The next phase of AI innovation will drive personalization at scale and help organizations efficiently deliver seamless and personalized experiences.
Interest in artificial intelligence (AI) has surged recently because of publicly available, user-friendly generative AI. These innovations will revolutionize the experience industry’s approach to solving persistent issues – growing data loads, shrinking budgets, scaling insights, encouraging adoption, and more.
Medallia has been building thoughtful, actionable, and native AI and machine learning capabilities since the launch of Text Analytics in 2008. Medallia’s Natural Language Processing (NLP) and Natural Language Understanding (NLU) capabilities now support hundreds of languages, action-oriented machine learning models, and over 15,000 pre-learned topics, enabling personalized experiences.
Since then we have continued to build upon our AI capabilities in speech, text, and more to provide businesses with actionable, democratized insights into experiences. Earlier this year, we surpassed more than 1 million active weekly users who are leveraging our powerful AI-driven capabilities today.
With the depth of our data, 15 years of AI expertise, and our leadership in people-oriented text analytics, we are now developing the next phase of AI innovations to provide unparalleled personalization of experiences.
Wouldn’t it be ideal if any user of your experience platform could get exactly the insights they want with minimal training and effort? While AI and machine learning (ML) have made analyzing large datasets far easier, the power to surface insights from that data is often still concentrated in the hands of users who are more comfortable with statistics and analytics. The result is slow time to insight throughout the organization and difficulty seeing and acting on that vital information.
For example, a regional manager has limited time to figure out what’s bringing down their customer satisfaction metrics. They certainly do not have time to wait for help to update their reports or to get an answer to their questions. AI should empower that regional manager — and other users — to get generated, predictive insights into their experience data using simple queries or prompts. That manager should be only one question away from knowing why some of their locations are performing better than others; AI should be able to surface the response to that question immediately.
Eventually, as the AI learns any user’s specific needs, prompting and querying should become an optional step. Instead, the AI should predict and tailor the insights that the user wants and needs to see in order to perform their role well. AI should eliminate the steep learning curve on performing root cause analysis on what is happening and why by providing intuitive and easy-to-use tools that let you interact with your data.
Generative AI is a principal technology that will help achieve this goal, but it will not be the only type of AI that will be valuable to this analysis. Predictive analytics — analytics that use data to predict future trends or events — should be built upon generative AI and work together with other methods. These AI-powered predictive analytics should enable businesses to tailor experiences based on those predictions, customer preferences, and other metrics, ultimately to achieve quicker issue resolution and increased satisfaction.
Such analytics should be able to score the unscored, that is, predict and score the likelihood of a customer or employee taking a certain action if we do not act on a given issue. For example, the AI should be able to identify the patterns in interactions and surface your biggest risk factors leading to customer churn or another outcome you are interested in understanding — all in the moment.
As budgets get slimmer and customer demand increases, every company needs to find novel ways to automate processes to deliver consistently high levels of service while also mitigating employee churn. AI-augmented automations are a win-win solution for employees and customers struggling with these exact issues.
For example, contact center agents have extremely limited time to do all of the tasks required of their job. Wouldn’t it benefit agents to automate post-call notes or be automatically served a summary of the customer’s previous interactions with the business so they can provide faster, better service? Customers would get the answers they want faster by reducing the time needed for employees to solve issues. Employees’ workloads and stress could be reduced in small ways with a big impact on the bottom line and customer satisfaction. Automatically summarizing a customer support interaction can save every agent anywhere from 15 to 60 seconds after every call or chat. Our goal is to automate the mundane, not eliminate the humane.
But the potential impact of AI-powered automations extends beyond contact center use cases. AI should help automate tasks in a way that frees up human intelligence and resources for the most value-add or complex tasks for every employee — at the same time as reducing costs. AI should augment human workflows and improve working conditions, while identifying and then enabling people to work on interactions that are of higher value, require human intelligence, and are more fulfilling — all in less time. At the same time, AI should scale to provide automated summarization, categorization, responses, and other enhancements that will help maintain consistency across every experience program.
These automations should also enable users to edit how AI generates personalized responses or other generated content to ensure compliance with company policy, such as limiting the number of coupons given to a customer or the allowable percentage discount for detractors. Employees would be able to relax knowing their generated responses are compliant, as the AI consistently learns what is acceptable from both other employees’ behaviors and administrator configuration of the AI. Moreover, AI should provide context and suggestions to employees, identifying next-best conversations or actions so employees can enact them automatically or manually. The result? Consistent, personalized interactions with customers across experience programs. AI-powered automations should thus provide every customer with a personalized experience, regardless of whether those automations require human intervention or not.
We’ve touched upon personalization a few times already, and there’s a reason for that. Customers are used to providing their feedback or other data to businesses both implicitly and explicitly, whether that be through chats, their digital behavior, survey responses, reviews, social media — you name it. Why shouldn’t they expect that data to be used to make their experiences more seamless, personal, and better with your brand?
That could mean automatically generating comprehensive, contextually accurate responses and next-best actions to customer feedback. AI should further enhance future experiences customers have with your business by being fully aware of their previous experiences and providing next-best actions accordingly. If a customer owns the current generation gaming console you sell, they would be annoyed if all your marketing is for a gaming console (or supplemental device/service) that makes more sense for a last generation console. By leveraging AI to personalize experiences, your business could increase brand loyalty, providing only the most relevant information and items to your customers.
This should include AI that surfaces insights into behavior from non-language data, like digital or video. AI should be able to uncover patterns in digital behavior and use that to help customers by providing specific help articles or links. The time savings alone could be huge for customers and employees. But looking out even further, AI could make sure that customers get exactly what they want before they ask you. If a hotel guest always asks for information about the gym, why not automatically send information about the gym — as well as extra towels to their room? Imagine this level of personalization, scaled across the entirety of your business.
We envision that all these AI methods will maintain a feedback loop to continuously refine AI models and provide ever-greater personalization to supercharge employee and customer satisfaction. AI should adapt to new information at every point in a journey, dynamically training itself with the data generated at each step across every signal. The end result should be unparalleled personalization for everyone who interacts with your business. The possibilities are endless with the right vision, expertise, and AI that enable infinite tuning and constant, adaptive personalization.
We aim to create AI advancements that will expedite decision-making processes and improve overall efficiency, accessibility, and user-friendliness of insights, driven by generative AI and predictive analytics. We also intend to do so responsibly, taking into account the relevant regulations and security standards of our customers, while also remaining transparent about how data is used.
AI-powered and AI-augmented automations will alleviate employee workloads while preserving the essential human touch in interactions, promising enhanced loyalty and satisfaction. Central to these advancements is the commitment to personalization, with AI generating context-aware responses and actions based on customer feedback and behavior, ultimately cultivating brand loyalty and more positive and personal experiences.
Our vision is supported by decades of experience, billions of data points, and design philosophies that have enabled us to build safe, enterprise-grade, and scalable solutions.
To learn more about the present and future capabilities of AI and personalization, join us for an interactive discussion with Babak Hodjat. Hodjat is the inventor of the technology behind Apple’s Siri and current CTO-AI, at Cognizant Technologies.