The Predictive Ability of Different Customer Feedback Metrics for Retention

De Haan and colleagues argued that academic literature literature that’s critical of metrics like the NPS are hard to interpret because they use different dependent variables, research settings, methodologies, units of analysis, and so on.

The authors analyzed data from 6,649 respondents, who in total filled out 8,924 firm evaluations for 93 Dutch firms across 18 industries. Respondents answered several CFM (customer feedback metric) questions, including a single seven-point satisfaction item, the 11-point NPS item, and the single five-point Customer Effort Score (CES).

Roughly 15% of respondents (1,308) answered a follow-up survey two years later and indicated which firms they were still doing business with.  Approximately 27% of customers no longer did business—were “churned”—in the two-year period.

They found top-two-box satisfaction had the highest correlation with two-year retention (r = .184), which was very similar to the conventionally reported NPS (r = .170). The length of the relationship was the best predictor (r = .199), meaning longer relationships lead to more retention. CES had a negative correlation (r = -.073) and was the only measure the authors did not recommend using.

They concluded that there is no single best metric to predict customer retention across industries and, in contrast to Keiningham et al. (2007), monitoring NPS does not seem to be wrong in most industries.

Takeaway: The authors found that the NPS correlated modestly (r = .17) with two-year retention, slightly lower than a single top-two-box satisfaction item (r = .18). Both NPS and satisfaction correlated highly with each other across 19 industries (r = .97). Contrary to Pingitore et al., they argued that using a top-box scoring approach offered superior results to using the mean, and a single-item measure was adequate, although combining measures like satisfaction and NPS may offer some predictive benefit.

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