility over their applications.
On the other hand, No Code software completely eliminates the need for coding and serves as an ideal choice for non-technical users seeking a simpler, more expedient route to application creation. So how do they align and how do they compare?
Not all Low Code No Code software can integrate smoothly with legacy systems and data sources. Some LCNC software may require users to replace or modify their existing systems, which can be costly, time-consuming, or even leave a project stuck on the starting block. That’s why it’s important to choose LCNC software that can sit on top of existing tech infrastructure and interact with it through simple configuration or third-party connectors.
For a new software application to serve as more than just a thought experiment, it almost certainly must leverage the data and functionality of legacy systems while also adding new features and capabilities. Integration serves that requirement and is a crucial aspect of any business that requires careful planning and evaluation to ensure that the new tools and platforms complement rather than conflict with existing operations.
The Low Code No Code software space sits on a spectrum that encompasses various tools and platforms, each with its unique features, functions, and pricing structures. It's crucial to identify the tools that dovetail with your specific objectives and requirements.
Here are some criteria and guidelines for evaluating and comparing various Low Code No Code tools:
Utilizing Low Code No Code software allows for quicker and simpler application creation, resource and cost optimization, and an improvement in customer satisfaction and loyalty. In this part, I will explore three primary impacts of LCNC software on your business:
Low Code No Code software drastically cuts down on the time and effort needed to develop and deploy applications. As per a study by Forrester, the use of LCNC software can expedite the delivery of applications by a factor of 10 or more, while using 70% fewer resources.
The reason for this speed is that LCNC software either minimizes or omits the complex coding process altogether, relying instead on visual tools, drag-and-drop elements, templates, and pre-configured features. Code-based projects with IT often leaves development teams starting from scratch. Code-free software also facilitates quicker testing, debugging, and updating of applications through automation and cloud-based deployment.
One significant benefit of Low Code No Code software is its unique ability to optimize resource utilization and distribution. By incorporating LCNC software into operations, any business can minimize reliance on overburdened internal IT departments or costly external IT services, which often consume considerable time and carry inherent risks.
LCNC software can free up your IT resources for strategic initiatives, such as innovation, security, and governance. It empowers even those lacking technical skills to develop applications autonomously, thereby decreasing dependence on IT personnel. This autonomy can enhance IT productivity and efficiency, circumventing IT bottlenecks and backlogs.
Another advantage of Low Code No Code software – is its potential to heighten customer satisfaction and build loyalty. The implementation of automation allows businesses to create applications that are highly responsive, personalized, and still engaging for its customers. LCNC helps businesses implement those automated processes even more quickly and effectively than ever before so they can give customers even more opportunities for automated self-service.
Choosing the right Low Code No Code tool for your enterprise hinges on its alignment with your business objectives, needs and challenges. The effectiveness of a LCNC tool can vary greatly across industries and use cases, making it vital to find a solution that is tailored to your specific business domain, requirements, and expectations.
To assess the business compatibility of a LCNC tool, consider the following key questions:
The duration it takes to deploy and begin utilizing a Low Code No Code tool can hinge on several factors including the project's complexity and scope, the availability and proficiency of resources, integration and customization requirements and testing and deployment procedures. A LCNC tool with a swift implementation process can prove beneficial by saving time and costs, as well as delivering results more rapidly.
Consider these guiding questions when estimating the implementation timeline of a LCNC tool:
The last crucial aspect to consider when selecting a Low Code No Code tool for your enterprise is its financial impact. Evaluating the cost of a LCNC platform involves juxtaposing the initial and recurring expenses against the anticipated benefits and savings. A tool that offers lower costs can aid in maximizing your ROI and minimizing your total cost of ownership (TCO).
In assessing the cost-effectiveness of a LCNC tool, consider these critical questions
The Ushur Customer Experience Automation (CXA)™ platform is the leading Low Code No Code (LCNC) software that enables businesses to create automated and AI-powered customer experiences. With its intuitive interface and robust capabilities, users can easily develop intelligent chatbots, personalized notifications, proactive communication channels, and other digital solutions without any coding expertise. The platform also provides analytics and insights to improve customer engagement and satisfaction across various touchpoints. Ushur stands out among other LCNC platforms with its omnichannel, AI-centric, and seamless integration features.
One should leverage the past to help understand the present, and to help better predict and create that future.
Let’s rewind and go back to the origin of what we now call Digital Transformation (DX), when various legal proceedings resulted in the legitimization of digital signatures. Digital signatures became a viable option for transacting business and it was the pebble in the pond that kicked off the move for organizations to revisit how they conducted business. Up until that point, organizations required contracts and agreements to be printed out, physically signed and either faxed or scanned and emailed. When this limitation was lifted, organizations realized they could go completely digital.
During this same time-period, the evolution of mobility began to have a major impact on consumer expectations. Consumers quickly became used to things being quick, easy and on demand. They became accustomed to having control over when, how and where they engaged and they weren’t shy about complaining or switching away from businesses, especially in banking or insurance, that were difficult to work with.
As a result, organizations quickly realized customer experience was going to become the primary battle ground from a competitive standpoint. Customer retention and new customer acquisition in the future would be predicated on delighting and creating the best possible experience for those customers.
The next pebble in the pond was the emergence of low-code and no-code platforms built in response to the overall tech democratization macro trend. The days of long, lengthy, service-heavy deployments were running out. Buyers wanted accelerated time-to-value, lower total cost of ownership and lower overall risk. Nowhere were these dynamics more obvious than with the emergence of Robotic Process Automation (RPA). Automating simple back-office tasks was now something the average knowledge worker could do themselves with minimal dependency on IT (or at least that’s what the RPA providers promised; topic for another time). Businesses were empowered to automate simple, repetitive tasks that were often tedious and time consuming.
More recently, we’ve watched the emergence of Conversational AI platforms. This began initially with basic (limited) Q&A oriented chatbots, which usually had the effect of agitating and frustrating customers rather than helping them, or cheesy virtual assistants that provided minimal value. The emergence of generative AI has the potential to breathe new life into conversational AI as long as platform vendors can figure out how to monitor and control generative AI responses.
Over the past five decades, businesses, especially enterprises in highly regulated industries, have been on a quest to develop ever larger Systems of Record (SoR) that have now become essential to their day to day operations. These SoRs are robust, scalable and seemingly bullet proof enterprise-class platforms that act as the backbone of very large technology infrastructures delivering the ability to conduct commerce every day without interruption.
Combine these complex and sluggish enterprise infrastructures built around behemoth SoRs with the emergence of a global populace that is increasingly mobile and tech savvy and you end up with a gaping chasm in human expectation versus human experience.
In this series of articles, we will address this very question: how do enterprises enable their SoRs and tech infrastructures to provide exceptional, omni-channel, low latency, on-demand, interactive experiences?
The obvious answer is selecting a System of Engagement (SoE) that is designed and architected from the ground up to provide optimal customer experiences while front-ending any SoR. System of engagement is a broad category, and we’re now seeing the emergence of a new set of SoE developers focused on Customer Experience Automation (CXA) – the interdisciplinary intersection of artificial intelligence, process automation, and conversational interfaces blended to deliver optimized human experiences. These low-code and no-code platforms combine conversational AI, document transformation, workflow automation, system-to-system interoperability and recent advancements in Artificial Intelligence (Large Language Models and Generative AI), all enabling organizations to quickly and easily build and deliver exceptional customer experiences with the highest possible efficiency. It is important to note that these vendors have deliberately focused on customer experiences as the outcome while engagement is a necessary component.
If delighting your customers is a priority, you need to figure out your Customer Experience Automation strategy. Stay tuned as we dive deeper into the art of the possible in the world of AI-powered experience automation.
As the role of digital communications grows, true omnichannel capabilities become more crucial to provide a satisfying, unified customer experience. The goal of omnichannel sounds simple — maintain customer engagement across channels to meet customer expectations — but it's among the most complex challenges businesses face today. But does an omnichannel approach truly contribute to business drivers loyalty, improved customer experience and reduced operational costs? What is the difference between omnichannel and multichannel? This blog pulls apart and outlines some of the nuances of the terms to clarify the general principles of omnichannel in a practical setting, and is intended to educate readers on what they should look for when qualifying solutions and strategies.
“Omnichannel” is a term that’s been touted by the software industry, but has been defined in the past in fields like retail or banking as the handoff between people who shopped or banked online, and then transitioned back to in person or live call interactions; or vice versa.
The information inherent to the handoff as people transitioned from live interaction to digital was key to identifying consumer behavior patterns, and so the advent of omnichannel marketing became a gold mine for customer data. It was because of “omnichannel” marketing’s inherent utility that it became an experience laden with value that other companies strove to achieve. The principle of easy handoff between channels of engagement is a key component that persists into the digital communications space as digital channels continue to expand.
What makes digital engagement solutions truly part of the omnichannel landscape is a design principle that guarantees a policy of open-door acceptance of customer traffic. Similar to the easy transition between live and digital experiences was the advent of “omnichannel” as a recognized term, omnichannel in digital communications requires that they support easy transitions from one digital channel to the next, across all relevant lines of communication - as well as seamless transitions to live and even offline channels.That means that whenever a customer wants to contact a company, the company can receive that contact whether it’s via text message, phone call, email, or any other digital channel. But where are the limitations?
Many digital engagement companies have built solutions that let people text their contact center, call into a support line, or even chat with a live agent, but the experiences are vastly different between them - and there aren’t smooth data transitions when customers hop between these channels. That’s a part of the reason why customers still leave channels intended to support call avoidance or deflection and call into contact centers, leaving companies still struggling to manage overstrained and under-staffed support lines.
Simply offering multiple channels with differing experiences is not the same thing as building omnichannel solutions. While an omnichannel solution is multi-channel, a multi-channel offering is not necessarily omnichannel. This is an important distinction that will leave digital engagement customers disappointed with an omnichannel implementation if not met. The experience between calls, chats, text, and emails needs to be of comparable ease and value - and data collected in one channel needs to be transitioned to the next one to enable the customer to not have to repeat information they just shared. With true omnichannel communications, enabling customers to start their conversation in one channel like email, then move to another channel for digital self service is a smoother, easier and frictionless process.
Besides the open-door policy of receiving any communication from any channel, omnichannel offerings are also those where businesses “maintain state” in communication with their customers as they transition between channels. The idea of maintaining state is not as complex as it sounds. It implies that when customers are interacting digitally, then call into a contact center, the customer support center they reach should both be able to pick up where they left off, and have access to any information they shared before they switched channels.
Omnichannel capabilities in Customer Experience Automation™ (CXA) solutions are a key differentiator for a platform like Ushur where users can build a workflow to handle a specific Micro-Engagement™ once and then deploy it to other digital channels with a single click. For example, when users text their support line with “#applicationstatus”, they get the same walk-through for checking their application status as they would if they were to go through Ushur’s IVR-deflection to digital self service experience when they call into a support center (e.g. calling a support center and choosing option x to receive application status digitally).
Ushur is also a seamless channel and data bridge between on and even off-line channels into digital channels, as well as a seamless bridge back to live channels as shown in the illustration below.
Ushur makes the deployment easy, but also the process for building flows and setting up data integrations. The Ushur no-code, drag-and-drop builder has pre-built modules to support interactive more effective digital communications with customers that alert, educate, influence, survey, push and pull information and even files and images in a convenient way. Ushur integrations and secure FTP file transfer capabilities make it easier to push and pull information to front and back-end systems. Pre-trained machine learning models also make interacting with AI easier
In addition to helping insurers, healthcare companies and financial service companies to route, classify, and answer any question their customers have - even those depending on data in core systems - Ushur also tracks the status of each Micro-Engagement across any channel. Customers can start their conversation on one channel and begin a new conversation on another, and Ushur connects the dots for the company, so customer service agents don’t have to. Once all the essential information is in one place, it’s much easier to resolve the query or even provide the relevant information proactively.
The Ushur Silent Listener allows companies to capture and respond to SMS text responses based on business rules or even via conversational AI rather than letting those responses result in a dead end of non responsiveness.
An example of an Ushur-optimized omnichannel experience is when an HR Benefits Administrator receives an Ushur automated email related to group benefits enrollment, and clicks through to an Ushur secure digital channel to answer a few questions in a micro engagement. They get interrupted by a meeting while they are on screen 2 of 4 of the engagement. When they come back to finish the engagement, they are able to pick up where they left off and none of the information they provided previously is lost.
Finally, Ushur is dedicated to handling the responsibility to build capabilities for new digital channels as they arise and companies need them. Ushur’s modular and flexible architecture future-proofs a company’s investment in customer experience by relieving it from having to build a pathway for every channel as it becomes more popular. From Email and Web, WhatsApp and Facebook Messenger, to SMS, Ushur’s CXA platform delivers the scalability companies need to engage on those channels.
With a more specific definition of what goes into omnichannel solutions, we hope readers will feel empowered to dissect what each capability means and strive to get to true omnichannel status. We also hope that everyone now has the language to ask for the functionality they deserve to support their customer experience improvement goals, and future-proof their communication investments.
If you want to see what a true omnichannel digital engagement platform looks like, and how to build a roadmap for an immersive, modern customer experience journey, please reach out to ushur.com/request-demo.
Ever notice how some engagement processes just seem to be littered with friction? What if you could whip up a solution, as tailored as a custom-made suit, to tackle those issues head-on?
Picture those little friction points along your customer and partner engagement journeys. They're like mischievous troublemakers, snatching away efficiency and turning satisfaction into a game of hide-and-seek.
This can really put a damper on the experience for your dear constituents. And no matter how important the task is, getting them to act can sometimes feel like trying to move a mountain. And even when they do, they're taking the scenic route – not the most straightaway path you had in mind. Your customers and partners end up resorting to old-school ways. Although it’s not the smoothest ride, it’s simply what they know best.
If these exchanges contain sensitive files or Personally Identifiable Information (PII), the chance of unexpected detours increases, with data wandering into places it has no business being in.
Communication doesn’t need to jigsaw through these confusing twists and turns. You're doing your best to smooth out the snags with the tech tools you've got. But either those tools can't shape-shift into the solutions you need, or getting them to do so requires serious magic from internal and external technical teams. And let's face it, we'd all rather spend less time pulling rabbits out of hats.
Imagine waving goodbye to fitting square pegs in round holes. Instead, say hello to a composable application that serves as your engagement sidekick – amplifying all investments and pushing productivity throughout.
Your apps, portals, websites, phone lines, chat channels – all your interaction channels – stay right where they belong. But now, they're turbocharged gateways to a secure digital world, finely tuned and purpose-built by you to bring every task to completion.
Whether reaching out or fielding requests, you meet everyone on their turf. Then, with an empathetic nudge, usher them through a series of dynamic prompts, straight lined towards resolution. It's like offering VIP self-service to your customers and partners for every touchpoint, from short micro-engagements to multi-phase journeys.
Customer Experience Automation is like a gust of fresh tech air – it's the rapidly emerging category of solutions designed to light up real-time engagement. Imagine connecting those ever-changing digital front ends with super-smart automated backends, creating a seamless end-to-end approach that benefits all humans involved.
And here's the kicker: this shouldn’t be some complex code wizardry. It works best as a no-code zone, intended for your enterprise to swiftly adapt to shifting customer demands by avoiding long lead times and extra hands on deck.
All this jazz leads to one powerhouse goal: nailing operational excellence through the pure magic of transformative customer experiences.
Customer Experience Automation™ (CXA) is the application of an AI-powered platform that is purpose-built to automate, scale, and remove the friction from the interactions between a company and its customers; from the beginning of a conversation through its resolution. CXA brings together knowledge work automation and conversation automation, to not only intelligently interact with customers, but also interface with backend systems to complete all necessary tasks.
Often the application of automation technology is focused either on handling only front-end customer service inquiries or managing back-office processes. CXA, by comparison, is able to provide the digital consumer self-service options that customers want both wherever the customer reaches in seeking service, and whenever a company reaches out to share or request information.
For insurance carriers, Customer Experience Automation™ offers opportunities to simplify conversations and automate repetitive tasks. Tasks like data collection, and multi-step business processes are faster, easier, and more accurate using CXA. It injects agility and versatility into historically sluggish processes; everything from quotes and onboarding to benefits enrollment, billing, and claims.
It used to be that consumers would have to wait for an item they ordered with only the original estimate of how many days it would take to receive it. Likewise, they’d have to call their insurer and wait to speak with a representative to ask if their preferred care provider was covered in the insurer's network, or when their next payment was due. In contrast, if you as a consumer today have ever received proactive tracking updates letting you know the number of stops your purchase is from your home, or if you’ve ever texted your insurance company to find the nearest in-network auto repair shop and received the answer immediately, then you’ve experienced the kind of ease-of-experience that CXA and its effort-saving capabilities afford. True CXA for insurance is only a possibility thanks to the capabilities of Ushur’s platform, that includes InvisibleApp™.
Customer Experience Automation™ is essential to modernizing your service delivery and operations because it allows customers to skip the long wait to talk to a person when they would prefer self-service, bypassing unpopular IVR systems, and solve inquiries or complete tasks, through robust Conversational AI. It also affords customers choice and empowers them in their interactions with your brand. Customers who are concerned that their urgent questions will slip through the cracks without immediate attention, will often forgo their preferred digital messaging channel and introduce another point of friction. By offering an artificial intelligence (AI) powered digital channel of their choice as soon as they call in, a company provides service without forcing an employee conversation that the customer does not prefer. This both elevates the customer’s experience and offers the carrier operational efficiency gains.
Ushur’s Invisible App™ is a 1-to-1, secure communication channel for automating customer interactions. It delivers an app-like experience, without the friction of having to download an app, or the cost to the carrier to build, maintain, and support. It also offers customers an easy alternative to logging into a portal and streamlines complex conversations. Invisible App™ enables carriers to provide their customers with an intuitive and consistent brand experience, regardless of the service request involving their policy, bill, or claim. And because Invisible App™ is omnichannel, this consistent experience is offered regardless of where the customer interaction begins, including phone, text, email, web, or popular messaging apps.
In other words, Invisible App™ is what lets an insurer build an automation flow for the reporting of a new claim and meet the customer where they are to begin that conversation, when they begin with a phone call, text, email, website, or chatbot. Invisible App™ affords carriers both flexibility and scalability, eliminating the need to create and maintain channel-specific solutions. Invisible App™ future-proofs your investments in your communication and automation solutions.
If you experienced any part of processing a claim for a car accident, from a minor fender bender to a total wreck, you know that the task of gathering information, data, and documentation from multiple parties is difficult, time-consuming, and fraught with tension. Customers want to know where the process stands, what happens next, and when. And delays that result from missing data are frustrating while also easily remedied by automation.
With Customer Experience Automation™, any missing information can be requested or shared between the many participants in the claim journey. CXA facilitates data gathering and sharing between parties including the customer, service providers, the auto repair shop or a medical facility, agent, and the insurance carrier. Automated outreach via SMS or email eliminates the back and forth of phone calls and voicemail tag, and creates vital carrier capacity for their people to focus on the interactions and complex decisions where human involvement is key. Customers are offered easier ways to respond to information requests, from a quick SMS reply, to photo uploads of an ID or document instead of the time-consuming task of manual data entry after logging into portals.
Technological familiarity, proficiency, and preference is increasing steadily across all generations. The “digital only” world during the pandemic lockdown created a larger, wider audience that expects the ability of omnichannel self-service. Customer expectations for speed and transparency are already high and continue to increase.
What’s more, consumers expect real time information, and Customer Experience Automation™ allows carriers to ensure their sales distribution channels have the most current product information at their fingertips; including service and pricing information. It introduces speed into the quote and RFP intake process by automatically recognizing incomplete submissions and by reaching back to the sender instantly. Finally, CXA also allows customers to review their benefits, confirm their next premium payment, understand the status of a claim, inquire about updating their policy, and more.
Ushur’s Conversational AI and Machine Learning (ML) combine to provide automation solutions that interact with customers easily, quickly, and on their channel of choice. When a customer initiates a transaction or request for service, whether that is filing a claim or a request for information about their policy, Ushur’s Conversational AI is able to automatically detect relevant information from the inquiry and take the appropriate next step.
NPS is directly related to customer satisfaction, retention, and loyalty. And whether it’s measured within your company or outside of it, NPS reflects customer experience which “correlates with increased revenue growth, retention growth, and referral rates.”
With Customer Experience Automation™ introducing the ease of sending and receiving information, insurance carriers offer a modern solution brand experience that is inviting and at the same time, reassuring to both new and current customers. And that positive customer experience translates to customer confidence and loyalty that is then reflected in higher NPS.
Ushur’s Customer Experience Automation™ is a robust platform that focuses on the automation of one high-value customer interaction (what we call a Microengagement™) at a time. Ushur increases a carrier’s agility to realize their digital transformation vision, enabling business employees to create new automated 2-way conversations in hours, or less, without adding to the demands on their IT partners. Ushur CXA platform is a system of intelligence that complements the wide array of internally facing core and CRM systems carriers use today, along with their contact center solutions. Ushur makes it possible for carriers to offer their customers, agents, and brokers a consistent brand experience regardless of where across the full insurance lifecycle they are interacting, and what technologies support those functions. Purpose-built with best-in-class insurance pre-trained AI with ML, Computer Vision and Intelligent Document Automation (IDA), it doesn’t simply sit at the front end of customer reaching processes. It works within every part of a carrier’s customer experience, optimized for knowledge work and customer engagement automation with a zero-code approach.
Ushur brings frictionless customer experience to the enterprise. Talk to a specialist today.
A few weeks back I was speaking with a colleague who had just bought a new laptop. Unsurprisingly, a lot had changed about laptops since his last purchase. They’re slimmer and faster, the screens brighter, the battery life longer. They hold more data, can connect to your wireless headphones with ease, and built-in disc drives are long gone. Most everything about a laptop purchased today is better than one purchased in 2016.
But my colleague did have one complaint that felt fairly unexpected — the machine’s built-in spell check function was markedly worse. Not only was it missing mistakes, it was actively recommending spelling, punctuation and tenses that would have rendered the content grammatically incorrect.
Now, spell check may very well be the first automation software most people over the age of 30 ever interacted with. We were seeing the industry’s first consumer-focused foray into Artificial Intelligence, the simple recognition of repeating patterns based on historical data. It’s incredibly useful and difficult to improve upon, so it has remained part of our personal computers ever since and continues to today. But we’re used to a reality in which ubiquitous and long-lasting technologies get better as time goes on. The camera continues to get better and better with each iPhone generation. Gas mileage continues to improve in cars. Home security systems have become more accessible and user-friendly. Even most basic toaster ovens now have air fryers built-in. Consumers expect the technologies they use to improve over time. So why then would a technology as basic and foundational as spell check take steps backward, particularly at a time when technological innovation is advancing significantly across the board?
The answer is simple —Data, data, and more data.
On its surface, this assertion might not make a whole ton of sense. We live in the most data-rich era in human history. Shouldn’t more data mean more insight? Shouldn’t it mean smarter decisions, and better outcomes? Sure, in theory. But not if it’s the wrong data – or worse, bad data. Artificial Intelligence is only as good as the data on which it was trained.
Which brings us back to spellcheck. The earliest iterations were trained on a wide array of published texts that had passed through a stringent set of editorial standards simply to be published in the first place. Today, nearly every human being in the world has their own personal publishing platform — the Internet. Even publications with strict editorial standards have had to let them relax in the name of the 24-hour news cycle and consumer demand for information delivered quickly. In short, there is infinitely more written text available in the world today than there was 30 years ago and the editorial quality of that text is objectively lower. The result? Legacy software that comes standard with every personal computer in the world no longer performs like it once did.
When people think of data integrity, they’re typically envisioning one of two things. The first is privacy — everything it entails to ensure that customer information is safe and secure. No bad actors getting access or using that data for illegitimate purposes. This has become table stakes across the industry, and anyone who aspires to be a provider of modern software has built enough internal controls both in terms of the product and practices to ensure that data is governed in accordance with industry best practices.
