Should Managers Bother Tracking Net Promoter Score? New Research Sheds Light

Net Promoter Score (NPS) changes can predict short-term sales growth when measured as brand health for all potential customers.

The Why's? ??

The Net Promoter Score (NPS) holds significant importance in business analytics as it aims to gauge customer loyalty and predict future growth. Despite its widespread adoption by marketing practitioners, academic research has often questioned its reliability in forecasting sales and predicting future growth. Such a conflicting view regarding the usefulness of NPS underscores the critical need to understand whether NPS explains business growth.??

This research has shed new light and revealed the nuance behind the NPS debate. Under specific conditions, NPS fluctuations, but not absolute value, can signal impending revenue increases in the short term. Results suggest that while NPS may not be a universal predictor, its value can be unlocked with careful consideration.?

So, what does this mean for businesses???

Marketing practitioners and managers should harness NPC’s potential by utilising representative samples and tracking (and communicating) changes over time instead of relying on absolute value of NPS. The optimisation of NPS usage can provide organizations with a valuable growth diagnostic tool. By doing so, businesses can enhance their sales conversion rates and overall financial performance.

The Findings ??

When appropriately used, NPS can effectively predict short-term sales growth. The key findings on optimal NPS usage are:?

• NPS changes (not absolute value) predict short-term sales growth, while static NPS levels predict future sales levels. For example, a one-point increase in NPS translated to a 1.458 percentage point increase in the next quarter's sales growth in the studied sportswear brands.?

• NPS works best as an overall brand health metric capturing responses from all potential customers, not just a customer loyalty metric surveying current buyers. In fact, brand health accounts for over 90% of sales variation.?

• NPS changes driven by the entire potential customer pool, including non-customers, are most effective at predicting sales growth. Hearing from non-customers provides unique insights not captured by current customers alone.??

Some key points about limitations or conditions around using NPS:?

• NPS works best in industries/segments with high consumer involvement, short repurchase cycles (e.g. consumer goods like apparel, shoes). So it may not work as well for predicting long-term growth or in other industries.?

• NPS is best for predicting short-term (next quarter) sales growth, not long-term growth.?

• Changes in NPS over time predict sales growth, not absolute NPS levels. So if a company's NPS does not improve, it likely won't predict future growth.?

• NPS should be measured for all potential customers, not just current ones, to predict sales growth. So if only current customer NPS is tracked, it may not correlate with growth.?

• NPS explains a fraction of overall sales growth - other factors beyond NPS also impact growth substantially. So NPS can never fully predict sales changes.

The Actionable Recommendations ?

• Track changes in NPS rather than static NPS levels to predict growth (e.g., a 1-point increase in NPS predicted 1.458% higher sales growth for the sportswear brands in the next quarter).?

• Adopt NPS as a broad indicator of brand health across potential customers. Using NPS solely as a customer loyalty metric has limited predictive ability.?

• Be cautious in relying on NPS for long-term forecasts or industries with infrequent purchases, as NPS is best for short-term predictions in segments with high purchase frequency.?

• Focus on improving NPS across the customer journey rather than achieving absolute score targets. NPS alone explains only a fraction of sales changes.?

• Supplement NPS with deeper diagnostics to uncover specific issues spotted by NPS directional changes. Like a body temperature reading, NPS works best as an early broad indicator requiring further investigation.

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Curious about the science behind it? Dive deeper with us...

The Theory ??

• Theory of reasoned action. Explains how customers' likelihood-to-recommend through WOM may influence other potential buyers’ intention and behaviours. Recommendations can shape new customers' attitudes, purchase intentions, and buying behaviour.?

• Granger causality tradition. Allows for establishing a temporal & causal relationship between NPS changes and subsequent sales growth changes. Demonstrates NPS changes precede and explain future sales performance rather than just correlating with contemporaneous measures (e.g., a study showing ice cream sales increase after temperatures rise cannot conclude that ice cream sales cause hotter weather).?

The Methods ??

The study collected 193,220 Net Promoter Score evaluations over a period of 5 years for 7 major sportswear brands in the US market. On a monthly basis, approximately 38,600 American consumers between 16-30 years old were surveyed to capture the NPS data. This target segment was selected because they represent the highest sports participation and sportswear spending demographic. The researchers also leveraged quarterly sales performance data for the tracked brands from the NPD Group in their analysis to examine the relationship between NPS and sales growth over time. By matching the time horizons of these two datasets, changes in NPS could be examined as a predictor of subsequent revenue performance in the studied brands.

The Caveat and Limitations ???

• Single industry (sportswear) limits generalizability: The findings may not apply evenly across other consumer product categories or non-consumer-focused sectors like B2B. NPS's relationship with growth may differ.?

• Optimal NPS time lag may differ across industries: The identified one-quarter predictive time horizon for sportswear brands could vary in categories with longer or shorter purchase cycles and brand relationships.?

• Causation cannot be fully proven with this methodology: While establishing some degree of predictive sequencing, the models cannot confirm NPS itself causes sales changes versus correlating with other omitted drivers. Experimentation could better isolate causative impact.?

Full Reference

Baehre, S., O’Dwyer, M., O’Malley, L., & Lee, N. (2022). The use of Net Promoter Score (NPS) to predict sales growth: insights from an empirical investigation. Journal of the Academy of Marketing Science, 50(1), 67-84.

The Consumer Research Lab is an award-winning research team at Curtin University specialising in providing evidence-based and actionable consumer insights for industry partners. Apart from traditional market research methods, it specialises in the use of consumer neuroscience, machine learning, and digital innovations to address different industry challenges. Get in touch to find out more by emailing [email protected]