The Tethr Effort Index (TEI) is an AI-based effort metric, measuring the effort for each customer interaction based on more than 100 independent variables and thousands of discrete phrases and utterances. Rather than waiting for low-response rate surveys to measure effort, TEI scores every customer interaction so managers can instantly deliver critical customer interventions and help agents upgrade their game.
The birth of an AI-based effort metric
Tethr entered a partnership with the original Effortless Experience research team at CEB (now Gartner) in 2015. The team was led by Tethr’s current Chief Product and Research Officer, Matt Dixon. They spent time with Tethr specialists training the platform to build what would become the market’s first native Effort library.
The Tethr data science team spent years combining conversational data with survey responses from tens of thousands of customer interactions across a wide range of companies and industries to build TEI. An exhaustive list of Effort-related machine learning categories was created to serve as potential independent variables inside the model. Variables related to “do” effort (the actions customers take to get their issues resolved, such as calling back repeatedly, switching channels, and repeating information) as well as “feel” effort (frustration, confusion and unmet expectations, etc.)
A richer data set through the power of AI
Using recorded conversational data yields a far more comprehensive data set. Customers go to incredible depth about what went wrong in their experience during the interactions. They specify exact scenarios, specific error messages, and their real-time feelings. With this contextual data, the Tethr team generated a massive list of potential measurable variables.
Effort drivers can be represented thousands of ways in language, making it difficult to identify with simple keyword spotting. A transfer is not the same as a long hold. An agent hiding behind company policy is not the same thing as an agent promising to deliver on expectations that they can’t guarantee. How do you pinpoint when a customer becomes a churn risk?
With an AI-based effort metric, results are instant
The Tethr Effort Index is based on 250 variables representing thousands of discrete phrases and utterances that are proven to be directly related to effort. TEI can score any conversations, including those it has never seen, all out-of-the-box. Businesses can use TEI to immediately start identifying problems as soon as all interactions are ingested into the platform, rather than depending on customers to provide survey responses.
What makes for a high or low score?
Much of what determines a low or high TEI score is based on what happens between an agent and a customer during an interaction and the way an agent responds to a customer issue. This includes the language they use as well as at what point during the conversation they say it. Most organizations using TEI will see a normalized distribution of scores across their interactions, with approximately 10-15 percent of their interactions scored as “difficult” with a TEI score of 0-4, 10-15 percent of their interactions scored as “easy” with a TEI score of 7-10 and approximately 70 percent of their interactions scored as showing a “moderate” level of effort.
How do we know it’s helping businesses coach agents?
Our model validated the idea that creating an effortless experience means coaching agents to understand context by requiring a framework of multiple skills that must be used together to shoot for an effortless experience for your customer. When examining individual agents, research shows that companies can diminish effort and increase TEI scores by as much as 37 percent by moving below-average performing agents to above-average, mostly by coaching for multiple skills. Managers can get insights to help coach their call center reps up to their ultimate performance.
Doubtful? Schedule a demo to see our AI-based effort metric in action.