Net Promoter Scores gauge customer loyalty, expressed by willingness to recommend and advocate the company’s products and services to others.
Its creator, Fred Reichheld of Bain & Company, posited that NPS is a more meaningful measure of a company’s relationship with its customers than customer satisfaction metrics because, he argued, it is correlated with revenue growth.
Satmetrix Executives Richard Owen and Laura Brooks further articulated this linkage between customer loyalty and revenue growth.
NPS’s customer loyalty metric is based on 10-point ratings in response to just one question: How likely is it that you would recommend our company/product/service to a friend or colleague?
“Promoters” respond with a score of 9 or 10 whereas “Detractors” provide ratings of 0-6, and scores of 7 and 8 are ignored in this system, leading to the question of why they are included.
NPS is calculated by subtracting the percentage of customers who are Detractors from the percentage of customers who are Promoters.
Critics, including Ipsos Loyalty’s Timothy L. Keiningham, Bruce Cooil of Vanderbilt, BI Norwegian School of Management’s Tor Wallin Andreassen, and Lerzan Aksoy of Fordham, argue that American Customer Satisfaction Index (ACSI) is an equally accurate predictor of revenue growth.
They reinforced the frequently-replicated finding that actual behaviors, including positive and negative “word of mouth (WOM)” are better predictors than attitudes about possible future behaviors, in their evaluation of longitudinal data from 21 firms and 15,500-plus interviews from the Norwegian Customer Satisfaction Barometer.
Likewise, University of Michigan’s Claes Fornell, Forrest V. Morgensen, and M.S. Krishan, with Sunil Mithas of University of Maryland, found that “it is possible to beat the market consistently by investing in firms that do well on the ACSI.”
Companies that invest in initiatives to increase customer satisfaction, reflected in higher scores than competitors on the American Customer Satisfaction Index (ACSI), also performed better in measures of market value.
More surprisingly, they found that these higher returns are associated with lower stock market risk, probably due to “stock market imperfections” that require time to adjust to news of strong ACSI performance.
Similarly, customer satisfaction and loyalty researcher Bob Hayes contended that “likelihood to recommend” measures the same construct and has the same predictive value of business growth as customer loyalty questions such as:
- Overall satisfaction
- Predicted likelihood to purchase again, evaluated through his Purchasing Loyalty Index (PLI)
- Number of referrals through “word of mouth” and “word of mouse,” calculated in his Advocacy Loyalty Index (ALI)
- Resistance to defection to competing offers, measured with his Retention Loyalty Index (RLI).
Hayes’ findings reinforced the caveat that actual behavior is a more accurate than attitudes about likely future behavior, also demonstrated by University of Connecticut’s V Kumar, J Andrew Petersen and Robert Leone in their analysis of telecoms and financial service customers willing to recommend their service provider.
Only about one-third of these potential Advocates actually recommended the provider, and only about 13% of those referrals actually led to new customers.
Kumar and team called this the “promise gap” and suggested that it can be mitigated by delivering beyond customer expectations, even when a customer complains.
Indiana University’s Neil A. Morgan and Lopo Leotte Rego of University of Iowa added a wrinkle to critiques of Net Promoter Scores as the sole necessary indicator of customer satisfaction.
Like Keiningham’s team and Hayes, they found that recommendation intentions (“net promoters”) have “little to no predictive value.
Unlike Hayes, their results found little predictive strength for actual behavior in average number of recommendations.
Instead, Morgan and Rego argued for multiple measures of customer satisfaction as the best predictor of revenue group.
Additionally they found that Top 2 Box satisfaction scores – the sum of percentages for the top two point on surveys of purchase intent, satisfaction or awareness – provided “good” predictive value.
The Net Promoter Score also had the lowest predictive validity when compared to three other scales by Stanford’s Jon Krosnick and Daniel Schneider, with Intuit’s Matt Berent and Hays Interactive’s Randall Thomas.
To improve the NPS, the team recommended replacing the 11 point unipolar rating scale with a 7 point bipolar scale from positive to negative impressions.
Their work replicated Hayes’ finding that liking and satisfaction with a company are highly significantly predictors than the likelihood of recommending, so Krosnick’s team recommended including questions like:
- Overall, how satisfied are you with the each of the following companies?
- How much do you like or dislike each of the following companies?
They uncovered correlations among measures of customer experience, and showed that liking is the best predictor of the number of recommendations and satisfaction.
Customers typically form more positive evaluations after the decision to purchase, probably due to validating purchase choices and reduce cognitive dissonance of purchase dissatisfaction, described by Stanford’s Leon Festinger.
These findings suggest that Reichheld’s claim of NPS as “the only question you need to ask” may be unsubstantiated, and that multiple measures of customer experience are more accurate predictors of a company’s revenue performance.
-*How credible is “willingness to recommend” a company as a predictor of its revenue growth?
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