Net searcher sentiment: A web-search based replacement for net promoter score

Abstract

In 2003, Bain consultant Frederick Reichheld published an article in Harvard Business Review arguing that the best predictor of top-line growth can be captured in a single survey question: ‘Would you recommend this company to a friend?’ In the almost 20 years since, Reichheld's question, referred to as net promoter score (NPS), has become a standard metric for many organisations, complementing and even replacing more comprehensive customer satisfaction surveys. Yet, just as Reichheld argued in 2003 that companies were measuring the wrong thing, NPS itself may be the wrong measure. As documented in recent literature, NPS captures self-reported attitudes versus behaviour, is prone to sampling bias and runs the risk of non-participation bias. This paper explores the creation of a new metric, net searcher sentiment (NSS), that replaces NPS's reliance on survey data with aggregated search data. This paper lays out the benefits of NPS, its shortcomings as documented in the relevant literature, the methodology behind NSS, how it addresses the shortcomings of NPS, and provides two examples of NSS in action.