Recently I fielded a question about NPS survey data: How do you process it? How do you use it? I popped off a quick response, but I’d like to go a little deeper on the topic here.
Everyone loves a good story. Insights teams need their work to be understood by their audience. In this article let’s explore some fundamental principles behind effective storytelling so your next feedback analysis is understood, actionable and has a tangible impact on overall customer experience.
Much has already been said about the limitations of NPS as a measure of customer loyalty. Where NPS falls down is clearly in the way it buckets customers into three broad categories of promoters, passives and detractors.
Many text analytics tools sell the dream of understanding customer feedback at-scale. More often than not, you’re still training that software to look for what you think is important - those known knowns. What you should do is be proactive about those unknown unknowns.
Insights teams are judged by their ability to provide high impact insights to decision makers that they can use to decide what to do next. Relying solely on unweighted data could jeopardize your ability to do that.
If you're using a traditional text analytics tool today, ask yourself this: how long did it take you or your vendor to add a COVID code to your text analytics ontology?