The second is about the visualization of data, what are most often referred to as insights. This is where both the challenge and the opportunity lie. We need to know how and why a customer is consuming a product or service in order to deliver a continually improving experience. Again, these insights are only as good as the data being analyzed. Accuracy of that insight is paramount to building legitimacy and trust with the user.
As a runner, I’ve always found it helpful to have a Garmin watch with me that can track my vitals. I’ve invested in six or seven over the years, upgrading to new models as they’ve become available. I find the insights the watch provides about my health to be invaluable; long distance running is hard on the body, and monitoring one’s own health is critical. Recently, I bought the latest and greatest watch. But on my next run, I was both surprised and concerned to find that my heart rate was unusually high. This continued for several more runs, and I had long business travel on the horizon. It made me worry enough that I drove myself to urgent care to get checked out before I got on a plane and flew thousands of miles away from home. The doctor gave me a clean bill of health, and I later came to find that my new watch simply had a software glitch. Inaccurate insights erode trust and confidence in the technology. At least for me, it was enough to make me consider abandoning a brand I’d been loyal to for nearly 20 years.
In the early days of data analysis the incoming data was very structured. It was a simple matter of math and computation that would spit out a chart or insight. As the world has evolved to produce more unstructured data, the statistical modeling around that data can sometimes lead to anomalies in the interpretation of that data. For example, I was driving with my co-founder Henry in his Tesla recently and he expressed concern that a few of the new software updates seemed to have regressed the Full Self-Driving (FSD) experience in his car. It was enough to make him worry about using the feature entirely. A lot of engineering goes into improving the algorithms and models, but if the accuracy of the data on which they’re built gets compromised then instead of being helpful, automation simply creates new pain points.
There is no such thing as too much data in today’s world – if we can harness it in a meaningful way. Otherwise, it can have an adverse impact on our ability to analyze and act on that data in the right way. Every connected device in use spits out tons of data. There are millions of sensors, industrial and IoT devices generating more data than we can possibly care about. If we can’t develop the intelligence to glean the right insights and act on them effectively, then the data itself is meaningless.
The world of statistical models and machine learning is rife with experimentation and innovation that is consistently improving algorithms and outcomes. I’m optimistic that over time, they will continue to get better. The challenge is ingesting new information. Oftentimes it’s those incoming data sets that are outside the boundaries of what the model is trained for that lead to erroneous behavior. It feels regressive, and customers become upset. Trust erodes.
At Ushur, we get a firsthand look at the importance of maintaining trust every day. Customers in the finance, healthcare and insurance spaces are incredibly risk averse. For the vast majority of professionals working in these industries, it is better not to take any action than to take an action that might lead to business risk. It’s one of the most commonly referenced barriers against the adoption of new technology within these highly regulated industries. Attention to detail, taking great care and providing accurate results are a must. If a hospital is applying AI to read a CT scan that will determine someone’s health diagnosis, that result simply cannot be anything less than 100 percent accurate.
But fascinating and life-altering advancements have been made in the way machines are being trained to detect diseases or conditions that the human eye cannot. As long as the training models are incorporated correctly, the outcomes we can drive for real people will be both predictable and transformative. But 80% accuracy won’t do the trick. 95% accuracy won’t even cut it. Nobody wants to be the victim of that 5%.
The way we leverage data, how we analyze and present it to others, and the actions we take on those insights represent a massive opportunity for the world. But it also poses a huge risk if done incorrectly, sloppily or thoughtlessly. These are powerful tools and applications being put into the hands of the general public. If the data on which our assumptions are based lacks integrity, the results will be ineffectual at best and catastrophic at worst. Which is why the next frontier of innovation in AI revolves around eliminating bias, hallucinations and defining guard rails for LLMs, along with considerations for data privacy and data security. It’s all about data!
If you think that deep learning (DL) and machine learning (ML) have a lot in common, you’re right. But if you hear someone using deep learning as a synonym for machine learning, it’s not quite as accurate.
Machine learning uses past data and statistical algorithms to create systems that recognize patterns and predict future observations. Deep learning is a machine learning technique that draws its inspiration from the human brain and how it thinks and extracts information.
In other words, deep learning is a subset of machine learning, but not vice versa. Both are types of artificial intelligence.
Machine learning solutions can automatically adapt over time based on the new and natural patterns within the data, rather than having to programmatically account for every possibility with logic and routing. There are 4 types of machine learning:
The most common form, supervised learning, refers to algorithms that learn from data labeled by humans. The most common supervised machine learning techniques include linear regression, decision trees or random forests.
For ML algorithms to make decisions, predict something or recognize a pattern, data scientists have to train them with properly collected, cleaned, engineered and labeled data. Based on a data team’s data pipeline, model accessibility and the model’s additional exposure to more data, the effectiveness of the algorithm can continue to improve.
Ultimately, a trained machine learning model is composed of the parameters that inform how much each variable affects the predicted value (for example, when predicting home prices, how much does the number of baths sway a home price).
In other words, the higher-quality the data the ML algorithm gets, and the more useful the features engineered within the dataset, the better, and more accurate the output will be.
Machine learning is the parent category for artificial intelligence methodologies that include deep learning, so the principles by which machine learning works give some understanding as to how all forms of artificial intelligence products work.
Machine learning relies on examples of past experiences in the form of data to offer predictions with varying levels of confidence in the future. If an experience in the future very closely resembles an example seen by a ML model in the past, it can likely predict an outcome (also referred to as a target) with fair confidence. That space in ML with an object to predict is called supervised learning.
Machine learning and deep learning derives results on cleaned data sets. In some more detail, now, supervised machine learning works by finding a function (just a combination of inputs and tuning parameters) to fit a dataset where the experiences (data) have a label.
The right parameters for the function are the ones that minimize the function’s error rate against that particular set of data; you can think of the error as the distance between a prediction and the actual value. The process of creating that function with minimized errors is also called training.
As an example, if a data scientist is trying to train a model to predict home prices, they may use a linear regression model whose parameters like the number of baths, the square footage of the house, and the proximity to a mass transit center affect the label: the home price.
By comparison, when machine learning isn't deployed to help predict an outcome, it’s usually used for identification or segmentation. Those approaches that lack a variable, or a target to predict, are a part of the sphere of unsupervised learning. That brand of machine learning works by grouping similar data points together during training and then determining which group a new and previously unseen sample should fall into.
As an example of unsupervised ML, if a data scientist is trying to train a model for anomaly detection, they may use a clustering model (like K-Means) to see which data points make sense to be clumped together. If there are data points that don’t fit into a cluster, those are the anomalies.
Deep learning works to recognize patterns and relationships in data and is often used for processing data formats like documents, photos, videos or audio.
“ Deep” is to indicate that learning happens in several layers. Say, the first layer learns to identify an “orange” and its basic features in an image. By going through the next layers, the model may add information about more features that make an orange an orange (texture, color, shape, etc). Each new layer has more information about the features of the orange based on the previous layer's knowledge so it can better detect the fruit and differentiate it, for example, from an orange ball.
How accurate it is at categorizing each item it sees (the orange), and therefore how quickly it’s learning is determined by comparing the predicted value to the correct value. Comparing the two values and then feeding the results back into the training process gives the neural network the chance to experiment with another set of weights and biases.
For another scenario, say you’d like to build an ML algorithm that can distinguish between an image of a cookie and an image of a dog. To do this, you’d need a lot of training data, in this case, images that present either a dog or a cookie and are labeled appropriately.
By training the ML algorithm on what features have dogs and what features belong to cookies, the algorithm learns to recognize whether it’s a dog or a cookie and uses this information to predict the correct label for the new image.
The truth is, your use cases should dictate whether you’ll use deep learning or simpler machine learning models. More traditional machine learning models are comparatively cheap and easy to train and deploy, while deep learning models help automate use cases for data types like images.
Say you have well-structured, clean numerical data and you’d like to predict customer churn in your insurance company or classify your customers and their lifetime value. In this case, building and training a simpler machine learning model like a logistic regression is a better choice.
But if you’re dealing with unstructured data types and need to do image recognition, deep learning will do the job here. Say you’re giving a DL algorithm an image of a dog but you’re not telling the model what the picture presents. Here, the neural network will recognize, step-by-step, features of a dog, until it classifies the image as a dog image.
Ushur’s Customer Experience Automation (CXA) Platform is designed to support the operations of complex businesses in the insurance, healthcare and financial industries. Ushur and Ushur AI Labs have already thought through the decisions an enterprise business needs to consider when evaluating AI projects in highly regulated industries. Using proprietary language and document services, Ushur’s AI solutions help businesses understand user intent, evaluate document content and drive seamless customer experiences so that customer support functions and business owners can focus on the highest-value projects in their queues.
If you want more information on the machine learning and deep learning capabilities in the Ushur platform, and to see how Ushur blends AI with customer experience technologies, visit ushur.com/platform.
Columnar data files are the dominant form used for exchanging information across industries like FMCG, Shipping, Finance, Insurance and others. The row-column format creates an understandable and recognized mechanism for sharing information which also happens to be easy-to-digest for digital processing. A lot of financial decisions and estimations are made off of information stored in rows and columns and colloquially the files are referred to as CSV’s (comma separated values). However, Microsoft Excel has quickly become the industry standard for visually processing CSV’s and it provides added user functionality like embedded tables, and the concept of “Sheets” where information can be logically partitioned.
There are many business conversations where data is exchanged via Excel/CSV files, and a classic example in the insurance industry is the dialogue between insurance brokers and insurance carriers. Here, CSV files are used to exchange information during the RFP (Request for Proposal) phase. The US has many insurance brokers. Brokers vary from individuals doing part-time work to small companies that employ a few people all the way to large brokers that employ hundreds of brokers.
When brokers meet prospective companies and need to quote them for their insurance needs, each broker communicates in their own styles and lexicon to represent the information they’ve gathered. For example, a simple column to represent “Date of Birth” can appear as “DOB”, “Birth date”, “Employee dob” and so on. Now, multiply that variability with about 100 columns of data for each customer and you understand why automating the process is so complex.
Automation and Efficiency are the primary goals of many carriers in the insurance industry in 2023. As more and more carriers automate portions of their backend and quote processes, discontinuity in data format and structure are exceedingly difficult to manage and thus need a lot of manual back-and-forth. It is estimated that in many cases, it may take anywhere from 1 to 5 days to clean and structure all information in formats necessary to correctly persist information in their System of Records (SOR’s).
This is where the Ushur Data Transformation Engine comes in and makes an immediate impact – adding automation to an intractable problem which was dominated by manual operations, and providing a high degree of efficiency by dramatically reducing the time taken to process the entire workflow.
In the RFP process, an insurance carrier typically receives hundreds of RFP requests per day. These RFP requests usually arrive in the form of emails being sent by the brokers to the sales executives. Among the numerous attachments, there is an excel file containing census information. This information generally includes: names of employees, birth dates, classes of employment, products required, premiums, specific clauses such as Cobra, eligibility for each member and so on.
This extraordinarily manual process invites automation to save on time and expense, and prevent overworked employees from introducing errors in data. Automating it, however, requires intimate knowledge of the process, a data standardization and cleaning routine, and user-friendly tools.
As Ushur went about solving this problem, we overcame many engineering challenges, some of which are listed below:
Ushur begins with table extraction. The data users want usually resides in tables within these excel files. Surrounding the tables are huge chunks of irrelevant data such as legends, demography info, huge headers, titles and other noise. The extraneous noise affects the performance of downstream tasks such as classification and transformation. The Ushur novel table extraction algorithm helps us to effectively customize table extraction for multiple use-cases. We use a combination of NLP and Vision Techniques to solve this problem.
The next step is column classification. Ushur recognises different column headers and normalizes them into CRM accepted headers. Since these excel files are sent by multiple brokers from around the world, the variation in representation of the data is immense. Ushur’s domain specific models help to cater to use-cases per domain.
The final steps are transformation and validation. A lot of transformations and validations are required to be performed on these normalized input tables. This ensures data consistency and easy feed into the customer’s system of record. Since, this is a highly customizable problem depending on various use-cases, it’s imperative that we enable citizen developers to perform these operations at their convenience. We enable this by the mechanism of “rules”. We have created our own rule language that end users can use to write their rules.
Once we have executed the above steps, we now have a clean, normalized and consistent excel file. We send back this asset as an excel file or as a JSON to be fed into the customer’s CRM.
Ushur’s columnar data transformation engine is a part of the patented Ushur Document Intelligence Services Architecture (DISA) and deployed within Ushur Intelligent Document Automation™ (IDA). DISA applications have led to significant improvements in business metrics for our customers – in one case, one of Ushur’s clients was able to reduce manual labor from 30-36 hours to about 3 minutes, and see many examples where there was no human intervention of any kind, freeing up agents to focus on high-touch, more meaningful interactions, rather than back office tasks. Best of all, the ability to create new rules very quickly via the Ushur no-code flowbuilder enables Ushur to provide ROI to customers in days.
There’s been a significant amount of buzz and excitement around generative AI and ChatGPT lately, and for good reason. Not only is it a potentially transformative technology for just about every industry under the sun, it’s one that is rapidly opening up to a growing number of people.
One of the better qualities we humans have is a capacity for true creative genius. When transformative technologies are opened up, that ability to create is unlocked for a larger portion of the population. Think about GPS capabilities, for example, which started as a niche technology designed for and by the military. It was ultimately opened to everyone, and soon we had GPS capabilities built into every new car. Applications like Google Maps and Waze were created, eventually paving the way for companies like Uber and Lyft to disrupt the entire taxi industry. Today, I can go for a 15-mile run through the woods and know exactly where I am, where the trail markers are, every detail about every turn and elevation gain, all by simply wearing a touch-screen watch on my wrist. Touch screen technology has existed for a long time. GPS has existed for a long time, too. It’s the assembling of these pieces to create better human experiences that is truly profound, which is why technologies like ChatGPT are so compelling.
Natural language generation technology has been around for nearly a decade. Many companies have created Machine Learning models known as Large Language Models or LLMs. Essentially, this boils down to using massive amounts of data to train a model that can generate answers to questions. In its simplest form, think auto completion when writing an email or sending a text. The underlying technological principles are not new, but that level of accessibility is in its infancy. Today, we are just beginning to scratch the surface when it comes to the potential applications.
Think about the explosion of innovation that has followed our shift away from on-prem data centers to cloud infrastructure. Before Amazon, GCP or Azure the process of building, deploying and running software was an onerous one for founders. Startups needed massive early stage financing purely to run servers and storage racks. Now, the cost of starting a company is minuscule in comparison, largely thanks to the availability of cloud infrastructure. If you’re building application software today you don’t have to worry about all of the hardware investments and overhead you did a decade or two ago. Now that the cloud has become commoditized and is accessible to everyone, it has made it easier for more startups to get their innovations off the ground — and quickly. In turn, more innovations follow at an increasingly rapid pace.
A lot of the recent chatter around ChatGPT has been what generative AI will mean for long-standing incumbents. There have been significant layoffs across big tech, enough that most of us in the industry know someone impacted. Does ChatGPT mean the end of Google, Facebook, or their revenue models? While that may be the newsier angle, I don’t think it’s the right place to focus at all. These companies will regroup, and remain powerful. They have leveraged their incumbency well, and many have invested in LLMs and generative AI themselves. They have enormous assets that they can invest in creating new technologies based on generative AI, or fund smaller companies already doing the work. I don’t believe they’ll be disrupted nearly as much as people might think, they’re well governed companies with strong strategic vision. Going back to the example of Uber and Lyft, many pundits and experts predicted they’d mean the death of the auto industry. Those companies – and their considerable resources – were able to adjust and adapt, and today demand for personal vehicles often outstrips supply.
But when we talk about disruptive technologies, it’s an absolute certainty that eventually a giant or two will be knocked from their perch. Where a status quo that no longer serves the customer exists, the potential for disruption is ripe. Google has enough data banked to continue innovating for multiple generations. They are famous for moving on from projects and products that don’t work. They wrote the book on making adjustments. But if we look at other big companies that have crumbled quickly, they share some clear commonalities. Monopolistic practices and processes had set into the way they operated, making it impossible for them to leapfrog into the next generation. Blockbuster, Circuit City — these were companies in the good-to-great category that weren’t quick enough to adapt and adopt when a newer, better way of serving the customer came along. Today, this is the same challenge healthcare and insurance companies face. In these regulated industries, incumbency is no longer an advantage. How do they adapt and survive in the face of increasingly sophisticated consumer expectations?
If there’s one clear obstacle between regulated industries and the wider adoption of technologies like ChatGPT, it’s the inherent bias within them. Many people think of AI like it’s something with a personality that can imitate or replace a human. But at the end of the day, it’s just a machine. Even its ability to generate language is a very statistical, mathematical, science-driven outcome. Its ability to learn is based entirely on the data it is fed, and so it might not be representative of all populations but rather the populations contributing most actively to the data set. Those anomalies and biases, the false negatives and false positives, have to be weeded out. For some applications of generative AI, like ChatGPT’s simple search function, 70-80% accuracy is good enough for the user. For others, 98% might fall short.
For example, let’s say you’ve applied for a life insurance policy. On the other end, a team of underwriters looks at your application information — age, habits, personal data, income, lab results, blood tests, a physician’s summary — and manually identifies your risk against a predetermined set of markers. That level of risk then dictates how high a premium you’ll have to pay. Some applications are rejected outright. Today, it costs most life insurance companies upwards of $500 just to reject a life insurance application. A technology like generative AI can come to the same conclusion regarding those risk factors based on the biomarkers in your application at a fraction of the cost. Of course, an approved application carries a higher financial risk for the provider than a rejected application. So while a company may be able to use technology that is only 70% accurate to reject applications, that rate won’t cut it for approving them. Regardless, even if just half the manual work is eliminated customers will be served more quickly and effectively at a lower cost to the provider. The immediate opportunity for industries like healthcare and insurance is to identify which applications they can accept under 100%, and begin to leverage generative AI for those tasks.
Applied to the world of healthcare and insurance, generative AI has enormous potential capabilities. For customers of these industries, costs continue to go up and access to care is dwindling. In the United States especially, going to the doctor is an experience full of friction. Recently, I started to feel a bit unwell the day before a long flight. It wasn’t an emergency situation, but a time sensitive one. Though I’ve been seeing my primary care physician for over 20 years, they couldn’t see me on short notice and sent me off to urgent care. All I really needed was a quick diagnosis to give me peace of mind before I sat on a plane for 16 hours. A technology like ChatGPT has the potential to provide a diagnosis like that, without requiring a trip to the doctor. Or worse, urgent care.
Access to healthcare is critical. I have better than average insurance, but still couldn’t get the help I needed when I needed it. Think of how it must be for one of the millions of people who don’t have the same quality of insurance or care. It shouldn’t be like that, nor does it need to be. Technologies like ChatGPT have the potential to democratize healthcare, to make it more available and accessible in a way that betters our collective quality of life. The big question for today’s incumbents is whether they’ll be the ones providing that improved experience, or whether they’ll go the way of Blockbuster. The opportunity to adapt and improve is at their fingertips, but action is imperative.
For those who are not familiar, Interactive Voice Response (IVR) is an automated system that uses technology-enabled triggers to triage customers within a phone support pipeline. The concept of IVR itself dates back to 1962, and throughout the last two decades, IVR technology has become a staple in complex professional industries like healthcare, telecoms, finance, insurance, and education.
IVR is so penetrative, in fact, that most customers have strong feelings about the technology — and most of these feelings aren’t good. In a 2019 study, 61% of surveyed customers reported a negative association with IVR, leading many companies to drop IVR systems from their customer experience (CX) strategy completely.
Yet global IVR adoption is still growing and the market is set to reach a $6.7 billion valuation by 2026, representing a 7.9% year-over-year increase. So why are some businesses continuing to prioritize IVR while others are disregarding it as a valid component of customer support?
Negative associations with general IVR solutions are reasonable. Most IVR solutions push customers into a predefined set of options that offer poor experiences and usually don’t solve their needs. However, many companies continuing to implement IVR solutions do so because their support departments are inundated with requests and often can’t offer service to customers in a reasonable time–or can’t get to all customers that need assistance. Paired with potential cost savings, companies are forced to implement far from imperfect IVR solutions as a best attempt to help customers.
The reality is that today’s IVR is not the same IVR that’s infamous for creating unhappy customers – and while the companies who are keeping IVR know this, they are also refining their strategy for using it . Those that know how to leverage AI-powered IVR are not turning their backs on the technology, and are instead welcoming it into their CX strategy with a twist.
After hearing a prerecorded message, which includes a courteous greeting personalized for the company, IVR customers are given a number of options to choose from that will direct them to the right customer service option for them.
In some cases, the IVR system can ask additional questions to further narrow down options and ensure proper redirection through multi-level menu functionality. IVRs are often supported by automatic call distribution (ACD) solutions. They place callers in a queue based on the information collected from IVRs, where higher-priority calls are answered first. As pointed out by IDC, they also traditionally rely automatic speech recognition (ASR) to hear what is being said to them, but usually lack natural language understanding (NLU) capabilities to “truly understand what is being said.”
IVR was created with the goal of understanding the intention customers are calling with and responding accordingly. Not only would it make the conversation more personable, but it would also shorten wait times and improve call resolution rates–both having a positive impact on the overall customer experience.
In practice, these automated phone systems can be frustrating due to their limited ability to provide tangible support. Customers may resort to using foul language or other creative methods to bypass the system altogether.
Humans like choice – some prefer digital messaging channels, while others lean more towards traditional calls. However, the more urgent the situation, the more likely they are to give your business a call, hoping that it’ll be the fastest way to solve their issue. You can imagine their frustration when their urgent phone call is met with an automated voice response and a 30-minute hold time (or longer).
IVR call deflection is a new approach to handling calls— urgent or otherwise. IVR call deflection empowers callers by giving them the opportunity to switch to a different communication channel like an AI-powered chat if they’ve heard their wait time is too long for their purposes. Their query will be resolved quickly through an automated solution, freeing up agents to handle more pressing issues.
Here’s how it works. When customers are met with long wait times, an IVR call deflection system will step in and give them the option to continue the conversation over a digital channel instead. It would sound something like this:
“Call volumes are unusually high. If you’d like to continue this conversation over text, press four or say ‘let’s talk’.”
IVR call deflection solutions benefit from recent advances in artificial intelligence (AI). Conversational AI models use Natural Language Processing (NLP) to understand customer issues and concerns, and guide them through the final steps for self-service and resolution. Particularly as large language models (LLMs) become more available for use, conversational AI deployments will only sound more and more like an empathetic and capable support agent to customers.
The more complex the industry, the harder it is to maintain a positive customer experience. Take the Insurance industry, for example. When a severe weather event strikes an area, insurers that cover the affected area may experience extreme surges in call volumes. Offering an IVR solution that still allows customers to submit a claim and schedule a phone call with an agent can expedite claim closure and ensure customers are satisfied more quickly.
For customers whose preferred mode of communication is not by phone, chances are they’ll reach for their phones and dial in, convinced that they’ll only get their request processed quickly if they choose a voice-based medium. These customers are surprised and delighted to discover a digital channel integrated with an IVR that is capable of handling their request, and may opt to deflect their call.
IVR deflection solutions for call centers mean more than just a faster resolution time. They also result in a more satisfactory employee experience. When many queries are handled automatically, your agents aren’t overwhelmed by the number of calls they have to answer. More importantly though, the conversations they’re assigned to are always within their area of expertise and of the severity that merits their involvement. This increases the chances of closing tickets, leads to higher productivity, and higher levels of employee satisfaction. Support teams want to help customers, but they prefer working on important cases that can make an impact.
