AiThority Interview with Brad Anderson, President of Product and Engineering at Qualtrics

Hi, Brad, welcome to our Interview Series. Please tell us a little bit about your role and responsibilities at Qualtrics. How did you arrive at the company?

I spent over seventeen amazing years at Microsoft where I learned from some incredible people. As I worked with CIOs and enterprises around the globe to modernize their workplace, I saw first-hand that the industry was reaching an inflection point in terms of how companies could use data to transform their business. What got me excited to join Qualtrics as President of Products and Engineering was the opportunity to dig in, learn more and contribute to the category of Experience Management (XM) that Qualtrics is defining and building. As an added bonus, it gave me an opportunity to return to my home state of Utah and become more hands-on in accelerating the growth of the tech sector in the state, in addition to Seattle where we have our second HQ.

What has changed at Qualtrics AI team in the last 12 months?

Investing in AI is no longer optional. As the scale of business continues to grow, it is exceedingly difficult or arguably impossible to manage experiences with sheer manual human horsepower.  When it comes to developing our AI, in 2022, Qualtrics analyzed 2 billion conversations and 1.6 billion interactions on the XM platform. 10 billion experience ID profiles were updated with real-time customer and employee insights…. that’s doubled from 5 billion in less than a year. And we executed 2 billion workflows in real-time.

Many businesses rely on digital systems and large data repositories to automate this work. However, automation threatens to remove the personal touch that we crave. AI is helping us bring that humanness back into the picture by empowering the frontline and inspiring the back office with the information that they need in the ways that they need it delivered with less effort than ever before. It’s not about replacing but rather augmenting humans to be the best versions of themselves even when they themselves are resource-constrained.

That approach comes to life in some of the recent products we’ve launched in the past year including Real-Time Agent Assist, which uses AI and Machine Learning to analyze customer needs and emotions, then deliver real-time coaching so contact center agents can take the best next step for customers.

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There is so much buzz around generative AI and LLMs. Please tell us how you empower your customers with Qualtrics AI and real-time data.

When accurately tuned to specific industry challenges, Natural Language Generation technology provides the opportunity to intelligently surface information in accessible formats, at a much lower cost for individuals and organizations.

I mentioned Real-Time Agent Assist. We pair that capability with something called Automated Call Summaries, which is a perfect example of a purpose-built generative AI solution.

Customer service agents spend loads of precious time on tedious, manual work like writing up a call summary or logging follow-up actions after each and every call. This work contributes to longer call wait times and adds hours of additional expenses to the business.

Automated Call Summaries uses Qualtrics AI to deliver instant, accurate automated call recaps that include all relevant details discussed during the call, including why the customer called, how the call went, whether the issue was resolved, how much effort was needed to reach that resolution, and what steps still need to be taken.

Accuracy is obviously the big question when it comes to capabilities like ChatGPT. When we worked with our preview customers, we found that their Automated Call Summaries were actually more accurate than previous manual entries, which often lacked important details and were highly inconsistent from agent to agent.

That is just one example of how we are empowering our customers through our AI.

Contact centers are facing perennial burnout and depleting productivity. How would bringing Qualtrics AI to enhance frontline performance and cut down operational costs?

If we automate a part of an agent’s job, we ought to replace that time with something that adds value to their experience. New technology should not be about automating people out of jobs or focusing exclusively on the bottom line, but an opportunity to actually elevate the agent experience which in turn elevates the customer experience.

In addition to Real-Time Agent Assis and Automated Call Summaries, at our recent X4 summit, we launched two new solutions that will enhance frontline teams.

Our new Qualtrics Frontline Agent Coaching solution extends the power of a solution we announced last year, Qualtrics Real-Time Agent Assist, and gives agents critical post-call coaching and performance insights. Agents automatically receive insights on their performance once the call is over and they get personalized coaching on how to improve their performance. It gives agents more control and clarity into how to improve their performance and provide a better, more empathetic customer service experience.

On the flip side, being a customer care team manager is HARD. They’re in charge of hitting team KPIs, CSAT goals, and employee engagement. It’s a big load to carry and they don’t always have the right data to inform how to invest their time and resources.

That is why we created Qualtrics Frontline Team Assist, a new purpose-built solution that takes Quality Management, customer interaction, and customer feedback insights and distills it into one view that helps customer care leaders know where to focus their efforts and provide better feedback to their agents.

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You are one of the leading software companies that offer AI to measure behavior and sentiment to enhance customer experiences. Is this technology full-proof as far as accuracy and trustworthiness are concerned?

More than ever before, having the most sophisticated algorithms is not sufficient. The cost of entry has shrunk due to the commoditization of many algorithms and the decrease in the volumes of training data required to get started.

Instead, differentiation comes from unique input data and proprietary features. Qualtrics has both: an unrivaled corpus of human experience data and deep domain expertise from two decades of engagements in research and enterprise experiences. These inputs yield highly differentiated ML outputs producing smarter, more mature AI solutions than our competitors.

Please tell us a little bit about Experience iD and how it improves omnichannel customer interactions?

Everything starts with Experience iD (XiD) for us. With XiD, we can provide a comprehensive view of a customer and their overall sentiment and opinion of a brand, from every interaction they’ve had across channels.

That is the key. Experience iD can pull in any experience data, from any channel — structured data from things like employee pulse or CSAT surveys, or unstructured data from public channels like social media and review sites or call center conversations.

With our platform, our customers can combine behavioral, experience, and operational data in Qualtrics Experience iD, providing a complete picture of the customer experience that helps their teams take the right action and remove friction across any frontline journey, from digital experiences to customer care, accelerating revenue and reducing costs.

What are the biggest barriers / challenges in taking AI and data analytics to every team for total digital transformation?

The biggest challenge to true digital transformation is the silos that exist between teams in an organization.

Take the digital experience as an example. Traditional web and product analytics provide some level of insight by translating consumer behavior into metrics like clicks, bounce rates, page views, conversions, and other abstractions.

But this is not enough to really understand why customers do what they do over digital properties, and feedback is typically siloed and not shared across product, marketing, UX, DevOps and customer care teams.

To design more effective digital experiences, brands need a digital tool to observe and replay their most important customer behavior and contextualize those experiences alongside operational data, customer sentiment and personalized feedback.

Companies need to bring that data together, and make it available and actionable across their marketing, customer care, and product teams. It’s a huge challenge that requires both the right technology and buy-in at the top of the organization.

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Your advice to CEOs and CMOs looking to build a strong AI-powered customer experience model for their organization:

In many cases, simpler algorithms can produce amazing results with lower costs. Look for purpose-built solutions –the more practical experiences a business has, the faster we can connect the dots between concepts and anticipate successes or failures.

The goal is to deliver accurate, relevant customer insights. Look for solutions that augment ML with human defined, rules-based algorithms based on a deep knowledge of the contact center and customer experience space. Ultimately, the combination of rules and probabilities helps a business navigate known and unknown scenarios with confidence.

Thank you, Brad! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]