Advanced predictive models can infer when a customer is happy (or not)—and then help you take action.
Today's customers are bombarded from every direction by companies vying for attention. As such, any given Net Promoter feedback request may have response rates in the low single digits. Even though a single piece of feedback can still fuel the Net Promoter System’s inner loop, having so few responses also means that you are virtually blind to what the majority of your customers perceive. But, many companies are sitting on vast amounts of customer data that can signal how they’re feeling—not just explicit feedback.
Enter Predictive NPS (sometimes called Signal NPS). The idea is simple: Predictive NPS infers a customer’s NPS status (promoter, passive, or detractor,) even in the absence of a survey, by using advanced analytics tools to process and model all structured and unstructured data about that customer. Bain has worked with companies to design and build Predictive NPS models, and the results are impressive: accuracy of over 80% for predicting NPS status and a fourfold lift above baseline in identifying who your detractors are.
Armed with the knowledge of the hidden detractors or passives, the best companies can proactively intervene to make the experience "right" for the customer. And better yet, knowledge of who are the hidden promoters provides a chance to activate these loyal customers to help you generate more organic, exponential growth.
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