Automated support is available 24/7 – customers can call anytime, day or night, and still have an option for self-service with call deflection. While customer service agents can only focus on one call at a time, call deflection integrated with an IVR can handle multiple calls simultaneously. IVR technology allows companies to provide a frictionless experience by resolving consumer queries in real time,irrespective of whether it involves providing information on travel insurance or updating financial forms.
Another benefit of intelligent IVR technology is the integration possibilities within each company’s data and Business Intelligence (BI) systems. For example, IVR systems allow enterprises to quantify exactly how many times each client has reached out with a request, or which types of queries are brought up most often. As a consequence of having that data, creating complete, up-to-date customer profiles empowers you to make better business decisions – after all, you’ll be basing them around reliable data.
One of the greatest advantages of IVR technology is its ability to effectively route calls. For example, when a customer contacts an insurance company, they’re connected to an agent who is qualified to resolve their issue. Gone are the days when a caller was transferred repetitively from one agent to another in the hopes that their request will finally be tackled. Effective call routing reduces handle time and increases first-call resolution.
Support has come a long way in the last 60 years, and the businesses that take advantage of AI-enabled solutions now have solutions to help their customers and solve their issues more quickly. When integrated into an IVR solution, Ushur Invisible App™️ is the only product of its kind designed to intelligently automate workflows, resulting in a better customer experience and higher retention rates.
With Ushur Invisible App™, organizations can implement a call deflection solution over chat or SMS that guarantees safety, security, and compliance while also helping triage a slower and expensive voice-only customer queue. Plus, Invisible App doesn’t require a name and password like a usual app so customers don’t suffer from login fatigue.
Learn more about how Ushur can help you with call deflection.
Ushur, the leading no-code digital Customer Experience Automation (CXA) platform, and Virtusa, a global leader in guiding clients through the challenges of digital disruption, are excited to launch a new partnership.
Virtusa’s appetite for disruption comes from the teams and technology it has centralized in its business. The unique expertise that Virtusa has acquired helps their teams guide insurance carriers through turbulent transformations and leverage digital capabilities like those from Ushur’s AI-powered, no-code platform. Across Property and Casualty, Life and Annuity, and Group insurance, Virtusa prepares clients for the next revolution of technology in their core businesses — no matter what it may be.
The complex and regulated ecosystem of carrier technology solutions make digital transformation projects burdensome without technology and service providers like those brought to bear with this new partnership. The Virtusa and Ushur partnership launches digital transformation projects for insurance carriers into a new class of accelerated delivery because they are already enterprise-grade to the core.
Digital transformation replaces the unscalable and manual versions of insurance carrier processes with more-repeatable, technologically-driven solutions. Leaders in these industries are constantly looking for ways to reduce journey friction, accelerate development cycles, and cut operational expenses with digital innovation because time kills customer experiences.
Virtusa clients going through digital transformation efforts can now bank extra time by using the flexibility of machine learning (ML) thanks to the Ushur platform. When speaking with claimants, partners, customers, and other stakeholders, Virtusa clients need Ushur’s state-of-the-art engagement capabilities. With just a couple of clicks, Ushur and Virtusa clients can rely on conversational AI and machine learning so they no longer need to programmatically account for every possible path and route a customer could go.
CX often suffers from technology underinvestment, and both customers and employees already have elevated expectations of what technology-first solutions should look like – it should be easy and it should be fast. When it comes to making digital transformation real, customer experience excellence is a bigger hurdle than many others.
Enterprises already understand that digitization projects are essential if they currently rely on people-first processes for customer service. Scale of customer interactions is increasing, not decreasing. Even in the best of times, it can be hard to find enough of the right people to help triage at that scale. Additionally, people leave jobs, or switch companies, and competing for that customer service expert is a difficult path for achieving growth.
Building a practice around Customer Experience Automation™️ is a new and emerging strategy for adding automation to customer service functions, and can truly help enterprises accomplish their digital transformation goals.
Customer Experience Automation™ (CXA) is the interdisciplinary intersection of artificial intelligence, process automation, and conversational interfaces blended to optimize the customer experience and engagement.
Automation removes procedural barriers that prevent expedient resolution. Artificial intelligence (AI) uses historical data to understand and predict future behaviors. Customer experience is the product of two-way conversations where action matches the intent.
Insurance carriers, and financial services organizations are already using digital-first strategies for interactions with members, customers, patients, agents, brokers, and providers, and they rely on partnerships like the one between Ushur and Virtusa.
Virtusa establishes a practice at each client for tackling customer experience projects and brings in the best-in-class technology needed to make delivering those a reality. Virtusa will use the Ushur CXA platform to engage directly with business users to better understand customer experiences, and then build out the digital representation of those customer experiences. The partnership brings the experts in digital transformation the kind of technological capabilities they need to make those conversations seamless
We want to say that Virtusa gets experts on digital transformation and Ushur gives them the tools to build customer experiences. The no-code capabilities mean they can easily get agreement from Virtusa clients.
Everyone involved in the insurance journey, from market development to service and administration, expects digital-first self-service to be available. Carriers with self-service-first principles will naturally rely on intelligent automation in the insurance journeys to engage with customers, claimants, brokers, adjusters, and providers.
Brands can improve digital customer engagement by leveraging the API-driven customer experience automation platform via integrations to make it the ideal technological bridge between consumers and the back office claims management, policy administration, underwriting, and billing systems.
The new partnership between Virtusa and Ushur highlights the capabilities of Ushur’s Customer Experience AutomationTM platform and puts it in the hands of the insurance carriers that Virtusa is guiding through digital transformation projects. Ushur applications can deploy in days, are easy to task for reuse, and are simple to deploy.
Virtusa expertise and technology, partnered with the Ushur customer experience automation platform, will help the industry's leading carriers tackle the challenges of new customer experience programs in the coming years. Together, this partnership will design and deploy the unique value-creating customer experiences that carrier customers have been waiting for.
Insurance companies receive numerous emails, which contain requests for proposal (RFP) quotes from brokers. These emails have valuable information that can help insurers to make informed decisions about coverage and pricing. However, extracting data with the desired accuracy from emails can be a significant challenge even with the help of AI. Here are some of the challenges while using AI to extract data from RFP quotes.
Using AI to automate the RFP quote intake process can help insurance companies reduce the time to respond to requests, giving them a competitive edge over those that process quotes manually. There are also potential benefits such as increased efficiency, monetary gains, and so on, without losing sight of the numerous challenges of using AI to extract unstructured data from emails in the insurance industry.
Ushur has overcome these obstacles and developed a data extraction framework for a Fortune 500 company. Using our intelligent data extraction capabilities and the Ushur Invisible App, our customer is now able to respond to incoming quote requests in 10 minutes rather than 5 to 6 days. Our AI pipeline can accurately identify and extract around 170+ entities from unstructured emails and provide highly structured information within a few minutes.
After conducting an extensive evaluation of various techniques, Ushur concluded that an ensemble of multiple methods was necessary to effectively extract the various entities from the email.
Ushur established a hierarchical relationship between the entities to accurately associate them with specific insurance concepts and devised mechanisms to narrow down the region of interest for each entity. An efficient modeling approach, which could capture these hierarchical relationships and select appropriate extractors for each entity was required to implement this. After careful consideration, Ushur determined that an ontology-guided method was the optimal choice.
This approach enables a structured and comprehensive framework to create the data extraction pipeline that ensures accurate and consistent results across large volumes of incoming emails from our customers. The approach is validated by accuracy rates exceeding 90% on a corpus of over 10k emails across a period of about 12 months. Ushur accomplished this by combining a novel ontology-guided extraction approach with an ensemble of NLP techniques.
The steps involved are:
To conclude, an ontology-based approach provides a consistent framework for data extraction from unstructured content. When combined with the power of AI, this can assist businesses in automating and increasing the efficiency of their RFP intake process.
People who first hear about the Ushur platform often ask what all can one do with the full suite of its capabilities. For better or for worse, people often have to deal with the answer that one can do pretty much anything. It’s a platform designed to let non-technical users build customer experiences that can be run once, twice, or a million times, and that leverage the pre-built capabilities that make those experiences cutting-edge and representative of a modern consumers experience.
The platform centralizes around a no-code flowbuilder by which users drag and drop each step of the experience. Users can use any number of pre-built modules, but altogether the small blocks of capabilities turn into a thorough experience representative of the quality enterprise customers demand.
A platform alone offers only so much value, which is why an API-centric architecture provides so much value to Ushur customers. The Ushur platform integrates with services like Salesforce, Zendesk, Twilio, or Amazon Connect. Those integrations extend the pre-built capabilities within the Ushur platform and make the experiences even more feature-rich and personalized. Ushur customers can serve up information from back-end systems, as well as persist data back to those same systems from customers. Altogether, that makes data exchange and gathering a hassle-free building experience.
Products on top of the Ushur platform represent pre-built technological capabilities to emulate well-known experiences like mobile applications and customer portals. Invisible App™, Ushur Hub™, Conversational Apps, and SmartMail package essential components to make solution-design with Ushur partners and your internal stakeholders easy, repeatable, and quicker.
Ushur leads the category of customer experience automation (CXA) and has an opinionated point of view on the components and trajectory of the space. CXA, from our point of view, is the automation practice by which customers converse with brands in their natural language and all appropriate resulting process implications execute behind the scenes. Consider the scenario of reaching out to a customer and asking them how they are doing, and how easily they have access to food and shelter (a social determinants of health use case). With Ushur, customers (patients) can communicate in a natural dialogue, and Ushur can connect them to the local resources they need to achiever more stable housing and food. Ushur can also persist that data into the customer relationship management (CRM) system, so brands can track their past conversations and results.
Customer experience automation bridges the gaps opened by point or spot solutions. The bridges are created to cover siloes in businesses who all own different portions of their technology stacks. CXA differs from other spaces like robotic process automation in that it is designed to be end-to-end, natively.
Ushur is designed with compliance and security in mind to service our customers handling sensitive data and transactions in the most intimate moments of their relationships with their customers, partners, and internal stakeholders. Our clients span insurance, healthcare, and financial services and we help them optimize their customer experience strategies in a time frame that make sense for them — projects that usually have to wait quarters or years can be started in weeks or months.
Ushur partners with some of the most established technology providers in the enterprise space that have already realized that they need a customer-facing (consumer, partner, internal stakeholder) interface and automation solution. Across the insurance, financial services, and healthcare sectors, Ushur helps technology providers who are serving their industries expand their capabilities to include conversational AI, prepackaged app-like interfaces, and email triaging capabilities.
Ushur’s partner program is designed to make it easy for technology providers who need to move quickly with building customer experiences in an affordable and maintainable design. Help your customers create a CX strategy that reflects the profile and brand experience they want their customers to enjoy when interacting with them on a day-to-day basis.
Part of Ushur’s core culture as a company is a collective yearning to solve problems. We serve a core set of customers that tend to share common needs when it comes to customer engagement, but the way we’ve built and shaped our platform is rooted in a much higher level of analysis of the way people and commerce work.
Ushur was initially inspired by disappointing user experiences within shared online marketplaces, like Craiglist or AirBNB. In these scenarios, the dynamic is simple: A prospective buyer or traveler navigates through a listing and makes a decision.
Of course, a dynamic so simple also means that there are questions left unanswered. And these unanswered questions come at a most inopportune time–at the height of the consumer’s interest. They are never more likely to buy than they are with the listing right in front of them, but if a key piece of information is missing most people will simply move on to the next option.
What if there was a way to connect buyer and seller in real-time? This question is at the heart of our innovation strategy, but so many industries have left this path critically unexplored, even as the demand for personalized consumer experiences grows daily.
Today, customers are expected to wade through thousands of online reviews from unrefined sources to make decisions about a high-value purchase. Instead, customers should be able to interact with an expert who can navigate them through the process of selection, purchase, set-up, and maintenance.
The auto industry, and the process of buying a car, is a perfect use case because it represents the intermediary point at which many organizations arrive and unfortunately stay. Buying a car used to be one of the all-time most stressful processes a consumer experienced. The people who sold them had bad reputations. Both customer and salesperson were incentivized to haggle, be confrontational and withhold key information. Most critically, customers never left the lot sure they’d gotten the best deal.
Fast forward to today, and buying a car is now just about as easy as clicking a button. Even in-person transactions are swift and painless because the internet has democratized information about car pricing. That one major point of contention–the best deal–has been removed from the equation. The salesperson’s role has shifted from negotiator to facilitator. What hasn’t changed is that the onus is still on the customer to do all of the requisite research on the car itself. The sale is quick, but the education process is long and painful.
A few years back, I was in the market for a car. I knew the year I wanted, and the color. I wanted a specific interior that wouldn’t scorch me during a hot California summer, and I wanted to be sure it was a certified pre-owned vehicle. With such specific criteria, it took me more than six months to find the car I wanted. When I finally did it was in Texas, in a lot where it had been sitting for over a month. I bought the car via text message with the dealership within an hour, and they shipped it off to me the next day. I got the deal I wanted, they got the customer they needed, and everybody won. But what if I’d been able to see all the cars that matched my criteria by simply plugging them into a two-sided marketplace app used by dealers and customers? I’d have had that car a lot sooner. This should be the standard to which any modern, customer-facing business aspires.
While this all starts with customer delight and customer experience, the long-term implications are so much broader. Obviously, the buying experience is improved and differentiated. But customers are also more likely to make an educated decision. They’re going to be more satisfied with what they purchase, and less likely to be disappointed. Ultimately, people will feel comfortable making more and bigger purchases because when they do, they’re actually getting what they want. Real-time connectivity between buyer and seller is the next great frontier.
What claim experience would you design if you could build an end-to-end policyholder or claimant journey with responsive conversational abilities and the ability to deploy that workflow across any channel? Thanks to the just-announced partnership with Guidewire, you can deploy that journey with just a few clicks.
Ushur is excited that we are soon to be listed on the Guidewire Marketplace, and will be joining the Property and Casualty (P&C) technology platform’s ecosystem of partners. Now enterprises with a Guidewire core system can use the Ushur ClaimCenter Accelerators for First Notice of Loss (FNOL) and Workers Compensation (WC) claim acknowledgement to automate critical inbound and outbound customer and claimant conversations.
Guidewire ClaimCenter is a leading P&C claims management system. ClaimCenter enables adjusters to process and resolve claims faster by removing the friction from internal claims handling, with automated workflows powered by data. Ushur, by comparison uses drag-and-drop capabilities to make designing automated, 2-way customer conversations easy. Business analysts can build workflows that incorporate conversational artificial intelligence, something that used to require the expertise of data scientists. The Ushur canvas and single-click deploy drastically reduce the cost of building and enhancing critical high-value conversations across not only the claim, but entire insurance lifecycle.
“It’s hard to overstate the change that automating customer experiences can bring to any organization and the effect a successful and cost-effective digital transformation can have on a brand’s relationship with their customer” says Meredith Barnes Cook, Vice President of Industry Groups at Ushur. “Ushur focuses on making customer experiences intuitive and partnering with Guidewire means gathering data and starting claim conversations for auto and workers Compensation can be a part of this year’s digital transformation projects.”
A McKinsey report from 2020 on the State of P&C covers the key topic of costs in Property & Casualty and breaks down how generating productivity to match or exceed the costs is imperative for any carrier in the industry, “...the industry needs to reset its operational efficiency. In recent years, while labor productivity has risen, overall industry expense ratios have also increased.” Ushur’s partnership with Guidewire matches this demand in the industry. The integration maximizes existing core system investments in Guidewire by introducing a productivity layer for process automation and efficiency while interacting with customers and claimants. It uplevels customer experience while ushering P&C carriers through an agile digital transformation that will also reduce costs and improve carrier employee experience.
Bruce Holbert, Head of Global Channels and Partnerships for Ushur, describes this new listing as “a game changer for Ushur’s partnerships and the Guidewire install base.” He said, “the Guidewire customer base is an established group of P&C carriers who probably have years worth of digital transformation projects they plan to execute to improve their overall policyholder experience. Ushur’s platform with capabilities that make it easy to build, deploy, and manage intelligent automation workflows means they can attack those transformation plans at warp speed.”
Ushur’s integration listing on the Guidewire Marketplace includes extra details about the Ushur platform. The apps in the listing will immediately help carriers meet their customers in their preferred channels and ease friction-filled experiences.
If you have unanswered questions about the Guidewire partnership and the best use cases for Ushur’s Customer Experience Automation platform, reach out to us directly or schedule a demo.
Digital transformation is something every modern company talks about, thinks about, and worries about, all of the time. The reason for that worry, largely, is that most organizations approach the task backward.
Many companies have a tendency to think about what they need to change rather than the outcome they’re trying to drive. They view digital transformation as something that has to be tackled all at once, an end-to-end revamp across the entire organization, with IT as the centralized decision-making engine.
In my experience, that strategy is a recipe for disaster. I’ve personally never seen it work. Organizations embark on these painful 3-5 year journeys to replace a legacy platform, spend upwards of $50 million, and in the end, are left staring at a partially transformed business. That’s because modernized infrastructure alone doesn’t actually modernize the system of customer engagement.
In reality, digital transformation can happen piecemeal, in a way that delivers targeted benefits to customers and users immediately rather than years down the road. It can be done for a lot less than $50 million right out of the gate, and it can be done without a multi-year overhaul of whatever legacy systems seem to stand in the way of progress. Let’s talk about how.
Digital transformation should begin with a simple question–what does the customer experience need to be? Using that as a guiding principle helps identify the right processes and infrastructure along the way. We believe that digital transformation can, and should, happen in stages–starting with customer experience.
Instead of looking inward at what they don’t have, organizations should look outward at what their customers want. Insurance providers, for example, can identify their most friction-prone customer engagements that lead to the most inbound interactions, and tackle those on a case-by-case basis without revamping their core policy admin or claim systems. The solution doesn’t have to start at the center.
Instead, organizations can identify what we refer to as “micro-engagements”–then digitize and modernize those experiences in ways that delight the customer. Many of our customers have seen immediate success with this approach of “a la carte” digital transformation because it’s strategically designed to have a positive impact on the customer as quickly as possible. It also allows breathing room to complete the rest of the journey the right way. The impact happens faster, the initial investment is lower, and you have a clear line of sight to value with real-time customer feedback.
We believe immediately eliminating any friction that exists in the way customers are served gives organizations the best chance to retain those customers through the long process of a complete digital transformation. Modern customers want to do business with companies that meet them, not ones that expect to be sought out. Part of this dynamic means that companies can’t expect their customers to suffer now for the promise of better things later.
One of our customers, a large insurance company, needed to modernize the way it engaged with customers. For instance, one process required claims adjusters and disability benefits specialists to manually call people going on absence for any number of reasons–maternity or paternity, injury, illness, life events–resulting in outbound phone calls that took countless hours to collect a few basic pieces of information.
But its central claims system operated on a 25-year-old mainframe, and to replace that mainframe would have been a five-year, $50 million initiative. We found a quick way to ingress info from their legacy mainframe into our platform and egress it back once it was processed. We enabled the digitization of all claims conversations, completely automating a process that used to take an average of three weeks and six call attempts per customer. Now, 90 percent of claimants complete the process in under an hour, via text. All without a single change to their underlying infrastructure.
At its core, digital transformation exists to modernize the way we conduct business. That should start with the customer experience. Everything else on the back end can be managed and orchestrated over time. Meanwhile, you retain your customers and provide them with better service, right away.
If you’ve been reading my co-founder Simha Sadasiva’s blog lately, you’re probably at least somewhat familiar with the concept of micro-engagements already. In the backdrop of the fact that Ushur was just officially issued the patent (US20170193557A1) on micro-engagements, I thought I would talk a little bit about the underlying technological strategy that makes this approach to Customer Experience Automation both unique and possible.
More and more of our daily lives are automated, particularly as consumers. But automation is not necessarily making things smoother for all customers on a consistent basis. To borrow an analogy from Simha, think about how much easier it is to use an ATM than it is to use the self-checkout at a grocery store. Of course, one action is far more complex than the other, with many more moving parts. But to be a viable long-term solution for consumers, self-checkout eventually needs to deliver the same painless, user-centric experience offered by the ATM.
The reality of most technologies available today, including any type of automation or AI, is that the customer is usually an afterthought. Modern technology companies are almost exclusively focused on solving back office infrastructural problems, the impacts of which they expect will eventually trickle down to the consumer. That has never worked and never will. We founded Ushur based on the idea that the customer experience must be solved first and intentionally designed into the underlying platform. When simplified, the key differentiator for Ushur is that we’ve built a platform that is architected specifically to engage at scale while meeting users where they are.
While we certainly didn’t anticipate the pandemic, we knew we needed to arm our customers with the ability to act instantly. We knew that users were suffering from information fatigue and cognitive overload — heavy websites, log-in requirements, experiences full of friction. By comparison, micro-engagements with Ushur provide a frictionless and lightweight experience. We also knew that very few consumers in today’s market are stationary. The perpetually-moving nature of micro-engagements allows the user to continually engage at any point in time based on their lifestyle and their needs.
We’ve mentioned micro-engagements, but have not really explained what they are, so what are they? Generally, they are easy ways to exchange small snippets of critical information. It’s helpful to think of micro-engagements as being measured in units. At any given point, an insurance company, healthcare provider, or financial services company is in the middle of countless small and separate units of conversation with customers. These conversations together are like a sea in that they’re fluid and might start or go anywhere — a phone call, an online portal, an email, a text. Each of these is a micro-engagement.
At the enterprise level, there are hundreds of thousands of conversations happening at any given time. Our overall experience as customers consists of each of these units. Response time, ease of use, and the facilitation of next steps all come together to formulate the holistic view and measure of a good customer experience. We set out to enable companies to provide exceptional micro-engagements that satisfy those measures with the goal of creating an overall experience that is simply awesome.
So why does micro-engagement matter? It is our belief that if a system were to bring a compelling experience to the customers, it has to be built as such right from its foundation. In a busy mobile world with so much information it is too much to ask a customer to stay engaged with an enterprise for too long. The enterprise must meet them where they are, in mobility and in short time intervals. We believe that the micro-engagements that form the foundation of our platform is getting the enterprises to meet their customers, where they are, in their journeys.
When we initially founded Ushur, the platform was designed for real time engagement, person-to-person. But we noted the nature of person-to-person engagements becoming increasingly asynchronous, and we decided the platform must be a purposeful part of the solution. We started by alleviating a common pain point — call center wait times. Instead of consumers calling in and waiting for help, an experience that might vary wildly in quality from one customer to the next, we decided to give enterprises the ability to reach out to people in a way that fits within their own personal lifestyle and maximize the value of their engagement.
Around this time, society had started to change around us, too. One trend we noticed in particular was the concept of micro-donations; people who didn’t have big sums of money to give but who wanted to make a positive impact regardless. We drew a parallel between this and consumer experience to develop the concept of micro-engagements.
How do we break down experiences to their component parts; both front end and back? Our approach is taken module-by-module. Most engagement solutions start in a similar place — a short module intended to send a message to the user with the enterprise reaching out for a specific reason. When we want that user to respond, we employ a different module. When we want them to upload a document, it’s yet another. This allows enterprises to literally build a workflow for the user piece by piece, like a Lego set.
Of course, automation on the backend needs to happen in parallel with this modular approach to customer engagement. We use the same modular, workflow-based approach here, too. And at the end of the day, no matter what the system is doing on the backend, it is still doing it on behalf of the end user and in service of their data. In some cases, the customer might only experience the engagement as a single unit, while on the backend there are actually three modules that make up that experience. A good example of this would be the collection of personal information. Say I want a customer's date of birth, phone number, and address. The customer will experience this as a single form, while on the backend it’s processed as three separate engagements.
