Several years ago, back when I was running the Customer Experience Practice at CEB (now Gartner), we set up a fascinating research partnership with a little-known AI venture out of Austin called Tethr. The idea was to take many of the concepts we had discovered over the course of the research that went into our book, The Effortless Experience, and to see if we could teach the concepts to a machine, effectively scaling effort-based listening for companies.
First, a quick refresher on the research: our team at CEB found that in a global survey of 100K service interactions that customers whose expectations were exceeded were actually no more loyal than those whose expectations had simply been met. We also learned that any service interaction (i.e., issue resolution) was far more likely to drive disloyalty than loyalty—four times more likely, in fact. What drove that disloyalty effect was a set of what we called “customer effort drivers”—things like repeat contacts, transfers, channel switching and generic service.
The punchline was this: rather than trying to make customers more loyal by “wowing” them in a service moment, companies should instead try to mitigate disloyalty by making the service interaction a low-effort one. In other words, play great defense and the way that you accomplish that is by making things easy for your customers. When companies do this, the loyalty impact is huge—customers who experience low-effort service are far more likely to repurchase and spend more with companies and are far less likely to say anything bad about those companies. What’s more, being easy to do business with is a heck of a lot cheaper for companies—37%, in fact.
We started this research project in 2007 and spent the next six years collecting data and digging deep into the management practices and processes that low-effort companies. We previewed our findings in a 2010 Harvard Business Review article called “Stop Trying to Delight Your Customers” (an article in which we debuted the Customer Effort Score) and, in 2013, we published The Effortless Experience, which was our attempt to boil everything we’d learned over the preceding six years into a set of principles and guidance on customer effort reduction for managers and leaders.
But as much as we put a “bow” on the customer effort research, effectively marking the end of the journey, it turned out to be only the beginning.
In 2015, I was introduced to the founders of Tethr, an AI-based conversation intelligence platform. The engineers at Tethr had figured out how to mine recorded customer calls for actionable business insights. Companies have recorded their calls “for quality assurance and training purposes” for years but have found it difficult, if not impossible, to tap into the data trapped in those calls because of the heavy manual labor needed to do all of the listening that would be required. Using automated speech recognition, natural language processing and machine learning, Tethr had built a platform that enabled companies to finally “listen at scale,” effectively shining a bright light onto this dark data asset.
The Tethr team approached our team at CEB with an interesting opportunity: teach their machine to understand what effort sounds like so that it could spot moments of effort (and therefore, opportunities for improvement) across the millions of customer conversations being processed through their system on an annual basis.
For our team at CEB, this was a fascinating and unique chance to take concepts we’d identified in our research to see how they manifested in real, live-fire customer interactions. We jumped at the opportunity and sent a team of researchers to Austin, Texas to work with their data scientists and speech analysts to begin teaching the machine.
Even though we understood, theoretically at least, the power of AI and machine learning, we were nonetheless shocked by our sudden ability to prove out concepts that had taken us years to develop at CEB in a matter of days or, in some cases, hours by using the Tethr platform. Not only could we test the Effortless concepts with real data from customer conversations with incredible speed and at scale, but Tethr enabled us to define them in far more precise ways than we ever did before—the words and phrases that represent the concepts—making the concepts themselves far more actionable for a company. And, along the way, by working with Tethr’s customers, we stumbled on many surprising expansions of the original Effort concepts—things that we never found in the research but are nonetheless critical elements of the Effort framework.
Drinking the AI Kool-Aid myself, I made the decision to join Tethr as their Chief Product and Research Officer in May of 2018 and thus began, for me, a fascinating journey into “Effort 2.0.” Across the next few posts, we’ll share some of the fascinating things we’ve found in our work at Tethr. If you’re an “Effort junkie,” you won’t want to miss these posts!
Contact us to learn more about how we’re translating effort concepts into human language using the power of machine learning.
The Effortless Experience team at CEB that I mentioned earlier is now part of a standalone company named Challenger. This team is a great resource for anyone looking to learn more about the Effortless Experience research—and how to develop effort-reduction skills at the frontline. They work with organizations around the world on this sort of stuff every day and have a wealth of experience and insight to share.