Examining the Longitudinal Association Between Positive and Negative Likelihood-to-Recommend Scores and Brand Growth

Author:

Dawes John G.1ORCID

Affiliation:

1. University of South Australia, Adelaide, Australia

Abstract

A topic of debate in business press and academic research is whether metrics such as customer likelihood to recommend predict revenue or market share growth. Empirical tests about this association show mixed results. Much of the work to date on this topic has used the Net Promoter Score (NPS), which has the shortcoming of inferring likelihood to negatively recommend from low likelihood to positively recommend. Also, most likelihood-to-recommend or NPS studies have relied on customer samples. The question of whether NPS data gained from non-customers can be used to predict firm growth has been less well-explored. While non-customers may have a lower propensity to recommend for or against a brand, the brand may have many more non-customers than current customers. Therefore, likelihood to recommend scores among non-customers could be an influence on firm growth, just as they could be among current customers. This study examines how firms’ scores on positive or negative word of mouth likelihood, from current, former and not-ever-customers, relate to future growth. The study employs likelihood to recommend and market share data over a 10-year period for brands in four industries: motor vehicles, supermarkets, airlines and QSRs (quick service restaurants/coffee chains) in the UK. Results indicate no clear association between likelihood to recommend (positive or negative) scores for firms, and their future growth or decline across the four categories. The managerial implication is that firms should be wary of predicating growth on high or improving recommendation/NPS scores.

Publisher

SAGE Publications

Reference101 articles.

1. The ultimate question? Evaluating the use of Net Promoter Score in healthcare: A systematic review

2. Advocate Group. (2023). Cost of living crisis: Almost half of UK customers switch supermarket brands. https://www.advocate-group.co.uk/2022/11/02/cost-of-living-crisis-almost-half-of-uk-customers-switch-grocery-brands/

3. Understanding the link between customer feedback metrics and firm performance

4. Allen J. (2020). Most and least reliable car brands revealed. https://www.carwow.co.uk/blog/most-and-least-reliable-car-brands-revealed#gref

5. Word-of-Mouth Research: Principles and Applications

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