If I may return momentarily to the self-checkout analogy to conclude, technology alone cannot solve a problem. A scanner alone isn’t enough to guarantee a good customer experience. How do you determine how many people will be in line? How many items will they have? How quickly they’ll scan and bag them? There is no way to control or enforce the experience unless it starts the moment a shopper picks their first item off the shelf – or perhaps even earlier! Customer experience automation has to be an end-to-end experience. The modular approach allows for flexibility within each and every stage of the journey, like being able to replace a small section of pipe rather than refitting an entire house’s plumbing.
Similar to a full customer experience automation strategy, customer engagement is not made up of one interaction, but many. Each of these moments requires a different action and a finite result, therefore the underlying modules that drive each interaction must be different. Tune back in next week, when I’ll dive a bit deeper into how this all looks for our customers in the implementation phase.
There is a school of thought that incumbent carriers, even those that are industry leaders, are at a disadvantage to be digital innovation leaders, because people with more time spent within the industry are less inclined to see the opportunities for change. Yet what I have learned from my own insurance journey, is that success and longevity in the industry requires change to be foundational in the company culture.
What if you could give every single customer from whom you needed sensitive documents a portal-like experience to upload their files securely and cost-effectively? Or a portal-like experience where they could check the status of a claim they had made against their insurance? These "Just-in-time" experiences are what Ushur calls "Invisible" and we are excited to share that we are launching Ushur Hub for general availability, today.
95% of IT leaders are worried that documents exchanged over email are a vector for bad actors to gain sensitive data. Documents can be sent to the wrong party, and it's unsafe to have key data sitting in an inbox to be forwarded accidentally with just a click. With a portal-like experience, IT leaders can rest assured their files and data are in the correct place, with the correct people. It also shortens turnaround and processing time.
A leader in the pension management business saw a 95-99% reduction in processing and turnaround time for pension file contribution management. That's due to the fact that they, like many other businesses, had previously used email to receive updates for pension contributions via static files. Those files would then have to be received, confirmed, and then manually evaluated and submitted to core systems to make updates to each employee's contribution plan.
A process with that much intervention is fickle and error-prone. By comparison, the resulting process with Ushur Hub received the employee contribution data, confirmed receipt to the sender, persisted contribution files to a core system, and provided a historical view of their interactions with the enterprise.
Client portals are not a new idea in the tech world, but Ushur Hub offers an easy way to build and deploy portal-like experiences that exclude it from the long drawn out development processes for IT teams. With drag and drop automation abilities and no-code configuration of portal experiences, the development and deployment costs of portals shrink significantly. No-code capabilities disrupt the world of IT-driven portal creation because it doesn't depend on traditional software programming paradigms.
As a result of this disruption, any business partner or key stakeholder becomes worthy of their own portal. Automation experiences that didn't seem worth the time and investment, are now tractable with Ushur Hub.
Secure document exchanges, managed file transfers, or bespoke client portal software are usually the solutions that sit in front of sites created and managed by IT teams. They're often just secure file transfer protocol sites (SFTP) sites. That just means they are file directories on a server that have been approved and whitelisted for receiving files or key information. They can feel like nameless and faceless vehicles for transferring files. Ushur Hub by comparison helps users select the end destination with ease and confidence, and without requiring business users to learn technical commands for moving files around.
When users send their pension management vendor an email with a document attached, they depend on the employees within the vendor to send them back a confirmation email. Additionally, the receiving employee has to process that file by hand - meaning correcting information and uploading the file to a core system; or even worse sending back an email asking for missing information.
When a user instead submits their monthly file via Ushur Hub, they instantly get feedback whether the file was uploaded or not. They can see historical tracking information for previous files submitted. And if the pension management team has also implemented Intelligent Document Automation, they receive immediate feedback on what data is missing or is incorrect in the file they submitted!
Originally, Ushur Hub was born of the need to securely handle a monthly file submission from an employer with data on the current status of pension contributions. If that doesn't mean anything to you, you can imagine a columnar file where each row represents an employee and the monetary value that their employer was contributing to their pension fund.
It's the same principle for defined contribution plans (like 401k's) where employees elect how much money they want to defer pre-tax to their retirement account. If it's a percentage of their income, and they were recently promoted, that value they contribute will change. That contribution amount could even change every couple of weeks! But whenever it does, there is an employee responsible for taking the employee roster from an employer and persisting the values into a core system. That could mean that someone is receiving an email as often as every couple of weeks which they need to manually evaluate and from which they extract key information to persist.
Any document which is regularly submitted and contains private data is one that businesses don't want to sit in an employee's inbox. It would feel like overkill to build a portal for each and every one of those cases, until you realize how easy it is to build, deploy, and maintain these portal experiences. It's not just a game-changer for business-users, it's a life saver for IT and security teams.
Finally, in any automation where files submitted would benefit from the ability for submitters to view and track the status of their submission, Ushur Hub can provide peace of mind to both originally submitting party and recipient.
Ushur Hub is the next product to change customer experiences, but the Ushur Customer Experience Automation (CXA) platform has included major capabilities for the platform included in this release - both advanced analytics and no-code integrations.
No-code integrations in the Ushur platform means that enterprises can easily let business users, who have been appropriately permissioned, add the ability to make real-time updates to their core systems of choice. With just a few clicks, IT administrators can provision the first connection and then business users can see all the relevant fields to update in their workflow dropdowns. These integration procedures save business time and money by minimizing the dependency on systems integrators.
Finally, in any automation where files submitted would benefit from the ability for submitters to view and track the status of their submission, Ushur Hub can provide peace of mind to both originally submitting party and recipient.
Ushur is introducing advanced analytics to the Ushur Customer Experience Automation platform to make it easy to inspect, analyze, and optimize customer experience campaigns. There are two components we've added that are key for perfecting customer experience: Micro-analytics and Macro-analytics.
Micro-analytics provide the view of every minute interaction between a brand and their customer. In each initiated activity, an Ushur client can view how their customers are engaging with the deployed customer experience workflow: whether they've opened it, tried to complete it, and where they left off. By analyzing the initiated activities, brands can see whether there are problematic steps in the customer experience process.
Macro-analytics show the holistic view of a customer experience campaign and how the thousands of customers have interacted with the automation workflows designed by an enterprise. A business can see what percentage of their overall target group have completed key processes like updating their address, or reviewing their account information.
All together, the segmentation and predictive analytics capabilities included in the advanced analytics feature help businesses get closer to their customers than they ever have been before.
If you want to see how these products and features can help you build a customer experience you can be proud of, reach out to us at Ushur.com. We'll help you understand the gaps in your customer experience, and how an automation platform like Ushur helps brands get closer to their customers..
Otherwise, be sure to follow us on LinkedIn and stay tuned for more exciting announcements in the world of customer experience.
Intelligent Automation is the practice of using predictive machine learning technologies to automate a variety of business processes. Using intelligent automation to avoid human intervention, streamline decision-making, and offer scale in real-time can completely change operational efficiency and customer satisfaction. These technologies center around Artificial Intelligence (AI) but also Robotic Process Automation (RPA) and Business Process Management (BPM). They help business leaders design solutions that altogether result in Intelligent Automation solutions across numerous industries.
For examples of Intelligent Automation, the healthcare industry uses chatbots to send appointment reminders and confirm or reschedule. The automotive industry uses RPA technology to reduce risk to human factory workers and improve defect discovery. Finally, the insurance industry can eliminate manual rate calculations and streamline paperwork in claims processing.
Rather than being two distinct technology umbrellas, RPA is an application subset within Intelligent Automation. RPA focuses primarily on the automation of back-office tasks. RPA is used to automate repetitive items that offer little to no variation in how the task is performed. Like most all tools in the Intelligent Automation space, RPA solutions leverage a graphical user interface so that its users aren’t compelled to leverage a software development stack. Intelligent Automation can use Artificial Intelligence to tackle more complex tasks.
RPA is rules-based, adhering to set parameters, while Intelligent Automation can leverage machine learning algorithms to "learn" as it goes and become more efficient at a task. As a result, RPA has fewer and more specific applications than the more general category of Intelligent Automation.
Companies incorporating different types of Intelligent Automation are streamlining business processes to the benefit of both customers and employees. With greater efficiency comes several benefits.
Humans are great at many things, but machines are better at consistency in their real-time decisions and at data extraction. Intelligent Automation can improve overall accuracy and quality as a result of the consistency in the solution results. Also, Intelligent Automation can automatically document processes, uncover potential compliance violations, and provide a consistent approach to meeting standards. Most significantly, every interaction is filled with volumes of data and automation, or bot, can extract key data for further improved accuracy.
Customers expect the modern enterprise to respond quickly, provide value, and understand them deeply. Intelligent Automation with natural language processing (NLP) can answer customer service questions, provide direction for new product launches, and even offer personalized experiences based on customer activity.
Machines can take on repetitive tasks without ever growing tired or making careless mistakes. This allows an enterprise to improve performance while maintaining labor costs. People can do what they do best—thinking critically, creatively, and oriented towards problem-solving.
Intelligent automation also benefits from another level of inherent value in that it can integrate with core legacy systems. Building upon different systems, automation can complement existing digital technologies for several key uses. There are several intelligent process automation use cases. Here are some great examples.
Manual claims processing takes weeks of emailing and calling a client to resolve issues and obtain missing information—even in a digital world. It's error-prone and inaccurate data entry can delay resolution, creating a poor customer experience. Intelligent automation can streamline the claims process.
Intelligent automation reduces resolution time from weeks to hours on average. Tools like advanced conversational AI helps reach customers more quickly in their natural language and provide consistent documentation throughout the process. AI can also comb documentation from PDFs and other files, text conversations, and even images to complete information retrieval. It brings simplicity to otherwise difficult process mining. All this happens without extra labor from human teams or customers.
Forrester Research found that while email is by far the preferred way for customers to reach companies, nearly 14% of companies never respond—possibly due to the complexities of unstructured data. The rest rarely respond quickly enough to satisfy customer demand. Intelligent Automation works 24/7 to resolve email faster.
Intelligent Automation can leverage a combination of artificial intelligence capabilities to triage email, social, and even calls; no matter what method customers prefer, AI can respond and resolve instances or engage a customer service agent for more hands-on queries. It scans email content to route it to the correct team member and automates documentation by pulling data into a CRM.
As businesses grow and scale, support teams may struggle to maintain high accuracy in query resolution with an influx of requests. It's frustrating to feel that the number of support tickets only grows, and that true success is always a case away. Intelligent automation leverages AI like another team member—one that never sleeps, needs a break, or takes off work—to facilitate workflows and reduce the load on support staff.
For example, AI can perform email triage to route customer inquiries to the right person the first time. As a result, support staff can focus on high-touch, complex cases and reduce customer frustration. Also, staff can create, update, and modify tickets through a central repository monitored and driven by AI. At the end of the day, automated responses and updates lessen the volume of tickets and improve resolution times—an improved experience for both customers and internal teams.
IA solutions can improve efficiency in an organization without adding to team members' workload and support staff. Instead, it allows them to refocus on creating the best value they can for customers again and again.
Intelligent automation is a powerful tool that can accelerate progress and increase profitability. If you're ready to learn best practices and take advantage of the unique combination of machine learning and intelligent automation that Ushur's platform offers, talk to an expert today.
The world of self-service has truly exploded over the past decade, but I would argue that to date, there has not been a customer experience that demonstrates the value of automation more clearly and universally than the one we have at the ATM.
It might sound pedestrian, with promises of checkout-free grocery stores and fleets of self-driving vehicles on the horizon. But any organization that plans to make an investment in automation–which should be every organization–can learn a few important lessons from the ATM before they start.
The benefit of an ATM is painfully clear. Would you rather walk into a bank, wait in line and speak to a teller, or simply walk up to a machine? In this case, the human element is not additive, it’s a roadblock.
Every automation experience we at Ushur want to enable should be like walking up to an ATM, done on the customer’s time, at their pace, at their convenience. But a vast majority of financial services companies, insurance companies, and healthcare companies still make their customers come inside the bank.
But customer experience automation is all about meeting the customer where they live, and that’s where we can learn our second important lesson from the ATM. Some people still like to go inside the bank. Maybe it makes them feel more secure. Maybe they’re friendly with a specific teller and want to say "hi". Maybe they just miss the way things used to be done. Automation has to be connected and implemented in a way that works for those people, too. Each demographic has a slightly different preference in terms of how they conduct business, how they build relationships, and what they expect from a customer service experience.
It’s essential to meet the needs of one customer without abandoning another. A 70-year-old customer may have bought insurance at their agent’s corner office for most of their life, while a 30-year-old customer never even knew that was an option. Asking the former to give up their personal interaction for an automated phone call is as jarring as it would be to ask the latter to make a trip to the offices.
There is an opportunity to bring in a new architecture, a purpose-built platform that can solve customer experience automation that can meet every demographic through any channel. While there are different channels of interaction based on demographic preferences, there are certain workflows that can be achieved across all channels. This dramatically improves the user journey, lowers the overall cost, and creates delightful experiences for customers.
Voice is an important, but ultimately limited, channel for automation. Today, simple asks and requests that should be fulfilled quickly are not, and that creates friction. The interpersonal elements of most customer engagements are far better replaced by an app-like experience.
Take, for example, a disability benefits company. Their customers may be new moms on maternity, more likely to interact via SMS. Or they may be someone in their 60s recovering from surgery, more likely to call in. Both probably have documentation that needs to be submitted, but neither is very likely to own a fax machine. So the challenge becomes, how do we start the customer experience in two different places–phone and text–and end in the same place with the ability to digitally submit documents?
We call our approach to this an “invisible app”–an experience completely intuitive for anyone using it, all self-contained without the need to download anything. Those who call are tactfully transitioned to the Invisible App later in the engagement when it will be of value to them rather than an unfamiliar inconvenience.
Making new technology accessible to multiple demographics is perhaps the greatest and most fundamental challenge businesses face today. Modern customer interactions have to unfold in a way that meets customer expectations and engages them without friction. Unifying varied forms of contact and communication through a holistic approach to customer experience automation is a universal challenge. Our vision is to make the processes and interactions that drive customer engagement–like transferring funds, adding a beneficiary, or submitting a claim–as easy as a routine visit to your nearest ATM.
Call deflection is reducing the number of inbound calls that require human service agents (call centers, helpdesks) by offering alternative digital self-service channels. The primary goal of call deflection is to reduce the amount of time customers spend waiting for an answer to their question.
Typically, customers must wait on phone lines, but enterprises can instead increase opportunities for them to resolve their questions or challenges. When it's done well, it also reduces the load on a live agent on customer service teams.
How does deflection benefit the staff who otherwise would be tasked with answering those incoming calls? It requires just as much focus and concentration from a representative to respond to simple questions as it does to handle complex ones. Agents must context switch dozens of times per day, if not hundreds, and that mental effort takes a toll on their patience and resources.
Due to the high-touch nature of the calls into customer service, it's in everyone's best interest to only put calls through that can't be handled through any other form of customer support.
There is understandable controversy in guiding customers away from human service agents. It feels disingenuous to position it at for the consumers benefits when we all remember times when we’ve shouted "representative" to a poorly coded IVR solution that made us want to throw our phone out the window. For customer call deflection to benefit both a business and its customers, companies must also understand what call deflection is not intended to do, such as:
Call deflection is more than a voice recording from the customer support team over the phone or adding a chat function at the bottom of a website to represent a knowledge base. It's a proactive, considerate look at overall customer communication strategy, website design, customer service channels, and technology to guide customers to what they need.
Businesses need consider a multi-faceted approach to call deflection that provides value for customers at every single interaction, no matter how simple or complex. Here are a few unique ways to implement call deflection principles.
Providing customers a choice for when and how they interact with a company is a powerful feature, but especially if that choice is adaptive and personalized. For example, companies could use email triage to route customer service-related emails to the right person and department the first time. This could help prevent frustrated customers from feeling that they must call customer service to resolve issues after receiving no answer through digital channels.
Even better, customers can choose to take a different communication path once roadblocks appear. When a customer decides to call in with a question and they hear that the current wait time is one hour, if they're given the option of switching to text message, they can receive an instant answer. With the touch of a button, they're out of the call line and handling needs through an alternative service channel.
According to a report from Gartner's research, 70% of service interactions over the phone don't have to happen at all—artificial intelligence could resolve them before the need for a human agent. That should inform a digital transformation strategy.
Companies can display their phone number and other contact channels prominently on their website, but merely making the channels known won't solve customer questions. A company must also make critical information easily viewable and searchable. For example, imagine a customer is having an issue loading a certain feature in the company's app. With a little digging, the company discovers that this is a common issue with iOS users. The fix is a 30-second toggle in their phone's location settings.
It takes much longer than 30 seconds to call customer service, and many customers may simply uninstall the app. However, a prominent section on the website with a direct link to this exact fix and other common troubleshooting solutions prevents an unnecessary call center touch. The customer fixes their problem and feels seen.
Companies can organize these tips in video form, allow customers to read them, or even program chatbots to walk customers through particularly frustrating tasks. With a searchable database right on the website, customers don't have to spend a lot of time before they get their answer.
Chatbots never get tired and never sign off work. They can pull data from previous interactions to customize interactions and predict what customers might need. They offer a realistic way to scale communications and handle even those sudden large number of calls pop up.
As chatbots have evolved, they’ve become more realistic, and many customers may not even realize they aren't talking to a human. Chatbots can address customers in their native language thanks to multi-lingual programming. Customers have overcome their initial distrust of chatbots and now may prefer a live chat with a bot to humans thanks to lightning-fast response times 24/7.
Chatbots can handle most customer service inquiries but nowadays these self-service options are designed to tag human agents for complex, high-priority tasks. This blend of human and machine intervention improves call center performance without increasing the team or running the customer service team into the ground.
Some companies have created spaces where the community can support each other. For example, peer-to-peer support allows customers to receive answers to their questions and give their own solution to others. It deflects calls and helps build brand loyalty.
Anecdotal answers also provide companies with valuable information about customers' real pain points, common troubleshooting issues, and even new problems before they become more widespread. A community space offers real-time information, and making it searchable offers even more answers to help customers help themselves.
Along with help articles, troubleshooting FAQs, and yes, customer service agents and associated chatbots, customers can find and receive information about their specific issue more easily than ever. These methods preempt the call in the first place unless it's necessary.
So how can companies ensure their call deflection strategy offers real value for customers and not just cost savings for the company? With a handful of non-negotiables.
A key strategy in customer satisfaction when it comes to call deflection is a tactic of preemption with proactive information. Preempting the call is the best way to handle calls to a customer service center. If 70% of calls are unnecessary, imagine the vast majority of those are best handled by distributing key information at the right moment.
But if that can't happen, timing the right alternative is best. Suppose a customer can find an answer to their problem through the community board or the FAQ section, excellent. If they need more help than what's available on the website, having the choice to email, chat, or call is still the best option. It may only need a quick email to solve.
The early days of automation frustrated customers because they had no choice and the designs were brittle. Shouting frustrated commands at a machine that didn't understand and sent them in circles or a chatbot that couldn't process even simple communications—this is the worst nightmare of someone contacting a company.
Now, customers can choose their interactions through the channel that suits them best. They can find answers before having to call. They can abandon long wait times at the call center for more convenient chat options or even schedule callbacks when it's most convenient. The choice transforms call deflection from avoidance to personalization.
The early days of automation also failed to deliver results because call deflection strategies weren't fully fleshed out. Chatbots simply directed customers to the call center or very limited resources without nuance. Call deflection didn't consider what channels customers preferred to use, or were already attempting to use, so they went unnoticed. That’s a failing strategy for customer satisfaction through automation.
Instead, call deflection only works when each different channel offers a comparable and viable option to calling customer service. If customers can't resolve their issues through these alternatives, they won't bother, and companies risk poor feedback. When they are equally as effective as calling for resolving most issues, they can revolutionize customer service.
Call deflection can relieve customer service agents while ensuring that customers receive valuable assistance for their questions and challenges. It can free up agents to handle high-touch, complex challenges and ensure that companies can scale their customer service offerings without adding extra cost or labor. The right call deflection strategies will be a significant business differentiator, and help build strong customer relationships through personalization, efficiency, and convenience.
There’s an easy to imagine scenario across three customers all waiting on the phone for service agents, but with different outcomes.
A common thread across all three customers is that each is on their own specific journey, and the service representative is a catch-all for the business when automation is unreliable and inflexible. IVR Call deflection can revolutionize the way companies manage customer service using limited resources in the post-pandemic, remote work age, but only if they understand it’s true constraints and true potential.
Customers may want the choice to access a human service agent but may not always need one for simple needs. A subset of customers may prefer to avoid human agents altogether unless absolutely necessary. Call deflection helps both groups because it reduces wait times and relieves burden on contact centers.
Excited to leverage call deflection for your business? Request a demo today!
Why is customer satisfaction the most important metric to keep in mind when evaluating and designing automation solutions? To explore that question and more, Ushur invites Maureen Flemming, Program Vice President, Intelligent Process Automation at IDC to break down the current market for Customer Experience Automation.
Renowned for their ability to collect data and share valuable insights, IDC’s latest research has made it clear that enterprises this year are prioritizing modernization efforts to drive customer satisfaction (CSAT), and are focusing less on the internal automation that a customer never sees.
Customer satisfaction is now an essential guiding metric for automation. Enterprises have been investing in automation for back-office and front-end applications for years—yet customers continue to demand more digital and intuitive experiences. Some businesses approach this by adding more features to their mobile applications. Others have responded by implementing IVR systems. But what automation strategy truly yields the greatest benefit?
Solutions that focus on the customer and not the application have the greatest chance to turn into successful, scalable modernization initiatives. Businesses that provide effective self-service options see increases in customer satisfaction, as well as reductions in costs and operations strain. If a business only focuses on the internal processes that are easiest and fastest to automate, they will still have to divert time, money, and manpower to the pain points that cause friction in the customer journey and decrease satisfaction every day.
Ushur is very excited to host Maureen Flemming for this fireside chat on using AI and automation to “Focus on the Customer, not the Application'' to show how frictionless customer experiences and end-to-end automation go hand-in-hand. From conversations to core system updates, Customer Experience Automation (CXA) is changing the way businesses engage with their customers. CXA empowers enterprises to communicate both proactively and responsively while overlaying core systems with a no-code approach to adapt quickly to evolving consumer preferences. Implementing CXA future-proofs the enterprise strategy for every customer journey, modernizing communication across all channels. More significantly, there’s no rip and replace or coding involved.
If you are a business looking for a competitive edge in acquiring and retaining customers, this webinar with IDC is a must-attend. Don’t miss the chance to ask Intelligent Process Automation analyst, Maureen Fleming, burning questions about your automation strategy. Register here and see you there!
Intelligent Process Automation is the next evolution for automation technology, where robotic process automation (RPA), natural language processing (NLP), and artificial intelligence (AI) intersect and become something better.
If robotic process automation alone is taking rote tasks and using software to handle repetitive tasks, intelligent automation gives automation flows their own metadata and design.
It can be helpful to think of IPA as an executive function that improves the workings of all your digital process automation. That means that it can become flexible and more responsive to your human-powered workplace and customer base.
Basic tasks also are pre-built and easily reusable between flows so that each automation solution isn’t designed or built from scratch. For example, intelligent automation depends on capabilities like pre-trained machine learning models which can be disposed of without further development and model training.
At its best, IPA allows companies to design their automated processes to make decisions and “learn” from experience; change with input from past automation results.
IPA applies artificial intelligence, conversational AI, natural language processing, and a host of other “intelligent” tools to your existing automation to augment your entire process. These technologies are already being used in digital processes to improve sectors like insurance, banking, and healthcare.
For some time now, businesses have automated processes like payroll, invoicing, and data entry. Simple automation can save businesses hours of human labor and can also save them from inevitable human error. However, traditional automation also comes with severe limitations. Simple automation cannot easily handle workflow changes and isn’t flexible enough to respond to unexpected inputs. As a result, the automation breaks down whenever the underlying application changes or a customer interacts with it in some non-predetermined fashion. Traditional automation has also likely depended on programming languages that require expertise in a technical field.
