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?
A lower than expected NPS is not a reason to be discouraged, in fact, it’s the perfect catalyst for reinvigorating your business and your journey towards a truly customer-centric approach.
Your customer insights team should combine qualitative and quantitative data to gain a holistic understanding of the ‘why’ behind customer feedback. Here are a few suggestions....