Think about the last time you decided to stick with a company, repurchase a product or even increase your planned spend. Can you isolate one single reason – one individual factor above any other – that drove your decision? Customer decisions are almost always made for a multitude of reasons, inclusive of the entire CX journey and preconceived expectations.
Moreover, that purchase and your perception were likely influenced by a number of experiences engineered by the company behind the product – ranging from frontline sales, service rep behavior, information conveyed via the web, to in-store signage. All of us can point to experiences that dramatically impacted how we felt after time spent with a company, both good and bad.
Beyond the ‘what’
Measuring customer experiences amidst this complexity of influencing factors has always been difficult. Unfortunately, even today, many solutions profess an ability to surface conversation insights while falling short of isolating why customers feel the way they do. It’s difficult to fix problems you can’t diagnose.
Absent insight into why, most fall back on measuring what – using the mere presence of words to extrapolate sentiment. While it’s helpful to know the percentage of frustrated customers, CX leaders need a finer lens to make the tough decisions about which levers to pull, which plays to call and which investments to make in order to achieve the desired outcome. Therefore, why something is said is more important than what was said.
Similarly, it’s helpful to know whether a customer objected to a sales pitch. It’s far more helpful to know if the objection was in response to the old or new sales pitch, whether it preceded or followed the new warranty offer or whether subtle pitch tweaks or discount offers changed the objection, in substance or frequency.
Conditional categories for the win
This is the next level of our contextual insights that we are excited to release with our new capability, “Conditional Categories.” With conditional categories, our customers are able to gain a greater understanding of the complexity of conversations by honing in on specific interactions between agents and customers. Once employed, this capability allows users to start measuring key sequences within conversations that profoundly change our understanding of the “why” behind customer perception.