What are some simple examples of brittle automation? Consider the automation flow that takes a document placed in a shared folder every week by staff, prepends it with the date, and then copies it to an SFTP site for the next person to review and process. That solution could be as light as a combination of bash scripting and Python to lighten the load for service staff, but is a lift for an IT team to build and maintain. Each time a person diverges from this straightforward process, IT and service teams would have to scramble to resolve the issue and add technical debt to their queue.
If your business relies on older automation designs, you've probably already experienced the unavoidable downtimes resulting from your IT team scrambling to rewrite and redesign “bots” so that your system can get up and running again.
Intelligence process automation doesn’t suffer the same limitations and consequences of brittle design.
IPA tools bring automation with a brain, and allow automation flows to learn from past mistakes, handle unstructured data, and collaborate with a human operator to keep customer experiences up and responsive. IPA can also take in new information and change course as needed, so that your automation keeps running even when circumstances change.
There are a number of important divergences between IPA and RPA. Inevitably, they get lumped together, which is why it’s worthwhile to tease out the differences and values.
RPA stands for robotic process automation. It’s an umbrella term that refers to the category of software solutions used to build and deploy robots that carry out digital back-office tasks.
Robots can make keystrokes and navigate online systems. They excel at predictable, repetitive tasks with a clear sequence of steps, which is why they are widely used in areas like data entry, invoicing, and payroll. Simple robots can be programmed to track employee hours and issue paychecks; they can also be programmed to calculate overtime pay and deductions.
Robots perform these menial tasks more quickly than most employees, and they do so without the volume of errors human intervention typically incurs. Automating menial processes can save businesses the cost of labor, while eliminating the cost of repairing errors in operations. Automation also frees up employees to focus their energy on more complex tasks. For these reasons, RPA technology is largely focused on improving operational efficiency within the enterprise, impacting the employee experience but less so the customer experience.
IPA takes process automation to the next level for various industries. IPA platforms apply artificial intelligence tools to manual business processes. Of course, each solution can look quite different depending on the use case; there is a broad array of tools that key components of IPA can bring to bear. Computer vision and natural language processing are just a few of the more common tools used by IPA to create a more flexible, and interactive automation journey.
Computer vision allows computer programs to “read” and “understand” complex visual inputs, so that they can collect data from video and still photos even when that information is not clearly labeled. Inputs such as photos of damaged property, personal identification, and scans of standard forms are good examples. Computer vision can be used for insurance inspection and claims settlement, among other things.
Similarly, natural language processing tools, or NLP, allow computers to communicate in human languages; written or spoken. NLP-enabled programs can read and respond to texts that are written the way people really speak. Chatbots are powered by NLP; so are search engines.
IPA can also apply machine learning tools to the automated processes themselves. Machine learning, or ML, allows the software to identify patterns in large data sets and is the product of many data scientists expertise. Eventually, ML-enabled programs can make predictions based on those patterns. This allows them to course-correct and to keep improving their own functioning by, essentially, learning from experience.
While Robotics Process Automation improves operational efficiency for employees, Intelligent Process Automation leverages Artificial Intelligence to self-service more sophisticated customer intents. To make an impact on your customer experience, you'll need an Intelligent Process Automation solution that understands the problems and jargon unique to your industry and those that it serves.
RPA and IPA have fundamentally different focuses: RPA is focused on a process, while IPA is profoundly data-focused. You don't need to have RPA tools in order to implement IPA. But if you are already using RPA, then Intelligent Process Automation platforms can complement your existing end business processes.
For example, RPA excels at capturing and storing data. IPA can take this process a few steps further by applying NLP and mining written texts for data to drive scale. IPA can use NLP to “read” emails from business partners and customers and extract all the relevant data so that it can be stored and later studied; thereby injecting intelligence into an automated process.
What kind of data are we talking about? It could be anything from sales data and transactional data to contractual information. When you use intelligent automations in your workflows, human employees no longer have to sift through an endless email inbox looking for actionable information.
IPA can be used for digital transformations in virtually any field and certainly different industries. Here’s how a few key sectors are already using this technology.
Insurance enterprises rely heavily on communication with customers who reach out to their service centers regularly, and all with a unique language and tone. The high volume of communication and varied intent can be a problem for brittle automation flows dependent on human actions. That’s why any tool that is flexible and intelligent enough to improve communication on a high level is a game-changer for people in the field.
Ushur’s insurance claims process automation slashes the time needed to process claims regardless of whatever data format they originate in. An insurance enterprise can use Ushur’s intelligent process automation so customers can submit claim details over any channel and receive proactive status updates as they arise and are generated by backend systems. And, with fewer delays and less friction in each engagement, customers report a far greater level of engagement and satisfactory outcomes.
The banking and financial services sector is rapidly growing its online presence, and customers expect to carry out a broad range of activities via their devices. The sector also still sees inordinate operational costs in their customer service centers despite heavy investments in other existing automation technology which leaves gaps in their overall customer experience. Intelligent automation is changing what’s possible with customer experience in banking.
Ushur is using IPA and conversational AI to facilitate smooth conversations with both existing customers and prospective customers. Conversational AI means that chatbots can understand customer queries and interact directly with backend systems to resolve their issue.
At the same time, Ushur’s solution allows early adopters to capture data from conversations with clients, and learn steadily more from each experience. As a result, every customer interaction becomes a growth opportunity, in which the chatbot learns how to improve its services. The more data the bank can capture, the better it can serve its customers via use of analytics.
Effective communication is a vital part of the healthcare sector, and IPA solutions make it easier for healthcare facilities to listen to patients and learn from their concerns while safely and responsibly collecting and storing data in a format humans understand.
But Ushur doesn't stop at simply collecting information. The system builds on the intent captured through Conversational AI by automating routine tasks across backend and legacy systems. The result is smoother workflows and greater customer satisfaction through the use of complementary technology and use of AI.
Ushur is working closely with clients in the insurance industry to implement IPA solutions so that customers can be better served at all times. Ushur’s Language Intelligence Services Architecture (LISA) is a framework for services that serve Natural Language Processing, Natural Language Understanding, Sentiment Analysis, Topic Detection, and Metadata Mapping to understand and respond intelligently to customer conversations.
LISA can understand human speech and respond in kind. The system can handle a variety of customer queries and perform triage, passing customers along to a human agent whenever necessary. Using LISA means that customers get their issues resolved quickly, while agents are freed up to focus on the customer relationship and make important decisions.
IPA has a very broad range of applications, extending well beyond what we’ve outlined in this post. At Ushur, we believe that every business -- in every field -- can transform their customer experience through automated business processes that eliminate the friction and risk from every customer engagement.
Curious about what IPA and associated new technologies can do for your business? Get in touch today and start a conversation about how Ushur can help you maximize your company’s potential.
When people think of automation, and in particular the ROI of automation, they typically think that the return part of that equation is driven primarily by savings realized by cutting costs or faster processing times.
But really there is so much more. Cutting costs is only part of the story.
What people often miss when evaluating automation is its ability to drive customer loyalty. And what is overlooked completely is its impact on employee engagement. The true ROI of automation lies in creating an environment where employees are enabled to think outside the box to better meet customers’ wants and needs. What Customer Experience Automation™ truly delivers is satisfied customers who are more likely to remain loyal and employees who are more satisfied with their job and, therefore, less likely to leave.
Early in my career, I had the opportunity to manage a 250-seat contact center for a big telecom company. We had all the modern tools, including a brand new IVR and phone system, and the best trained professionals for our team.
But we still had a big problem. Despite paying very competitive hourly wages, we had high (voluntary) employee turnover.
44% to be precise.
Out of 250 agents, 110 chose to leave within a year.
We recruited terrific people who genuinely wanted to help our customers. But in exit-interview after exit-interview, we heard the same reasons for leaving; high call volume and rote, routine call types that led to frustrated customers and exasperated staff.
It had me wondering: what if organizations could provide ways to address customer issues and questions (both complex and simple) easily, even proactively?
What if we could reserve the interactions between front-line staff and customers to focus on high-value and truly service-oriented work? What if we could let our employees support customers and just automate the rest?
Fast forward to the present where I currently serve as VP of Product and Operations at Ushur, offering automation solutions that reduce costs and speed up processes but, more importantly, also improve customer and employee experiences. And what I’ve seen in practice is that the employee experience reflects and affects your customer experience.
When people think of automation in organizations, they often think it only applies to back-office work. Technologies like robotic processing automation (RPA) are commonly used in the accounting or finance departments to simplify the automated copy/paste of routine tasks. Those tasks could be anything: like processing invoice information, such as payee and amount, to establishing due date and moving into a separate payment system. However, these are just the better-known use cases and benefits. They are not the only ones.
Actually, the ROI of intelligent automation, which goes way beyond RPA’s capabilities, is not just doing administrative work faster. With Customer Experience Automation™, you are delighting your customer because you meet their needs faster and you free up your people so that they aren’t burdened with the routine work that can be automated effectively.
Given my background in running contact centers, I not only value the ability to automate work that happens within the walls of big companies, but also the ability to automate the interaction between enterprises and their customers. I like to tell people that at Ushur we automate the communication between our customers and their customers. This is the next frontier for enterprises which can unlock huge value.
Customer Experience Automation™ (CXA) isn’t just leveraging technology for technology’s sake. It’s not just inserting technology into back office processes. CXA leverages technology to create better experiences for your employees, promote their day-to-day work, and as a result, it creates better experiences for your customers. Employees have less repetitive work, are able to get things done faster, and can convey their job satisfaction by delighting your customers.
Some people are passionate about providing the best customer service T, and by minimizing their routine work, they don’t have to compromise on their passions. While any large organization faces problems scaling their processes to address claims, sales, or customer service issues, automation means the people you choose for service-oriented work aren’t the ones who need to struggle. As a consequence, you don’t have to compromise in choosing your talent from the broader talent pool. You can eliminate churn caused by the dissatisfaction of employees’ time being consumed by repetitive and manual tasks. You can encourage service-oriented employees to do what they do best. You can boost end-to-end satisfaction for all parties.
By automating basic calls for information, you give your employees time to focus on the personal touch of working with customers who need the time and attention. When as much as 42% of your calls are automated, your employee experience improves. And satisfied, enabled employees provide excellent customer care.
Let’s face it, automating repetitive processes and straightforward tasks frees valuable employees from manual roles. It gives the people who know the most about your product or service an opportunity to connect meaningfully with your customer.
Of course, this doesn’t mean removing that human touch from the process. Just the opposite-- automation lets you build and retain a better, more fulfilled talent force. It gives your people a chance to shine with the customer without bogging them down.2
One customer we worked with started using automation in their sales department with great results, and yet their best results happened when they automated their information gathering process for disability claims.
Originally, they were calling members over and over trying to get information. As the case study describes, by automating the process, not only was the client able to get the information they needed in minutes instead of days or weeks, but their customers also responded positively and requested more of the same type of automation in different areas of information gathering.
And the smaller percentage of customers who needed to speak to an actual human were able to receive the time and attention to answer their questions and improve their customer experience, too.
Dissatisfied, unhappy employees are more likely to leave. As I mentioned before, 44% of our call center employees left because of high call volumes and routine work that took away from their ability to help customers.
I can say without a doubt from personal experience, automation can help reduce these types of tasks and studies show that when you reduce these tasks, employee satisfaction grows.
Customer engagement automation decreases the repetitive work for front line employees and frees them to focus on more complex activities and person-to-person interactions.3 When your employees feel like they are working towards a higher purpose, they are more satisfied.4 And according to The Smart Guide to Conversational AI for Insurers, employees who spend more time solving meaningful problems for customers and less time completing routine tasks are happier employees.
According to a McKinsey study, 60% of employees who are extremely satisfied with their job and company intend to remain at their job.5 And those are the employees who are experienced and happy to delight customers with their service and care.
Customer experiences for your front-line employees aren’t going to get simpler. But retaining a workforce that is not only knowledgeable but also happy to be there will only please your customers and give your company a better reputation.
The insurance policy is a contractual agreement that stipulates, in exchange for payment, a person or business will receive reimbursement or financial protection against losses from an insurance company. In other words, if the required premium is paid, a customer will be paid for covered damage, injury, illness or loss.
The insurance claim is the formal request for coverage for a loss or event.
For as many kinds of insurance that exist (from workers compensation to travel to life), there is the potential of there being a corresponding type of claim. And while the details of a claim for short-term disability benefits and a claim for auto damage following an accident are different, you might be surprised to hear that the overall process flow is similar.
When thinking about customer interactions and processes, we find it helpful to first think about claims as either those that physically affect people (i.e., injury, illness or end of life), those that impact physical property (e.g. vehicles, buildings, equipment) or those that involve service (e.g. travel, events).
For any claim, there is a claim intake process, where the customer makes their formal request for coverage under their policy. Insurance carriers have different terms for this step, including First Notice of Loss (FNOL), or Filing A Claim. Sometimes a claim will be reported by someone other than the policyholder – for example, the owner of a car that was damaged in an accident where they believe the policyholder is responsible.
Policy verification involves confirming that there is a policy in effect that matches the facts within the claim. That the customer has a policy in effect on the date of event or loss is key. Other elements of policy verification will vary based on the type of claim. For example, for a short-term disability claim against a group benefits policy, the injured party needs to be verified as being a member of the group. For a personal auto claim, the car needs to match what is shown on the policy.
Claim assignment needs to happen both as quickly and accurately as possible. Claims adjusters may specialize not only on a specific line of business (say, workers compensation) but may even be focused on specific industries or jurisdictions. Business customers may have dedicated claims handling teams. Sometimes claim specialist assignments are based on reported loss severity. Regardless of the claim assignment rules in play, the insurance company wants every claim to get into the right hands to provide the best and right-fit service immediately.
During the claim investigation the claims adjuster obtains the facts needed to confirm coverage – that the claim is in fact covered under the terms of the policy. Policies have exclusions that limit what is covered. This can be to prevent overlaps between types of insurance, like homeowners and flood or workers compensation and disability. The claims adjuster also confirms the cause and extent of the damage or injury, where there may be applicable terms in the policy. This could involve obtaining a report from a doctor or obtaining a repair estimate from a contractor. The claims adjuster looks for other responsible parties, like the manufacturer of a defective part that caused a machine to malfunction, should there be a reimbursement claim to pursue, referred to as subrogation.
Some claims will have a longer duration, including significant building losses or injuries. During the claim management process, the claims adjuster is in regular contact with the customer or injured employee. For disability or workers compensation claims, the adjuster monitors medical treatment and return to work readiness. For a larger property loss, the adjuster is following the progress of repairs. And in all instances, the claims adjuster is ensuring any payments due are being made timely.
Claims eventually reach the point of claim resolution, where all payments have been issued. At that point the claim file is closed or moved into a recovery process if there is subrogation potential.
The claim journey is highly interactive, from intake through resolution. As outlined above, most claims follow a similar flow of information received, processed and requested. This creates many opportunities to leverage AI to automate and accelerate these anticipated touchpoints. Each discreet customer, agent, broker, member, claimant and even provider interaction that is automated, can simultaneously improve customer experience, operational efficiency and employee engagement.
We offer just a few transformation possibilities here with the hope they will inspire you to reflect on your own high value opportunities.
The claim intake process sometimes begins with a customer or their agent emailing a form to their insurance company. An integrated AI platform that includes machine learning and email automation can rapidly recognize new claim forms as they land, extracting the data and moving it into the core claims system in minutes. This eliminates hours – if not days – of delays in policy verification and claim assignment, and in turn, customer service. Absent that automation, carriers have to deploy vital employee resources to monitor email boxes to rekey each new claim, which can hinder the claims adjuster’s ability to manage each claim to its best outcome.
During the claim intake process, AI can anticipate – and ask for - the information that will be needed from the customer as part of the claim investigation. This could include facilitating the e-sign of a medical authorization and introducing them to in-network medical providers specializing in their type of injury. Or AI can enable them to schedule an appointment at a preferred repair facility nearby. A health plan’s need to coordinate benefits before being able to pay a claim can be easily addressed by a member uploading a photo of another secondary plan card.
Throughout the claim investigation, management and resolution phases, AI empowers carriers to be proactive, automating the answering of customer questions before they are even asked. Claim status is a high-volume driver of inbound customer calls and emails for most carriers. The customer, member or claimant experience is transformed when they automatically receive regular updates, from the progress of their appraisal, to the timing of their next benefit payment or confirmation their medical bill has been paid.
Insurance companies can also leverage AI to automate reaching out when information is needed, be it asking the customer for the location of a vehicle to be appraised, checking in with a member for the timing of their next doctor’s appointment or confirming the desired payment process for a life insurance claim.
Insurance carriers formed data analytics teams several years ago, to tap into the power of decades of loss data using modern desktop business intelligence tools. This enabled carriers to gain new insights, including patterns of claim outcomes. Predictive models followed, where insurance companies used historical trends to predict new claim outcomes. This not only improved the accuracy of loss reserves (think claim payout budget), but it also helped carriers to see early indicators of initially simple claims that historically became complex. However, it wasn’t an integrated process – claims handlers were provided lists of claims where intervention was needed.
Machine learning within an integrated AI platform enables carriers to insert the power of data analytics into the claim process to automate both predictions and decisions. Utilizing historical claim data, ML can find the early indicators of fraud and automatically notify a Special Investigation Unit. Injuries and illnesses that have a strong likelihood of worsening could be routed to a nurse to arrange a medical second opinion. Losses identified as high contenders for future litigation might be elevated for manager review and reserves adjustment. These are just a few examples of proactive automation that can both effectively and efficiently improve claim outcomes.
Insurance customers are also consumers whose automation expectations have been heavily influenced by digital leaders like Amazon, Netflix and Facebook. Gartner’s research indicates customers expect to manage 85% of their relationships with businesses without interacting with a human. According to Microsoft, most customers of any age will select a self-service channel first.
Integrated conversational AI platforms enable carriers to meet their customers where they are, in their digital channel of choice or to connect them to the right person. This not only positions carriers to offer the self-service options customers expect. It also enables carriers to automate high volume customer outreach, including differentiating touchpoints beyond the traditional policy, billing and claims process.
No-code platforms put creating new workflow automations in the hands of business analysts. These “citizen developers” position carriers to continuously introduce new solutions to respond to an increasingly digital world.
Connected devices including wearables are already used by almost 40% of insurance customers today and more than 70% expect to be users in the future, according to Bain & Company. McKinsey estimates there will be up to one trillion connected devices by 2025. Connected devices create customer value in preventing claims and can offer an insurance carrier valuable data for underwriting. A surprising benefit identified by Bain & Company is that connected devices drive customer loyalty, increasing customer-carrier interactions five-fold, in an industry where 1/3 of consumers have no interaction with their insurance company or health plan throughout the year.
Robotics will continue to permeate our everyday lives. There will be more use of 3-D printing in manufacturing and construction. Not only will more cars gain autonomous capabilities, like self-driving. Similar growth will occur with farm equipment, programmable drones and surgical equipment. These waves of change will require insurance products to keep pace, including designing different ways to understand and price new risks. Claims handling will require new capabilities and resources to appraise damage, determine coverage and liability. When a car is involved in an accident while operating in full-autonomous mode, a software engineer may need to be on call to evaluate computer code to determine responsibility.
It’s understandable that the focus is on capabilities when evaluating an automation vendor - you need to know first and foremost if their product can truly meet your organization’s needs. The conversation usually pivots to cost - vendor software licensing, potential professional services needed, in addition to talent allocation from your own company.
What should also be occurring in parallel with learning about features and pricing is carefully evaluating how a solution provider would protect and secure one of your organization’s greatest assets - your data.
If an information security assessment is never completed, the reality is the risk of a major data breach that will damage your business and reputation is only a matter of when not if. When this evaluation is only initiated as the last step before signing a contract, you’ve lost - if not wasted - time.
Either you go into a holding pattern, as your data security team works with the solution provider to complete the assessment, and you’re not able to yet launch your important automation project.
Or the worst outcome becomes your reality if the vendor solution is found to not meet your company’s information security requirements. Now you’re back at square one to look for other automation options. You’re late even before you can get to solving business problems, improving the customer experience, and eliminating manual tasks for your employees.
In Part 1 of our Information Security (InfoSec) blog series, we introduced the idea of a vendor embracing a proactive information security approach as being mission-critical to protecting your - and your customers’ - enterprise data. Here in Part 2, we will discuss 6 important considerations for delivering the two key components of information security - cybersecurity and operational security, including Ushur’s recommendations for combating the associated challenges.
Cybersecurity focuses on protecting networks and systems from digital attacks. This is an increasing threat not only to businesses but also governments and our personal lives. Not a week goes by where we don’t read about a data breach at a company or a ransomware attack on a community. And we are all probably receiving phishing emails daily at home, as criminals try to access our personal data.
Operational security consists of the procedures and protocols used to maintain and enhance cybersecurity. If cybersecurity is the fortress, operational security is the camera system monitoring the perimeter to ensure all gates remain locked.
Be proactive and be informed. Ask questions as you explore new vendor partnerships. Be sure your business and technology teams are partnering early in the exploration process, to complete a 360 degree assessment as quickly and effectively as possible. Information security should be a shared responsibility for everyone in your organization.These cybersecurity and operational security best practices will help you safeguard your data.
To learn about how Ushur uses a proactive and end-to-end security approach to protect clients and arm them with confidence, check out our Information Security Whitepaper, where we discuss our security practices and protocols in-depth.
In the first of this three-part series, we explore what Information Security (InfoSec) is, what it isn’t, and which questions will help enterprise customers discern responsible intelligent automation vendors from those susceptible to data piracy.
That’s a motto that savvy, Cloud-based companies live by. The cost of known data breaches since 2012 far exceeds $50 billion, according to one study by CGI and Oxford Economics.
As businesses, solutions, and customers rapidly migrate to the Cloud, the sheer number of expert-designed, collaborative platforms is a boon for innovation and new growth, and businesses that have traditionally operated in siloed, brick and mortar facilities stand to gain the most from this bonanza.
For a business that’s evaluating a cutting-edge technology, how a solution provider protects and secures information needs to be answered transparently, coherently, and completely. Otherwise, the risk of a major data breach is only a matter of when, not if.
Of equal importance is a company’s ability to ensure privacy. Given strict and diverse regulatory frameworks, it’s imperative for a solution provider to demonstrate future-proofed and comprehensive privacy protocols.
With the emergence of the Cloud, the narrative emphasis has been on cybersecurity, but secured data demands a turnkey, end-to-end approach.
To separate the wheat from the chaff, here are three questions that any vendor who is serious about InfoSec should be able to clearly answer:
Ushur, for example, is committed to addressing each of these questions in its Information Security plan, thoroughly framing our security and privacy design. Our complete solutions for intelligent automation attract and retain Global 2000 enterprises because we innately understand security and reflect that in our people, processes, and technology.
A turnkey security and privacy plan seamlessly integrates proactive, adaptive, and reactive security models. In Parts 2 and 3 of this series, we will discuss both the challenges of delivering security and what a comprehensive strategy looks like, analyzing the following topics:
On August 26th, Ushur, an AI-powered automation platform, hosted its inaugural Innovation Awards ceremony, where individuals and companies were honored for their leadership in driving digital transformation initiatives in the insurance industry. Three awards went to two individuals and one company, respectively, who are “building a more compassionate world through their exemplary development and deployment of innovative AI solutions,” according to Simha Sadasiva, CEO of Ushur.
Unum, the Fortune 500 global disability, life, and financial protection insurance behemoth,won the Digital Transformation Innovator award. The individual awardees were Ken Lynch, Head of Information Services at Irish Life, and Ted Reed, who spearheaded business development at Unum Group prior to his retirement.
The event, which took place virtually at InsurTech Selection Day, was also a testament to Plug and Play Tech Center, an innovation platform that pairs large global corporations with vetted startups to foster relationships and improve corporate nimbleness while granting young companies access to institutional resources. According to Saeed Amidi, Plug and Play CEO, successes like Ushur, a Plug and Play portfolio company, “are a real pride and joy for me. [...] We pride ourselves on finding great entrepreneurs, incredible technologies and ideas, and then making a positive impact on their journeys.” Amidi and Simha opened the ceremony with a discussion of the companies’ close relationship, the opportunities to expand digital transformation to new verticals, and the need for agility as a startup working with enterprise clients.
The awards were especially significant in the context of the Covid-19 pandemic that has tested the ability of insurers to offer their policyholders consistent and quality service. While the honorees have been pushing digital innovation in career-long strides, their work has noticeably buttressed their organizations and stakeholders during the crisis that has seen rapidly rising and sustained policy-holder demand, highly remote workforces, and the confluence of urgent, seemingly unrelated disasters.
Simha acknowledged the unusual new pressures felt by the insurance industry.
The winner of the Digital Transformation Innovator, Unum has been notable for its aggressive push towards conversational AI and workflow automations as a vector for new growth, modernization and better service. The desire for operational efficiency and enhanced member experience has been especially critical to Unum as it serves a diverse, global customer base.
Marco Forato, Unum’s SVP of Corporate Strategy and Development, underscored this idea. Given the nature of Unum’s products, potential customer touchpoints may be periodic, so it is incumbent on agent teams to deliver exceptional interactions.
As Simha pointed out, company-wide change at large enterprises is often a difficult, slow task, stating that “even at a startup like Ushur, every workplace tool we adopt - let’s just say adjustment is a process! So for a large enterprise to make strides in a new space can only be orders of magnitude more daunting.”
With a culture that rewards experimentation and leaning-in, Unum is a case study for large, deeply established enterprises on how they can nimbly pivot to keep would-be competitors at bay, while giving policyholders more flexibility and opportunity. According to Forato, the company has seen customer satisfaction metrics rise dramatically, and the success of the partnership has led to more opportunities for collaboration with Ushur.
Leveraging low-code solutions, Unum has pioneered empowering business teams to join the ‘citizen developer’ movement (read more). “We started with a small pilot a few years ago and now have over five applications, with more in the pipeline. [Ushur’s] ability to rapidly prototype and automate customer journeys has provided us with a competitive edge, delighting our customers while allowing our employees to focus on higher-value work,” Forato added in an email interview.
Simha highlighted that Unum was committed to working with startups despite the perceived difficulties of doing so. Forato agreed, praising the Ushur partnership and calling the company “a true partner in our digital transformation.”
Speaking from a sailboat off the Northeast coast, Reed recounted working with Ushur as a capstone to a long and impactful career driving change. “We began with a very small seed use case that we were able to turn around in literally less than a week [...] it evolved to many other solutions for intelligent automation opportunities [...] What this has taken on both parties’ parts is really good listening, the ability to see latent opportunities. [...]"
Ken Lynch won the AI Pioneer Award for his vision to apply “the power of AI to create operational excellence and drive an unparalleled customer experience at Irish Life.” Lynch, who heads information services for Irish Life Corporate Business, Ireland’s largest benefits company, was key in bringing AI and agile methodologies to streamline and automate many of the manual processes that had created administrative hassle and a burden on Irish Life’s business teams (Read the Case Study). As the need to address member concerns in real-time grew clearer, Lynch invested in vendors and partners who would create technologies to drive down response times and improve service. Lynch has worked with Ushur for a number of years and lauded the success of the partnership.
During his acceptance speech, Lynch recounted meeting Simha at a Plug and Play event. “At Irish Life, we started to look at conversational AI technology several years ago with the aim of both improving customer experience and delivering operational efficiencies.” After a 10-minute pitch from Simha on low-code and no-code AI, Lynch told Simha that he was not completely sold on Ushur’s ability to deliver and could not believe the Ushur platform could perform as Simha had described.
Now, he has no doubts. “Having built the partnership over the last year, we’re now interpreting, indexing, and routing all incoming email without human intervention. This means quicker service for our customers and frees up our highly trained staff to focus on strategic initiatives."
If the theme of the ceremony were a celebration of technical excellence and implementation, the throughline for the winners was relationship-building. Said Simha, “Ushur could have the coolest technology, [...] but it took smart risk-takers with an appetite for discovery to get us here.[...] Aligning technologies is one thing, but selecting individuals with the same temperament and commitment to excellence is challenging, and we are lucky.”
Picture two rival health insurance providers with similar policies. There’s only one difference between them. When open enrollment season comes around, the first trots out the same old enrollment game plan: recruitment teams making cold calls, educational events, a website for prospects to register interest, and a limited post-enrollment plan. The other embraces and deploys new AI-powered software to automate outreach, appointment reminders and onboarding on an enterprise-scale, with scalable customer retention capabilities baked in.
At the traditional firm, agent teams spend most of their time on administrative tasks and chasing prospective customers. Everything is a struggle, even getting prospects to show up for calls they scheduled.
At the firm using insurance sales automation, it’s a whole different story. Agents aren’t wasting time touching base, sending reminders, doing data entry. They’re doing what they do best: seeing the big picture and signing up new members. In fact, they’re:
Improving close rates by
Cutting quote processing times by
Those aren’t fictional numbers. Insurance agency automation systems are the real deal--light-years more advanced than the barebones expert systems of yesteryear. They’re highly customizable AI-powered workflow automation platforms that integrate with a firm’s other customer systems (e.g. CRM, service desk) while maintaining security and compliance.
Today, we’ll walk through five insurance automation ideas for getting an insurance prospecting system—fully equipped to handle the pandemonium of open enrollment season—up and running in less than a month.
We recommend these five insurance automation ideas to start because most focus on mobile engagement. Your prospects are already on their phones. Meet them there! You’ll see better engagement rates using the channels your customers prefer.
Additionally, cloud-based insurance sales automation platforms (like Ushur) are driven by quick development cycles. Automation is no longer an arduous, multiyear, big budget initiative. Born-in-the-cloud companies pass that iterative, fast-moving advantage back to you.
We know you can’t sell health care over SMS. Hear us out.
Due to heavy government regulation and the difficulty many health insurance prospects have comparing plans online, most firms still reach out to prospects over the phone.
But customers much prefer the convenience of digital channels, and your firm can use SMS automation as a supporting, auxiliary medium. For example, it can send health insurance prospects SMS reminders about an upcoming call—and give them the option to reschedule if needed—to improve appointment attendance.
This relatively simple form of automation can improve results across a firm’s prospects because it reaches even those without smartphones. For those who do have smartphones, the opportunities for engagement are even more diverse and media-rich.
Your prospects might want to engage with you digitally, on their phones, but no one wants to install another app. Not convinced? One insurer we work with saw a download rate of just 2% of their mobile app among members and a usage rate of 3%. Low/no-code virtual agents are the perfect solution here.
When we say “virtual agent” we’re referring to a conversational interface accessed through a mobile device. Basically, prospects engage with a virtual customer service agent in an app-like experience.
A firm might use virtual agents to send health insurance prospects a pre-appointment checklist reminding them of everything they’ll need for the call (social security card, current set of prescriptions, doctor’s contact info, etc).
The trick with virtual agents is getting them to do what they need to. That’s where coding comes in. Normally virtual agents need to be programmed by someone who knows their Java from their Python. But a new breed of low- and no-code platforms have ushered in the era of the “citizen developer,” allowing business teams without any formal coding knowledge to construct sophisticated, AI-powered workflows.
Using these low/no-code virtual agents, prospects can schedule calls, browse plans and check the status of a quote—all tasks that previously agents would have needed to handle. And since these prospecting platforms work just as well on mobile devices as they do on desktop, customers can take a picture of a signed form and seamlessly upload it to their application.
Because the low-code UI is designed for agents, not engineers, firms can spin up open enrollment “special agents” in a matter of days, without waiting on IT.
Choosing a healthcare plan can be stressful and confusing, and people under stress ask a lot of questions. It’s human nature. But for your agents, fielding all those informational calls leaves little time for anything else. That’s why it’s important to give prospects a legitimate self service channel so they get their questions answered.
AI-powered chatbots for health insurance can drastically reduce the volume of inbound requests by automatically answering simple questions that would otherwise flood your call center. Conversational AI enables these types of health insurance chatbots to answer even the more complicated questions, such as queries comparing different plans.
Health insurance chatbots also excel with outbound communications. For example, if the customer’s behavior indicates they might need support (perhaps they’ve left parts of the application blank), the chatbot can proactively reach out to the customer and retrieve the missing information or guide them through the next steps.
Of course, not every inbound request can be answered by a bot. It’s worth mentioning here that some chatbots for health insurance have highly evolved AI sentiment analysis, which can automatically and seamlessly bring in a human agent if it detects the customer is getting frustrated.
Many requests for quotes come in via email from brokers. The more brokers you work with, and the more clients those brokers have, the more data you’ll need to process—data that often arrives in disparate formats and various messages.
If providing manual quotes is a huge drain on specialists' time and resources, a powerful email automation system that provides data entry automation and document processing should be high on your wishlist. An email-to-quote automation tool does exactly what it sounds like: the software scans incoming emails from brokers, extracts critical quote data from the body of the text AND attached documents and populates directly into quoting systems. With email-to-quote, you can process customer information, and thus turn around a quote, much much faster.
Time is money when your organization is trying to hit 70% of its yearly sales targets in Q4. The longer it takes to provide a quote, the greater the chance of losing out to a nimble competitor.
Closing business at open enrollment isn’t the end of the sales cycle. The best insurers know, it’s just the beginning of a long customer relationship.
Instead of thinking about the sales cycle like this:
Outbound > Appointment > Quote > Sale
Think about it like this:
Outbound > SMS opt-in > Reminder and appointment prep > Appointment > Quote > Follow-up > Sale > Survey > Outbound > Sales Incentive > Outbound > Next Year’s Open Enrollment
Insurance automation can do a lot more than just win business today. It can strengthen and expand customer relationships throughout the year and beyond, ultimately reducing member churn.
The key is customization. Remember those low-code customer agents? Likewise, a low- or no-code automation platform can be quickly tailored to offer discounts, send birthday greetings or whatever you’d like after the customer is on board. Because the automation platform hooks into the firm’s CRM (and you should make sure any solution you purchase does), it can access all that customer data to deliver a highly personalized experience from start to finish, without an agent needing to make a single call. It’s the most efficient way yet devised to engage customers—and retain them.
No need to bombard them—just connect with them periodically to keep the relationship warm.
Remember: firms that keep most of their customers year after year don’t have to sweat their sales targets in Q4.
Open enrollment is fast approaching, but it’s never too late to equip your teams with insurance sales tools that can help them nail their targets—and even get a head start on next year’s.
Ideal for companies struggling to get ahead of open enrollment, Ushur’s insurance agency automation system accelerates time-to-value from months to weeks with features like:
Contact us for an insurance sales automation demo today.
COVID-19 turned everyday life upside down, and everyday work too. Yet work goes on. Companies need to connect with their customers as much as ever, maybe more. The question is how.
We recently hosted a webinar on just this topic—how businesses are using conversational AI to engage customers during the COVID crisis, and just as importantly, why. For those who couldn’t join, here are some of the highlights.
Customer inquiries have spiked during the pandemic. In one study by Customer Contact Week, 32% of the companies surveyed reported an increase in inbound requests, and 10% of those surveyed saw a 50% jump in volume.
For businesses, this is a real dilemma. They need to keep customers happy. But their agents are working from home, where tasks may take longer to complete and Internet connections aren’t always reliable. At the same time, businesses still need to comply with strict regulations governing customer privacy and cybersecurity. Communication channels like email, which might have been fine for casual exchanges in the pre-COVID world, buckle under the strain of everything they’re being asked to do.
One Harvard Business Review study examined a million customer service calls fielded at call centers since the start of the pandemic. It discovered that escalations were up 68%, and overall call length 34%. Calls were not only becoming more numerous, but longer and more complex. Not surprisingly, the study found abandon rates up as well, signaling that customers were losing patience.
Clearly, status quo solutions won’t cut it anymore. Businesses need alternatives, and one of the standouts is conversational AI. This is technology that understands human language, but also understands intent and sentiment, giving it the ability to engage customers with realistic dialogue and react like a human agent.
Unlike human agents, conversational AI doesn’t get tired or impatient. It doesn’t forget facts. It allows businesses to offload high-volume, routine conversations to a bot, giving agents the freedom to focus on higher value formats that require human judgement.
We see enterprises leveraging conversational AI and, more broadly, automation, to power a comprehensive, omnichannel approach to all sorts of customer tasks.
According to a survey from customer-desk software firm Zendesk, customer inquiries via SMS have increased 26% since the start of the pandemic. This is an ideal opportunity for a chatbot powered by conversational AI. The bot can handle routine conversations and automatically pull in a live agent if the discussion gets too complex, or the AI senses that the customer is getting frustrated. Similar bots can be implemented on a company’s website.
With Dynamic Call Deflection, businesses can give customers waiting on hold the option to talk with a digital agent, powered by conversational AI, rather than to wait for a live agent.
Conversational AI and email automation mesh surprisingly well. Companies that receive hundreds of thousands of inbound emails per month can use conversational AI to quickly route them to the most relevant customer service department, or even answer them automatically.
Automation is good, and bots are helpful. But no one wants customer interactions to feel, well, robotic. With conversational AI, businesses get the efficiency of automation with the personal touch of a conversation.
It doesn’t look like businesses will be back to normal anytime soon. They need to automate now, and they need solutions they can implement remotely. Conversational AI platforms that are based in the Cloud or can be implemented by remote teams fit the bill.
Customers have options. They’re impatient. They want—and expect—to engage using the digital channel of their choice, whether that be text, email, or other. conversational AI allows businesses to meet customers where they are, enabling an omnichannel approach that’s automated from beginning to end, while still delivering a friendly and personalized experience.
Remember that need for speed? So-called low-code conversational AI solutions allow business users—what we might call “citizen developers”—to quickly build sophisticated AI-powered workflows without knowing a line of Python or Java. That in turn allows them to quickly launch customized new campaigns, build their own customer journeys and implement the right compliance and security guardrails without having to wait on IT. It’s the breaker of barriers, bringing siloed organizations and departments together.
A survey conducted by AppDynamics found that 88% of technologists and IT professionals rate digital customer experience initiatives as their number-one priority. Conversational AI can be a big part of that strategy. We see customers use it to proactively engage with claimants, subscribers, or patients, to request information or complete routine tasks. And since conversational AI platforms like Ushur easily connect to your CRM and customer-desk systems of record, the data from these interactions can be saved, analyzed and used to improve future customer engagements. The more you use it, the more it learns and the more it can do.
If you’d like to learn more, check out the full webinar and hear Michael Fisher, VP of Product, and Vandana Rao, VP of Customer Growth, talk about how leading organizations are adopting conversational AI today.
If you groaned, you know how frustrating and demoralizing this experience can be. After getting rerouted multiple times, customers, who’ve come to expect instant service, are placed in queues that feel like an eternity with delightful Muzak, growing more irate by the minute. Harried agents are disparaged for something out of their control. It’s a recipe for losing morale and consumers.
When phone calls come in faster than your teams can handle them, you need a strategic way to deflect those calls, for everyone’s sanity.
Existing Interactive Voice Response (IVR) technology or Workforce Management (WFM) software are no match, given the increasingly complex and heightened expectations of consumers.
Today’s customers are empowered, and they:
With the world at their fingertips and with a swipe on a smartphone screen, why would customers be OK waiting for hours to speak to a live agent?
Hint: they’re not.
Enter call deflection, a solution that supports existing IVR systems with the flexibility of digital channels.
After fire, writing may be human beings’ most enduring invention. It’s powerfully asynchronous ‒ you can communicate across lifetimes and continents.
Customers don’t like to call in, if they can help it. In fact, the No.1 inbound digital medium is the email, a writing delivery system that would make Guttenberg grin. However customers often turn to the phone when they’re convinced that their urgently dashed off requests will be lost in the ether.
Gartner Research indicates that 70% of service interactions over the phone can be solved via AI-powered, automated customer support. That’s
of customers whose needs can be addressed in real-time.
of customers who won't experience a wait time when they call in.
of customers who will have dedicated agent time to resolve their complex requests.
It’s time to meet customers halfway, by suggesting a digital medium of their choice just when they’ve given up hope and are desperately trying to reach an agent (and simultaneously overwhelming the system).
Introducing IVR-to-text, call deflection with the humble, but beloved SMS.
Instead of being held in-queue during peak hours, you can give your customers a choice to have their issue or query handled via a digital channel instantly...right through their smartphone. For example, your customer can enter Ushur’s Invisible App, an app-less, app-like automation that exists over their web browser. No download or account needed.
The Invisible App (IA), part of the Ushur platform that is designed to intelligently automate workflows, is a white-labeled, direct encrypted channel that allows for two-way communication with your customer. It can handle the type of flows that agents would typically deal with over the phone, using Natural Language Processing (NLP) to understand and address customer needs.
For inquiries as diverse as billing to support to account update, even for sales orders, customers can now interact with an AI-powered experience instantly, instead of waiting for a live agent. The Invisible App is especially adept at handling multi-step, complex processes with accuracy and speed.
Regardless of the selected digital medium, customers can get and communicate information at their convenience, on their preferred digital channel, even as the pressure on your agents and call infrastructure drops.
With COVID-19, businesses were confronted with surges in calls, and this incredible volume shows no signs of dissipating. Deflecting calls with digital channels, like the Invisible App, allows you to manage this lumpiness. Automated digital channels are easy to scale, allowing you to service customers in real-time, reducing the possibility of angry calls and overwhelmed agents. With the reduced demand from repetitive inbound, your agents can actually dedicate their time to problem-solving worthy of their abilities.
With digital deflection, give customers a frictionless option to have their needs met in real-time. Whether it’s checking on their insurance co-pay or updating critical financial forms, customers can verify and upload documentation on their own schedule, with the convenience of an app. As a two-way medium, the Invisible App can keep customers abreast of changes or missing information, giving them peace of mind that you’re on the case. The customers who do end up needing a live agent will be able to reach them faster.
IVR is here to stay; it’s endemic to so many enterprise operations, and while it may not be the sleekest of technologies on its own, it’s reliable and ubiquitous. The Invisible App can easily integrate with your existing IVR technology, instantly modernizing your operation while keeping the best parts of your current setup.
Reduce churn, bring those C-SAT scores back up, and give your agents the breathing room to have quality customer engagements. From healthcare to financial services to car insurance, your customers want to feel heard. Raise them one, and make them feel understood. And AI-powered call deflection allows you to do just that.
If you’ve ever been burned by a chatbot, we’ve got good news: just because you’ve met one doesn’t mean you’ve met them all. The truth is chatbot solutions span a wide range of functionalities and use cases. Many businesses tend to start with a bottom-of-the-barrel model to get a feel for how chatbots might benefit them. It’s a fair idea in theory. Inevitably though, they end up with disappointing ROI and write off the whole breed as unhelpful.
But if you’re here reading this blog, it means you’re giving chatbots a second chance (thank you!) Or perhaps you’ve heard mixed reviews, and you’re “just browsing” to see what’s out there. In any case, we know there’s a lot of factors involved in chatbot evaluation.
So we’ve done a comparison of chatbots to help you out. Our chatbot evaluation criteria examines chatbot providers in different tiers—what services they offer, what use cases they support, pricing and ease of use—and how Ushur’s own conversational AI chatbot rewrites the paradigm.
Our chatbot comparison groups chatbot providers into SMB- and enterprise-tier, implying both the intended end-user type and their chatbot’s corresponding functionalities. You’ll notice enterprise-tier chatbots are workflow-driven, serving as virtual customer service agents, whereas SMB-tier chatbots tend to be conversation-driven, acting more as virtual hosts.
We pointed out earlier that enterprise-tier chatbots tend to be workflow-driven while SMB-tier chatbots tend to be conversation-driven. Clearly, there are a lot of variables even within those two categories. An easy way to keep them straight is by thinking about them the difference between a conversational chatbot vs. a customer service chatbot.
Low-tier, SMB-oriented chatbot solution providers offer conversational chatbots. These are essentially e-commerce assistants trained to recognize buyer intent and accomplish simple sales tasks. When you shop or request services online, most often you’re interacting with a conversational chatbot. Conversational chatbots can offload volume by deflecting simple questions away from your customer support team. But these bots don’t offer much in the way of customer service beyond answering FAQs.
On the other hand, enterprise-tier vendors provide customer service chatbots designed to help end-customers self-service. Customer service chatbots guide users to get stuff done without assistance from a human agent. Industry-specific solutions like insurance chatbots, fintech chatbots and healthcare chatbots all fall into the broader category of customer service chatbots.
Unique to Ushur’s customer service chatbots is our design-your-own automation workflows. Our intelligent AI platform enables business teams to build custom and complex workflows that align with the customer journey via simple drag-and-drop tools, without involving the IT department.
PS: We wrote an entire e-book on using chatbots to retain customers during COVID-19. While the book is framed by the insurance industry, the key takeaways apply to any audience in any context.
Now that we’ve explained the broad strokes, we’ll dive into the more specific qualities that set chatbot tiers and vendors apart.
Your relationship with an automation partner truly begins at implementation. Fittingly, this is also the stage where customers start to wonder if they’ve bit off more than they can chew. Overwhelm by implementation happens for a few reasons: perhaps the solution is more technically complicated than you bargained for, or perhaps the vendor wasn’t transparent about customer support resources.
The bottom line is, as you compare chatbots, it’s crucial to consider how much support you’ll need to implement your bot.
Some vendors do not offer training or free setup assistance, so the process falls entirely to you. Others charge onboarding fees that depend on the use case and complexity of system integration. For example, typical onboarding starts at $40K for some enterprise-grade providers.
Unlike other chatbot providers that just hand over the chatbot without training or upcharge for onboarding, Ushur’s implementation is all-inclusive and led by our engineers.
Additionally, we pre-train our chatbots on a database of common industry parlance so they can start with 80% accuracy out of the gate. Typically chatbots require training on a minimum of a thousand machine learning examples before they achieve human-acceptable accuracy.
If you’re planning on setting up a chatbot in-house, better get up to speed on machine learning, as it’s not for the faint of heart.
But once you’ve got your chatbot up and running, how can you tell if it’s hitting ROI?
It’s unlikely a bot will work perfectly for you right out of the box. Most organizations have to modify a few workflows and do some tuning before the chatbot can really sing. Determining chatbot efficacy is where a committed chatbot provider comes into play.
A committed chatbot provider will use key performance indicators (KPIs) to evaluate whether your chatbot is excelling in its target use case and meeting your business goals and objectives. Measuring the KPI of your chatbot is critical. You have to know what is and isn’t working so you can make the right adjustments.
Ushur begins every engagement by setting custom KPIs with our partner. Examples of KPIs include number of deflected calls, number of deflected emails, increased resolution time by xx%, customer satisfaction scores and more.
In addition to measuring value through KPIs, we continuously add value by providing AI/ML model training with historical data and version controls. Our intelligent call center chatbot software gathers data from the powered-by-chatbot call center and strips the data of personally identifiable information (PII). Usher engineers train the bot using the dataset, then later evaluate its performance.
If Version 4.0 is better than Version 5.0, then we revert back to 4.0. We use this iterative process to improve our AI chatbot solutions version-by-version until they reach or exceed human accuracy.
Security is the last factor that separates the sheep from the goats, and thankfully it’s fairly straightforward.
We encourage every buyer to ask potential chatbot providers if they follow industry compliance standards. When you’re an enterprise hosting millions of data points of sensitive customer information, you need to ensure every vendor with access to your customer data maintains airtight security.
Our chatbot solutions are built with military-grade security, complete with AES 256 encryption and multi-factor authentication. Ushur is SOC 2 Type 2 and HIPAA compliant.
Obviously there’s a wild gulf between solutions designed to capture leads and answer basic questions and enterprise chatbots that can automate complicated customer service workflows.
Usher’s chatbot solutions technically fall in the latter category, but just calling them “enterprise chatbots” doesn’t quite do them justice. “AI chatbots” or “AI chatbots for customer service” might be more appropriate, as our chatbots are powered by intelligent automation and conversational AI.
Remember the frustration of being on-hold for an entire afternoon, as weird Muzak plays in the background? Using chatbots for customer service (especially when they’re AI chatbots for customer service) minimizes wait-times and agent transfers, accelerates issue resolution, reduces operating expenses and ultimately improves the user experience.
If increasing customer satisfaction and engagement is your goal, you need an efficient customer service chatbot—that’s where basic types of chatbots like conversational chatbots fall short and result in missed customer service opportunities.
The chart below explores how Ushur compares to other enterprise chatbot providers.
“Give me a place to stand, and with a lever, I will move the whole world,” the Greek scientist Archimedes supposedly said.
Silicon Valley is full of Archimedeans. We love to talk about leverage. Leveraging our differentiators. Leveraging investments in technology and people. But not all levers are equal. As an executive, you have short levers that move a little. You have long ones that move a lot. And it’s harder than you might think to tell the difference.
I thought about this while reading an interview we recently did with one of our customers, Irish Life. It’s a distinguished company that came to us with a specific need: establishing an automated email routing software solution for their corporate division. They had a team of highly educated employees reading incoming emails and routing it to the right departments. They saw an opportunity to streamline the email routing process with Ushur’s SmartMail email routing software and put the triage staff onto higher-level tasks.
And that’s exactly what happened. We set up a pilot, trained our conversational AI, and put it to work. The benefits started rolling in immediately. Responses that used to take as much as 2.5 days to process were now routed in a blink of an eye. Total resources required to get the work done dropped 40%.
I’m not going to lie. It was a thrill. It never gets old seeing your product and people deliver.
But while the project’s success was exciting, it wasn’t exactly surprising. After all, it’s what we do every day. What stood out to me was that what might seem to outsiders a modest modernization of Irish Life’s back office, in fact, has huge impacts far beyond the initial scope of the project. It didn’t just improve Irish Life’s email routing. It inspired the company to take a fresh look at how it interacts with customers.
Ken Lynch, Head of Information Systems at Irish Life, explained that insurance companies tend to steer customer queries to portals on the Web. But after the success of the email routing automation project, Irish Life started thinking there might be a better way to reach out to customers. Maybe they could proactively gather information from customers using Ushur’s Invisible App.
Lynch said: “You need to think about how the customer wants to interact with you. Think of yourself, think of your mother, or one of your relatives, and how they would try to interact with big companies. What we’ve seen with Ushur is that it's all about customer ease.”
I take away two lessons. First, transformation doesn’t have to be extremely difficult. Anything that makes the customer’s experience better--even in the back office--has the potential to improve the entire business.
Second, automation can be an even longer lever than you think. Obviously, it helps you do more with less. But as Irish Life discovered, figuring out how to automate customer service forces you to think about what customers want. How do they like to communicate, when, and through what channels? Would they prefer a phone call from a human agent, with all the potential friction that comes with it (e.g. missed calls, or calls that reach customers when they don’t have information on hand)? Or might they prefer to handle routine interactions at their convenience, using digital methods?
It’s impossible to answer those questions without putting customers front and center, which is where we all know they’re supposed to be. This interview reminded me yet again that improving customer ease--as Irish Life memorably put it--might be the longest lever you have.
In the shipping and logistics industry, the phrase “last mile” describes the final leg of the delivery journey: the step right before a package arrives at your door. The last mile is famously the most expensive and inefficient part of the delivery process. It’s frustrating for consumers too—if you’ve ever cursed a package for spending a week “in transit” after leaving the warehouse, or waited to sign for a delivery that never came—you’ve felt the strain of the last mile.
We see a similar “last mile” in end to end customer engagement. It’s the final touchpoints customers have with a brand before they complete a workflow. This is the sweet spot after a customer has received their marching orders and now must execute them. Just like in the shipping industry, a lot can go awry during those penultimate steps.
Today we’re spotlighting part of the Ushur platform that helps customers deliver seamless customer engagement down to the last mile: our location based services feature.
You likely use the Invisible App for its conversational AI and end to end automation functionalities. What you might not know is that the platform also integrates artificial intelligence and location based services. Customers can send you their location in the Invisible App by manually inputting their address or allowing the browser to pick up their position. Then, you can guide them to the next step in their journey, whatever that may be.
Our customer engagement platform already enables brands to personalize interactions. Using location based services to offer extra support is a natural increment to any strong customer engagement strategy. With this feature, you can dynamically deliver solutions based on a user’s surroundings. Our location based services software is time-zone aware, and the services rendered reflect local market rates. Talk about effective customer engagement!
The timing of this blog might seem rather ironic. Why highlight location based services now when, for the first time ever, most of the world is staying home?
Right now it’s important to limit your public exposure as much as possible. That means taking the most direct route from A to B when you do venture out.Let’s say a customer gets into a fender bender coming back from the grocery store. They complete a claim through the Invisible App. If they opt in to location based services, the insurance company can identify exactly where they are (helpful when an accident occurs out on the road where there’s no exact address) then send the customer names and addresses of nearby body shops. The customer can call ahead to ensure availability—eliminating the need to “shop around” and keeping public interactions to a minimum.
Or, in a perhaps more significant example, a customer who reports a medical emergency can be directed to the closest in-network hospital. A health insurance company might also guide customers experiencing COVID-19 symptoms to a nearby testing site.The auto-locate capabilities is one of the top benefits of location based services. Time is of the essence when a customer is in crisis, and typing in an address can be mentally or physically difficult.
Every customer is different. Every customer journey is different. The beauty of digital customer engagement is that workflows can be, too. When we talk about practicing contiguous customer engagement, we mean supporting the customer no matter what situation they’re in—whether they need to see a doctor ASAP or they’re a work-from-home dad desperately searching for baby wipes. With location based services, help will be on the way very soon.
B2C enterprises everywhere are completely swamped with customer emails. Even the world’s most efficient support agent can’t wade through the torrent of customer emails pouring into bulk inboxes quickly enough to respond in a timely manner. All of these emails have to be reviewed and routed to the correct employee, who then must read the email again and finally write a reply. It’s no wonder agents everywhere are desperately wondering how to respond to emails faster.
That’s the focus of our latest e-book, The Email Automation Guide for B2C Customer Service.
The guide unpacks how email automation software (like Ushur’s SmartMail solution) processes thousands of incoming emails, attachments and photos to help you answer customer queries more efficiently.
In under a second, SmartMail categorizes bulk emails based on pre-set email classification rules, auto-responds to each sender and forwards the email to the appropriate internal team. If it’s a simple question, SmartMail’s conversational AI and intelligent processautomation services can retrieve information from back-end systems and proactively answer the customer. Pretty amazing, right?
Even sending them a holding message (“we’re working on your issue!”) via outbound email automation software is a big improvement in responding to customers faster. No, really! A little validation that they’re not being ignored goes a long way.
For one, responding to customer emails faster curbs channel switching. Channel switching is when customers cycle through multiple conversation platforms over the course of their journey. If a customer doesn’t get the help they need over email, they’ll keep trying other outlets (like your support line) until they reach you. The more delayed your response, the more likely you’ll trigger channel switching.
Channel switching quickly becomes detrimental because it runs up your OpEx astronomically. Live channels (phone calls, live chat, manual email) cost about $8.01 per contact, while self-service channels (automated email, chatbots, mobile apps, website FAQs) cost around only $0.10 per contact. Switching to even one live channel incurs an OpEx cost 80 to 100 times higher.
Self-service channels save
per contact
OpEx cost increases
without one live channel
Automated email sending, even if it’s simple holding messages, blows your customer’s expectations out of the water. Forrester research found that while 41% of customers expect an email response within six hours, only 36% of businesses respond that quickly and 14% never respond at all.
How Email Automation Improves the Customer Service Experience
Customer Service Problem | Email Automation Solution |
---|---|
Support inbox receives 100,000 emails a month | Inbound email processing AI scans, reviews, classifies and routes to the right department in seconds |
Agents spend valuable time answering the same simple questions over and over | Pulls relevant answers from knowledge banks and back-end systems and auto-responds to the sender |
The longer emails sit in the queue, the longer customers wait for responses, the most frustrated they become | Automated email routing helps questions get answered faster |
Customer emails are often missing key information | Outbound email automation software proactively responds to the customer to request missing details |
Manually extracting relevant data from emails and populating fields takes forever | Smart email workflow automation populates form fields, extracts data and connects with back-end systems |
Let’s look at a real email routing case study. A life insurance company serving 1.3 million customers sought to use an automated email system for the hundreds of thousands of emails it receives every year. Every email was opened and read by a member of the email-triage team, who would classify based on Key Business Indicators (KBIs) and forward it to the appropriate team. There the email would be opened and read again, and finally sent to an individual case manager.
Customers interacting with automated email platforms notice a difference. Questions actually get answered and problems are resolved delightfully quickly. They have validation the issue is being addressed—even if it’s just an auto-response that says “we’re working on it!” They’re no longer left wondering if their email was lost in the ether.
For enterprises always drowning in a flood of messages, email automation delivers the holy grail: inbox zero.
Customer support agents are limited by fatigue, the 40-hour work week and the increasing weight of unanswered emails on their motivation. AI reaches the end of the queue in seconds and looks around for more work. With your email bases covered, employees assigned to the menial review-and respond-work are freed to focus on more business-critical tasks.
Enterprises using email automation can not only achieve the impossible (processing hundreds of thousands of emails every month), they can eventually run a nearly hands-free operation, automating 100% of incoming email.
Applying conversational AI and machine learning transforms email from a major headache into a compelling channel that actually solves issues. Sure, automated data extraction and population knock out time-consuming, error-prone tasks, but they can also enable new business insights.
Data extraction tools pull critical information from customer emails. Data population tools automatically ingest it to your CRMs or systems of record (if your email automation platform integrates with back-end systems). That’s already a huge bridge to better understanding your customers. Email automation platforms with reporting dashboards take analytics a step further, delivering real-time stats on automation activity, customer engagement rates and step-by-step responses.
We’re back with another installment in our SMS tips and bad habits series! To recap, in our last post we covered text messaging best practices around SMS compliance and writing clear, actionable copy.
Today we’ll unpack four more do’s and don’ts of running successful SMS campaigns. How often can you text customers without seeming desperate? When do you know it’s time to give them some space? Read on to up your mobile customer engagement game!
Hitting customers with four or five messages a week is only going to get your number blocked.
Sending message after message in rapid fire is an especially bad habit. Not only is it annoying, a customer’s anti-spam and anti-DoS attack safeguards can cause similar messages to be dropped. Related and equally annoying is sending duplicate messages that repeat earlier content. If you’ve already conveyed the information over SMS, there’s no need to do so again unless it’s a reminder message.
SMS marketing experts recommend sending 4-5 messages/month for the most successful SMS campaign possible. 10 messages/month is the absolute maximum, and only if there’s a really active engagement opportunity.
It’s important to note this guideline only applies to text message marketing. Important transactional messages, such as the status of claims/orders or appointments, do not have a cap. However, per the TCPA, you need to let customers know how many messages they might receive in a month when they opt-in.
For example:
If your SMS campaign involves a survey, give customers an estimate of how much time it should take to complete. Keep the number of questions and open-text responses to a minimum. Long surveys that require lots of typing are more likely to be abandoned by customers on a mobile device.
If you’ve ever gotten a text from a brand that spills over to two or even three separate messages, it’s likely because it hasn’t been encoded properly.
Special characters in SMS require two bytes instead of one byte. Incompatible encoding will cause messages with special characters to bloat and fragment, meaning they land in a customer’s inbox in multiple incomplete parts.
Nobody wants to get triple-texted… especially not by a brand.
GSM-7 character encoding is the standard alphabet for SMS messages, written in the standard GSM 03.38. You can dig in to the details of GSM 03.38 encoding later, but for now, remember to keep your messages under 140 characters. That should accommodate any URLs or links without fragmenting the message.
You obscure instructions for opting-out and fail to tell customers what SMS charges they might incur. Or, the unforgivable: you text customers in the middle of the night or after they’ve unsubscribed.
It’s good practice to warn customers about potential messaging fees when they first sign up for SMS. You can simply say, “Standard SMS rates apply.”
Text message regulations also mandate that you may not text a customer who’s opted-out of messages, or text them during non-business hours (9am-9pm local time). Don’t be that guy sending “U up?” at 3am.
We all have that friend who never responds in a timely manner. A good way to make them realize they’ve left you hanging is sending them something completely unrelated two days later. Then they text back something like, “Sorry just saw this!” or “Sorry, I was asleep when you texted me earlier!”
Whether they unintentionally missed your message the first time or not, sometimes all it takes to get a response is a gentle nudge that starts the conversation back up.
The same thing happens at the brand-customer level. Customers see your text come through while they’re at work. They make a mental note to read it later, but then six hours pass and they’ve forgotten it entirely. Or maybe they get distracted in the middle of the SMS campaign and never return to finish the workflow.
The truth is, if a customer doesn’t respond, the onus is on you to make sure they complete the campaign. Why are you responsible? You’re the one running a mobile customer engagement strategy, not them.
You don’t have to let yourself get ghosted. You can easily draw the customer back in with quick reminders to respond to an outstanding request or pick up where they let off in the workflow. A simple follow-up can go a long way in maximizing campaign completion rates.
Only by being a good texter will you build trust and enhance customer experiences through SMS. Constructed through industry recommendations and years of learnings with our own customers, follow these best practices to maximize your SMS outreach.
Ushur uses a template-based approach to solve industry-specific use cases for SMS campaigns. A state-of-the-art linguistics engine can capture unstructured inbound texts, analyze responses using our AI based Language Intelligence Service Architecture (LISA) and take intelligent actions, such as retrieving information from your system of record or routing to an agent.
Together with a drag-and-drop process and workflow builder, Ushur’s Invisible App delivers a fully-functional white-labeled conversational interface. The platform also provides real-time monitoring, audit capabilities and a powerful analytics engine.
Sample Ushur features every good texters should use include:
Invisible App is a customer engagement mobile app that automates inbound and outbound communications using conversational AI. Try it today and deliver fully branded, app-like experiences that convert more customers than ever before.
Bad texters: we all know them. Maybe we are them. Bad texters write long, rambling messages. They annoy you with too many emojis or too much punctuation. Or, they’re completely toneless, leaving you wondering if they’re mad. They blow up your phone. They take days to respond… or don’t at all.
And guess what? Bad texters aren’t just grandmothers and irresponsible friends from college. Sometimes they’re insurance agencies or banks or healthcare providers.
More enterprise brands are launching a mobile customer engagement strategy, and for good reason. SMS is a cheap channel for communicating quickly with thousands of customers. If done correctly, it can drive impressively high engagement rates.
But those bad texting habits that crop up in our personal lives roll right over to brand-consumer interactions.
So, if you’re an enterprise with an SMS channel, here are three signs of bad texting behavior to watch out for—and three text messaging best practices to use instead.
This is first in the list because it’s the absolute worst habit you could develop. Why is it the worst? The others might annoy or confuse your customers, but ignoring text message regulations is downright illegal.
The reason you don’t get telemarketing calls in the middle of the night is thanks to FCC text message regulations called the Telephone Consumer Privacy Act. The TCPA controls what kind of content and conversational methods brand can use when communicating with consumers over phone, email and text.
Honoring customer consent is the throughline of TCPA regulations, which you can read in full here. For example:
The TCPA is a good snapshot of general SMS regulations, but it’s certainly not the only standard you should know. You may also have to comply with country or state-level rules, such as the California Consumer Privacy Act (CCPA).
While not requirements, the Cellular Telecommunications Industry Association offers a list of best practices worth a review.
If following the law seems like common sense, then good, because brands do get this wrong. In 2013 Papa Johns agreed to pay a $16.5M class action lawsuit after sending 500,000 unsolicited texts advertising pizza deals—violating SMS opt in regulations. Customers reported getting spammed with 15 to 16 texts in a row, even in the middle of the night, despite not opting in to receive promotions. Yikes.
If you send customers a message that says, “Your appointment is on Feb 5th at 3:00,” they might have some questions. Among them: “Wait, what appointment?” and “Who dis?”
Customers will hesitate to interact with your messages if you don’t provide any context. Worse case scenario, they might think the message was sent in error and simply ignore it.
You absolutely should keep your messages short and sweet, but remember that they need to very clearly explain the offer, update or reminder.
The content of your message should introduce yourself and address the customer by name. That helps customers identify that the message comes from a legitimate source and is intended for the right person.
See example below:
“[HappyInsurance] Hello Bob, we’d like to remind you of your upcoming appointment with Agent Joe on Feb 5th at 3:00 PM”
However, you can use direct language and still keep a friendly tone. Like this:
“Hello from Your Insurance! Alice, we’d like to remind you of your upcoming appointment with Agent Joe on Feb 5th at 3:00 PM”
Are customers leaving you on read when you’re expecting a response? Maybe that’s because you’re sending messages like:
“Hi, We need more information for your application.”
Without a specific action requested, customers won’t understand what you’re asking them to do or how to do it.
When you need customer to complete a task, your message to them should contain:
Compare the message above to this second message:
“Hi Jane, please upload your driver’s license using the following link to complete your insurance application.”
Did you see your brand in any of those bad texting habits? We’ve got four more signs you’re a bad texter (plus four more text messaging best practices) coming up in part two. Tune in to 4 More SMS Bad Habits + 4 Mobile Customer Engagement Tips to learn what customers find creepy, how to combat ghosting and the dangers of triple-texting.
For a customer engagement mobile app that provides everything you need to be a good texter, try Ushur’s Invisible App. The Invisible App automates inbound and outbound customer communications, combining advanced natural language technology, sophisticated process automation and a UX designed for large brands.
Sample Ushur features every good texters should use include:
See you in part two!
We are proud to announce that we’ve officially launched the Ushur’s Managed Package for Salesforce, now available in the Salesforce AppExchange.
Now you can use Artificial Intelligence to automate customer engagements over email, web, SMS, and more, integrating directly within the Salesforce interface. This allows customer support and operations teams to deliver powerful self-service tools across the digital channels your customers are engaging on.
With intelligent process automation in Salesforce, you can streamline a number of use cases including:
Ushur’s App key features include:
With Ushur’s Intelligent Automation App for Salesforce, enterprises see faster customer response times and increased customer satisfaction. To learn more about the app, how it works, and the benefits to the enterprise, check out our app on the Salesforce AppExchange or contact us for more information.
We get a lot of questions about whether conversational AI lives up to its promise. And we get it: on a personal level, one frustrating experience with an all-too-helpful chatbot or virtual assistant can be enough to turn you off. So it’s hard to imagine those conversational platforms in play at an enterprise level, especially if you’re dealing with sensitive customer information.
But the reality is that our interactions with smart speakers and chatbots are a tiny window into what full conversational AI platforms can do. That’s particularly true when it comes to delivering personalized customer experiences.
Today, conversational AI can automate nearly any kind of customer interaction, execute complex outbound and inbound requests and pull information from backend systems—and perform all of these tasks across platforms. Customers can text you if they hate making phone calls, and later they can continue the conversation over email. All of this happens in the same communication channel. Conversational AI operates at both an incredibly wide and incredibly personalized scale: it can read and process thousands of emails in seconds, and it also remembers to tell your customers “happy birthday” every year.
We would know a thing or two about its capabilities. We wrote a whole guide on using conversational AI for customer service, which you can download for free here.
Our guide details the AI techniques used in chatbots and other conversational AI tools for a business audience (read: we’ll tell you how Natural Language Understanding actually works in a digital self-service context), provides conversational AI examples in use by customers today, and explain the difference between conversational AI and chatbots, best use cases, what value to expect from conversational AI platforms and more.
Chapter 5: Business Benefits & Impact
In addition to the customer experience benefits, conversational AI also drives two varieties of business results.
Better Service = Happier Customers
The faster you address their concerns, the happier the customers. In insurance, for example, it might take two or three weeks to resolve a claim. With conversational AI automating the manual data entry and back-and-forth, that claim can be resolved in a day.
The majority (66%) of adults feel that valuing their time is the most important thing a company can do to provide them with good online customer experience.3
Obviously, “happier customers” is sort of an indirect benefit, but it does break down to real business benefits. Take this story for example.
One large insurance company decided to automate its claims servicing using two-way SMS. The claims process took three weeks to complete, with agents making six attempts to reach customers by phone. In its first year deploying conversational AI, the company converted its outbound calls into 70,000 automated text messages per month. The results were dramatic:
Other enterprises using conversational AI saw similar results:
Reduced OpEx = Happier Employees
Companies that make a concerted effort to improve their customer experience also see employee engagement rates go up by an average of 20%.4
Service agents who spend more time solving meaningful problems for customers and less time completing menial tasks are happier employees. The main business benefit to conversational AI is happier employees—but reduced OpEx is another sub-result.
In chapter three we talked about how conversational AI slashes high volume drivers. Let’s take support calls as an example. Typically, every call costs you between $12-17. Lowering call volumes has an impact force on operational expenses.
One large enterprise using conversational AI reduced outbound calls by 94,000+ per year.
We’re talking about reducing OpEx spending not in the thousands or even hundreds of thousands—we’re talking about saving millions of dollars every year—and that’s on just one call driver.
Time is money, so there’s also the impact to top line revenue to consider. In the shipping industry, for example, if a customer emails requesting a quote it takes businesses (on average) 57 hours to respond. Conversational AI can automate quote processing in 5 hours. Getting 90% of your time back and being 90% faster than your competitors drives an outsized advantage.
Intelligent automation is a comprehensive solution for digital transformation which combines AI, ML, and RPA technologies. The financial and strategic benefits of this technology are widely known and acclaimed, yet many companies have intelligent automation questions regarding its implementation and deployment. Keep reading to learn more about the Top 5 FAQ’s potential customers have about the Intelligent Automation adoption process and how Ushur can answer these uncertainties.
We have had customers with complex systems of record integrations fully deployed in 1-2 months. The process typically starts with scoping out the data requirements needed, and then how that data will be transferred. Often, this is done through APIs or standard integrations with core systems which Ushur will provide.
Our technology offers an automation analytics dashboard which continuously tracks user trends, behaviors, and adoption patterns. Any alarming patterns or trends will be reported to appropriate personnel, so that your company can make necessary adjustments to the processes before they become obsolete. Our intelligent automation platform highlights proactivity and being one step ahead at all times.
Our visual, workflow builder with drag-and-drop capabilities ensures that any modifications to the automations can be achieved in an expedient matter and by non-technical users.
This technology is designed to ease your employees’ lives, so our workflow builder, which can be handled by any employee regardless of their technical background, can stitch together a process which can be engaged on SMS, email, or web chatbots depending on what you prefer.
Furthermore, our customers are often using Ushur for deflection to preferred, lower cost channels. For example, with voice to text technology, Ushur is able to deflect customer calls to web or SMS based communication if the customer prefers, saving the company money and customers their time.
The Telephone Consumer Protection Act limits the usage of automatic dialing systems, artificial or prerecorded voice messages, SMS text messages, and fax machines. To maintain the standards of the TCPA, we have text and email opt-in capabilities so customers have control over the messages they receive. Moreover, Ushur Visual Tools and APIs allow enterprises to distinguish the customers who have opted in by themselves and those who were added explicitly by the enterprises. Users can also opt out of receiving messages whenever they want. Once a customer opts out, Ushur will maintain their contact information on a blacklist.
Lastly, Ushur has a Do Not Disturb (DND) feature which limits the times when messages can be sent to an end user depending on user preference (i.e. restrictions on sending messages during the weekend). All of these capabilities are in place to protect the end user and ensure that our customers are TCPA compliant.
Our AI/ML solution can be deployed without investing in any data science engineers since with our data anonymizer, model training, and pre-flight simulator tools. Moreover, the workflow builder allows users to handle and manipulate solutions with ease and without technical expertise.
Some specific solutions such as SmartMail, Smart Conversations, and Intelligent Data Extraction will apply AI/ML to problems in real-time, making the platform more relevant and useful for customers and enterprises alike.
Business Insights through our analytics dashboard make it easy for companies to realize KPIs and analyze customer experience and ROI in a transparent manner, so any issues will be flagged and remediated before coming close to failing.
Lastly, there are a multitude of machine learning and deep learning models available and designed for specific intelligent automation use cases. These specific solutions are more tailored to your company’s needs, so the success rate of the solutions are skyrocketing.
App usage is skyrocketing with 194 million global app downloads in 2018 alone. This smartphone era is distancing itself from calls and emails and shifting toward mobile solutions. Modern problem solving is designing apps and throwing them at the problem hoping for something to stick. With this surplus of apps comes a greater challenge to differentiate your business’s app, to capture the attention of the customer, and to convince them to download and utilize your app. In 2017, the average smartphone user had 80 apps on their phones and used around 40 of them a month.
But what if you could deliver an app-like experience, without asking your customers to download an app?
Keeping these challenges in mind, Ushur conceived the Invisible App to remove the friction of apps, while still maintaining the digital, app-like experience that customers prefer. Not only is app adoption an arduous process, but app development is time-consuming and demanding on developers. Bringing apps to market takes around 4-5 months and constant updates and bug-fixes are required after the app is taken to market as well. We wanted to lower the barriers customers face when interacting with enterprises, thus marking the inception of the Invisible App.
Fueled by conversational AI, this 1:1 communication channel connects you with customers in the snap of a finger and automates two-way conversations to fulfill tasks like onboarding, scheduling, order tracking, and much more. Through a link delivered via SMS, email, or the web, customers are taken to a secure, encrypted HTML5 container where rich experiences including video submissions, surveys, file uploads, and payments are all possible. Since there is no login required, the Invisible App is there when you need it, and when the customer engagement is completed, the secure channel closes.
Build as it requires no development resources on the enterprise side. Ushur’s web-based portal is the central hub for building, deploying, and tracking all automations visually. The drag-and-drop workflow builder requires no-code and champions creativity and customization.
After you’ve mapped out the experience you’d like to have with your customers, building the app simply requires the arrangement of the modules in the order you wish. Ushur’s APIs allow you to integrate with your CRM platforms guaranteeing that all data exchanged within the Invisible App is automatically updated in your backend systems. The Invisible App also offers white-labeling capabilities allowing full brand visibility for your enterprise.
Some other notable features include:
An app-like experience without the app development, the invisible app means the customer does not have to take any additional steps since engagement is possible via communication channels such as SMS. Additionally, the journey which is designed in the drag-and-drop workflow builder mimics the features of apps such as image uploads, file and document uploads, order tracking, and more.
In this age of overwhelming app proliferation, we wanted to create a refreshing take on two-way communication that takes the pressure off developers, while maintaining customer needs and expectations.
About 124.5 billion business emails are sent and received each day, and this number is only growing (DMR). Manual email triaging, the primary mode of email intake up until recently, is not scalable for such growth. Employees, on average, receive 121 emails per day (DMR). Spending approximately 3 minutes per email would result in 6 hours of labor just combing through emails. Moreover, employees whose jobs are dependent upon checking email (i.e. IT, helpdesk, customer service, etc.) receive mountains upon mountains of emails exceeding that of an average employee.
The crazy thing about this is that complex and time-sensitive emails to bulk email inboxes which necessitate diligent supervision and follow-through get muddled up with routine requests for address changes or password resets. This is why it’s paramount that enterprises begin automating their email intake with process automation.
Automating these processes frees up employees allowing them to hone in on other tasks which require their detailed attention and utilizes important human skills such as context, cognition, empathy, and sentiment.
Prior to digitization, a large life insurer we work with, had email queue times of 15 hours, all front-ended by a team of intake agents.
For this insurer, it took approximately 17 full-time employees to:
With SmartMail implementation, their emails are classified in just a few seconds. Furthermore, their triaging process before SmartMail was limited to the 8-9 working hours. Due to SmartMail’s unsupervised functionality, their email classification is now a 24/7 process.
At Ushur, use cases can be deployed quickly. The process begins when our customers specify their pain points, and define the areas where they are receiving the most repetitive emails and tasks that could be automated. After deciding on the use case, the Ushur team creates a custom demo to showcase how the Ushur platform can be used for the specific needs of the customer. Then, a proof of concept (POC) process begins.
For the POC, we take a sample of historical emails and strip away PII information using an anonymizer tool. This sample set acts as training data to augment to Ushur’s existing machine learning data. Our quick and efficient POC process is a limited scope deployment, and it takes about one to two months to complete. With the KPIs and goals in mind, the POC is tailored to meet the needs and expectations of the customer. During the POC process, customers go through training so that they can adopt the knowledge and skills to ensure the SmartMail technology runs smoothly post-deployment. After this process is completed, both the customer and Ushur analyze ROI and KPIs to verify all intended goals were met.
The process is simple. Emails flood the company’s inbox. Ushur’s technology uses AI, ML, and OCR and ICR to read email body and attachments. Then, the emails are either a) classified and routed to the appropriate destination (person or department) or b) classified with intent clearly identified to kickstart an automated Ushur workflow to accomplish a certain task (i.e. change of address, claim initiation, tax ID modification). Not only does Ushur’s technology have the propensity to automate email triaging, but the SmartMail technology can also reduce the manual labor needed to perform simple, repetitive tasks.
Email Intake Automation is a “must-have”
The best part about automating email intake processing is how flexible and customizable automation can be to your company’s specific needs. From Helpdesk automation which enables automatic resolutions, to new user/password reset, to address changes, the options for email triage automation are endless (helpsystems).
Interested in reading about how one of our customers, a large life insurance provider, implemented SmartMail for their email automation needs? Download the whitepaper below.
Business processes involve routine tasks that are getting increasingly complex with rapid technological advances. Workflow Automation can provide the relief to both the employee and the employer—to the employee opening them up for more creative and value-added work; the employer in cost savings, efficiency, and avoidance of errors. Workflow automation is about automating business processes based on workflow rules where human tasks, data, or files are routed among systems or people. Typical workflow automation examples include onboarding a new customer, adjudicate a quote request, handling an Invoice, process a new claim, etc.
Conversational AI is about leveraging the channels facing the customer, including messaging apps, speech-based assistants, and chatbots to automate communication and create personalized customer experiences at scale. The AI in conversation represents elevating the engagement to a new level of intelligence by appropriately introducing certain components, especially the Natural Language Processing units. At times, the processing involves Machine Learning and sometimes even Deep Learning. The key is to leverage the AI components at appropriate points in the conversational flow.
Combining these two powerful technologies (process automation and conversational AI) is the key to building a smart enterprise and happier customers. With these tools, major insurance provider has already seen ~12M in costs savings after the first year of deployment. Similar impacts are being felt in other industries as well.
Workflow automation is typically achieved by connecting software nodes involved in a repetitive process, capturing human operator’s visual movements on a screen and deploying them as being done by software robots.
An SMB can automate their workflow by connecting a cloud-based application like Google Sheet with their backend structured database. As the google sheets are getting filled-in with data, manually or automatically through other means, their backend database is getting populated for further internal processing. The database tables can then be viewed via a CRM application.
A large banking organization that is processing loan applications can employ software robots to take the information from the submitted loan forms that are on the screen and copy it into the next system to continue processing. This was originally being done by humans who were involved in the routine task of copying and pasting data across systems.
Conversational interface is about being proximal to the customer (the end-user). This interface is the conduit through which a customer is reached, content of their interaction handled, conveyed into a software-based system, stored into a database, used in computing, fetching other related information, and finally efficiently giving back to the customer what is relevant in that engagement. With the recent advances in machine-learning algorithms and with a set of relevant AI technologies, there is a cognitive dimension to the conversation and hence termed conversational AI. This now becomes the realm of NLP (processing) along with NLG (generation) and NLU (understanding), depending on the depth of the use-cases.
For the digital transformation to be on the right path, clarity, and priority in sequence of actions must be established in the strategy. As found in PWC article [1], in the modern era of advanced engagements, the customer is the first stop. They are the driving force for businesses. It is only then that the business processes kick in. So, here the conversations with intelligence drives the workflow automation giving rise to an era of intelligent automation.
Let us consider an example here. A user initiates an engagement by texting in a keyword to a well-known virtual number. This is done over SMS channel. This user is interested in securing a health insurance policy. The content in this engagement is fetched from the backend system and is presented to the user. The two-way engagement continues as a conversation while the backend system is continually engaged for further information. As part of this ongoing engagement, there can be information stored on the backend system. At any point the backend system may choose to reject the policy creation and the conversational interface will deliver that experience appropriately.
While the end-user has engaged, the backend system is kept in sync and may transition accordingly. So, we see here workflow automation on the backend system as well as in the front-end, along with the advanced conversations going on in a seamless flow. Regardless of the user input being voice or text, the user’s sentiments can be derived and fed back to the backend for appropriate actions. Instead of a plain old telephone call where the operator is picking up the call, support systems at the backend operation in this old paradigm is forced to enter data manually into a system (this is often much slower and prone to errors). Here, the cognitive elements of the conversation interface is now extracting the information (structured and unstructured data) and feeding it appropriately to the backend. Thus, the conversation together with the backend workflow acceleration leads to a fully unified workflow automation via conversational AI. As part of the ongoing engagements, a cognitive graph can be built and enhanced for future intelligent engagement handling with the end-user. The system is thus self-learning for ongoing improvement.
A successful deployment is one where the focus is not just on the efficiency of business processes, but also on the means of achieving it. This includes customer engagement; the mode of interaction that is the current trend; future-proof considerations of how the consumer is moving; changing paradigm of software, computing, advanced cognitive technologies, cloud deployments and so on. Instead of narrowly looking at the backend efficiency of automating processes alone, it is vital to holistically look at the use-cases and bringing in the right set of cloud-based solutions, which can withstand rapid changes in the software spectrum and societal trends.
Companies that are deploying Ushur’s Workflow Automation Solutions are primarily first focusing on the Conversational aspects of it. They fine-tune the message delivered to the end users based on the context of the engagement, clearly establish the purpose of the engagement with the user, are very specific in what they expect from the customer, they set reminders to the end users accommodating the users behavioral patterns and finally they convey to the end user if the purpose of the engagement is achieved. They also employ Ushur’s AI-ML modules at the right places in the conversational flow.
The companies choose their integration strategy based on their backend systems. Some have leveraged reporting capabilities of Ushur and using those reports have integrated customer data into their backend systems. Still, others have utilized Ushur’s API that taps into every AI-powered engagement Ushur deploys with the end user and thereby integrates those API hooks into their backend systems. The possibilities are limitless depending on the goals and strategy
I was at the Atlassian Team Tour event recently when a panelist commented how email has become a digital space where information goes to die. It is hard to imagine how much has changed since Blackberry made email accessible just 15 years to-date. Email was the tool that allowed us to move business communications from paper and phone-based communication to the electronic form, but it feels like it is past time for email to step aside for some things. It is time for a better way to “interact”, particularly one that lends itself for the modern workforce, for the mobile workforce and for anyone wanting to get their request unstuck from the pile sitting in an Inbox. There are better ways to accomplish fast and smooth IT workflows i.e. simple text messages.
The average time it takes to service a request in an enterprise is about 6 minutes. Yet, the average resolution time is nearly 48 hours. The majority of time lost in servicing a request is in the theatrics of information exchange between the requestor and the fulfiller and to extract the most urgent calls to action for either party buried in emails. Most of us can attest to the experience of an urgent approval that gets lost in the boss’s inbox and requires that special “Please Approve” email or text message.
In an age where we have distributed teams, collaborative efforts within systems of records form the backbone of agile teams. But service desk workflows relying on email can be long and cumbersome. While an email is not delivered any slower than a text message, user behavior has transformed in the way we react to a text, compared to the responsiveness to an email. In our world today, we can take days to respond to an email, but most text messages are opened and responded to within a couple of minutes.
The ubiquity of the mobile device and the small form factor of the mobile phone leads to a better user experience to consume shorter snippets of information. This is what the term micro-engagement means - a short snippet of information exchange over messaging. A conversation consists of one or more micro-engagements™. It is perfectly suited for the sachet sized information exchange age we now live with our penchant for micro-blogging, micro-learning, micro-service. So why not micro-engagement?
Micro-engagements™ are not just how we engage in a social context. Many of the trends adopted by consumers are eventually embraced by enterprises. As a matter of fact, when 50% of our workforce in 2020 will be millennials, the way they prefer to engage socially and professionally will not be all that different. Bringing Micro-engagements™ to the enterprise bridges the gap in the digital experiences that most employees are already engaged in their social contexts. Besides, micro-engagements™ bring real benefits for improving productivity, lowering costs, particularly by automating many of the tasks that currently served through human touch.
Want to be the hero of your company? Introduce SMS based micro-engagements™ into many more IT workflows so you can solve problems quickly and efficiently with users interacting with Jira Service Desk using simple text messages for a quick and powerful service desk experience.
We are launching our Ushur for approvals app in the Atlassian Marketplace that works with your Jira Service Desk for text-based approvals, so you don’t have to send those email or text reminders to your boss. With Ushur, your approvals are no longer getting buried in the pile of emails. Check out Atlassian.Ushur.com/learn-more to see how teams leverage Ushur for the most modern team experience.
Want to learn more? Text #UshurForAtlassian to 87487
AI and Chatbots are all over the news lately, with companies moving to automate their low-level, repetitive tasks in droves. With the sector relatively new however, some might remain confused as to how exactly chatbots work and whether the return on investment actually lives up to the hype. Does your company need to consider chatbots?
Do you have any departments spending 10+ hours a week on repetitive tasks?
Is your team struggling to keep up with your growing business?
Do you want to increase your conversions from your customer outreach strategy?
If you answered yes to any one of these questions, then your company could benefit from chatbots and see decreased costs as well as increased customer conversions.
Chatbots are a useful, efficient form of automated customer engagement. Bots are simple artificial intelligence tools that interact with your customers. Those interactions can be straightforward, like asking a customer to refill their prescription or provide missing information to process an insurance claim, or more complex, like having a customer troubleshoot a problem with their internet service or help them to make a product purchase decision.
Chatbots, using Artificial Intelligence and Machine Learning, are able to answer your customer inquiries as well as automate your sales & marketing messages via based on the conversation mapping you have created. The context and cognition is built from ingesting existing data from your business which can include: knowledge base, FAQs, case history, incident reports, or just plain old documents.
Companies have started to use chatbots in order to bolster and automate customer support functions, supercharge their marketing and sales campaigns, and streamline IT and HR service desk operations.
There are two main reasons companies are choosing to integrate chatbots into their customer engagement strategy. One, is to create and retain loyal and engaged customers and two, is to lower their operational tasks and costs at the same time.
The world is completely mobile. 80% of all internet traffic in the world is now consumed on mobile devices and the most ubiquitous form of communication for mobile is texting or messaging. In order to reach your customers and keep them, active and engaged, companies must effectively engage their customers on their mobile devices
In fact, Gen Xers and Millennials are now texting more than 100 times a day, compared to just an average of 0 to 1 phone call. More than 70 percent of Gen Z and Millennials say they sleep with their phones within reach, and automatically pick up their phones if they’re awakened in the middle of the night.
This is perhaps just one reason why chatbots and automated messaging perform at significantly higher rates than traditional forms of engagement like email and apps. Open rates for text message for example, sit at 98% with the majority opened within 90 seconds, while email is just opened 24% of the time. When you include a call to action in your text message, companies see an average click-through-rate of 36%, compared to an average of just 6% CTR in emails.
Companies that implement chatbots have seen significant reduction in operational costs when it comes to customer support and marketing techniques.
Call center costs for example, have been dramatically reduced because of chatbots, with companies reporting a 50% reduction in call volume since implementation. If you’re struggling to keep up with customer support demand, chatbots can be a great way to automate repetitive support functions and leave your reps to focus on what they do best.
Would you like to learn more about what your company can do with chatbots? Reach out to us at [email protected] with any questions you may have.
Companies with the best NPS scores power their business from making their service delivery front and center. Everything they do as a business focuses and delivers on the service experience for customers. Think of companies like Apple, Amazon, Costco which consistently rank in the top 10 for brands with the best NPS scores. These brands invest in infrastructure and technology to deliver outstanding customer experiences, and customers in turn reward these brands with their loyalty and active brand promotion.
But service delivery remains an afterthought for most companies. Historically, it has been an expensive proposition to deliver service because the bulk of the service has been driven by expensive human capital. It’s estimated by Metric Net that each minute of a customer service call costs on average $1.03 and each agent receives an average 2,202 calls per month. Hence, investing in service delivery is often looked at as an avoidable, costly overhead and companies consequently make it difficult to reach their customer service teams, with 75 percent of customers stating that it takes too long to reach an agent when calling for customer service.
The focus has been to avoid human touch (or any touch at all) rather than solving the problem. Companies would be surprised to find out though, that if customers were to have a choice, they prefer self-service rather than talk to a person.
Companies avoid effective, comprehensive customer service because they think it will cost them in extraordinary expenses of human capital, when really they could solve for customer needs and wants without any human capital at all.
Because of the power of automation and the rise of bots, it is now possible and practical for companies to make service delivery front and center. And this is not just for service delivery for external customers. It is an equally important KPI to deliver an awesome service desk experience for internal customers too. Productivity improvements with a great service desk experience for an enterprise translates to happier employees which translates to better products and services delivered timely and cost effectively to external customers.
It is now possible to scale a business without having to scale spending on human capital. Thanks to platforms like ServiceNow, SalesForce and others who are making programmable systems of records easy to deploy with software as a service. The race is on for a holistic platform play that can cater to the breadth of the enterprise needs – be it internal or external use cases.
Which is where service “bots” have a huge potential. The ability to engage and automate service delivery for internal and external customer so that you can reduce service outages, manage sales ops, automate customer engagement, streamline order management, automate the approval process, automate IT operations, and all this at a fraction of the cost of how it was done in the past. Replace expensive and proprietary hardware solutions with SaaS and divert a big fraction of the engagements to service bots instead of handling it all with expensive human capital.
The ServiceNow Knowledge 17 Conference happening next week in Orlando will be a great opportunity to learn more about service automation and deploying effective customer service strategies.
I started dictating this article to Siri, but I gave up within a few minutes after I had to repeat my words again and again. Emerging Human Assisted Artificial Intelligence (AI), Virtual Assistants, and Machine Learning technology has become a reality everywhere. We use them to check the status of our orders, reorder a prescription or book our trips. While chat bots enhance and optimize company function, bots assisted by humans are shown to be more effective for businesses, at least for the time being.
While these bots are ambitious, they’re not humans yet. They rely on scripted, command-responses, which can quickly get things wrong as they deal with more complex issues and the user forgets that they are interacting with a machine and start the path of natural language in the context of that specific instant-message interaction. There is no doubt that instant messaging is the preferred way of communication for most of us. We prefer instant messaging to a phone call in a 5:1 ratio and Millennials on average send 67 text messages per day, and preferring to go to a dentist rather than phone a retailer!
With the continuous improvement of AI, Natural Language Processing (NLP) and Machine Learning (ML), we should be able to make these interfaces work much better. Despite the impressive number of chatbots deployed – chatbots on Facebook are growing at a faster speed than the apps on Apple Store in its early days – we still need to overcome some challenges, as chatbots are still in their infancy.
Gartner predicts 85 percent of customer business relationships will be intelligently automated by 2020, while a Forrester survey found that almost seven of every ten contact centers expanded their number of seats in 2016 to meet customer demand. Companies are automating customer relationships with chatbots while increasing their agents as well.
Can humans co-exist with chatbots? The answer is yes. Leading companies report a double-digit increase in marketing conversion rates and operational cost reduction when they automate low-value, repetitive functions and program their bots to escalate issues where necessary, leaving their employees to focus on complex problems and deliver the best customer experience possible.
There’s evidence that this automation is already happening and working. Just one of every fifty calls to local businesses involve asking for directions, hours, and account balances these days, as the answer to those questions are handled by technology.
80 percent of the population accesses the internet from their mobile device. The always-connected mobile consumers expect immediate responses from business services. Three of every four customers believe that it takes too long to reach a live agent and Accenture found that half of those customers stated that a better mobile service experience could have kept them from switching companies. This is where chatbots can be of great value, as the response is immediate. Instant-messaging is also the communication mechanism preferred by the average customer these days. And finally, chatbots can escalate issues as necessary, freeing up your customer service reps for important, timely issues.
Human-assisted Chatbots have a substantial impact on the bottom line of companies. The data shows that customers expect to communicate with businesses in the same way they interact with family and friends. Instant-messaging is a powerful communication channel, and Chatbots are a fantastic way to work alongside customer service and sales reps; while AI and NLP continue its relentless advance towards a robotic future.
ServiceBots could be a solution to some of the current woes of the retail industry. Using instant-message-based systems, these bots can automate many of the functions of a retail operations service desk, bringing a conversational machine interface to what is a costly human-to-human interaction today, thereby reducing the need for scaling human resources, while bringing faster and friendlier support to store employees.
Retailers face increasing business challenges, not the least being staying competitive in the challenging environment of their brick and mortar stores. What happens inside the store may be the weakest link in the chain, when it comes to operational efficiency and customer satisfaction. Forward-looking retailers want to empower all their staff with tools and resources that fit neatly into the daily routine of the store and do not require specialist equipment and training.
Store employees already have complete mastery of their own personal mobile technology. Now there’s a way for retailers to offer their staff a communications tool that utilizes these capabilities to reduce the friction of managing the store, and deal with any issues that may come up, in real-time via mobile devices.
What are some of the common culprits that reduce store efficiency and discourage customers and staff?
Imagine if the moment there was an issue with store equipment, a staff member could engage with Corporate, report the issue, and receive updates on how fast the solution would be fixed. For instance, if a cash register goes down on a busy Saturday morning, the manager can report the issue via text message, to alert the Service Desk that the cash register needs to be fixed. Once the issue is logged as a support ticket, the Service Desk can send a schedule of when service personnel will come to the store to repair the register. In the meantime, the manager can set expectations with the store team as to when it will be fixed.
For incidents involving store-related software systems, such as stock systems, security tag systems, or access to employee-only areas of the store, Service Bots can send a resolution confirmation to the store manager to confirm that the issue is actually resolved.
Beyond store equipment and software, there are also ways that ServiceBots can support the day-to-day interactions of employees with Corporate. For example, on the sales floor, store employees don’t usually have access to a computer, and face-to-face training minutes are generally reserved for the highest priority safety and compliance training. However, there’s a host of other information and training that ServiceBots can deliver via instant messaging.
These engagements could take place between the Retailer and the employee at the employee’s convenience. By using simple dialogs, employees can conduct HR activities like choosing benefits information, taking a video based training course, or checking on staff rosters, all on the device of their choice.
All of these interactions can occur on any mobile phone without downloading an app or without the need to install client software. Retailers design workflows to fit their company processes and they can be scaled to national or even international employees.
The promise of ServiceBots is to automate many of the functions of a retail operations service desk, bringing a conversational machine interface to what otherwise is a costly human-to-human interaction today.