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The predictive ability of different customer feedback metrics for retention

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  • de Haan, Evert
  • Verhoef, Peter C.
  • Wiesel, Thorsten

Abstract

This study systematically compares different customer feedback metrics (CFMs) – namely customer satisfaction, the Net Promoter Score, and the Customer Effort Score – to test their ability to predict retention across a wide range of industries. We classify the CFMs according to a time focus (past, present, or future) and whether the full scale of the CFM is used or whether the focus is only on the extremes (e.g., top-2-box customer satisfaction). The data for this study represent customers of 93 firms across 18 industries. Multi-level probit regression models, which control for self-selection bias of respondents, investigate firm-, customer-, and industry-level effects simultaneously. Overall, we find that the top-2-box customer satisfaction performs best for predicting customer retention and that focusing on the extremes is preferable to using the full scale. However the best CFM does differ depending on industry and the unit of analysis (i.e., comparing customers or firms with one another). Furthermore, combining CFMs, along with simultaneously investigating multiple dimensions of the customer relationship, improves predictions even further.

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  • de Haan, Evert & Verhoef, Peter C. & Wiesel, Thorsten, 2015. "The predictive ability of different customer feedback metrics for retention," International Journal of Research in Marketing, Elsevier, vol. 32(2), pages 195-206.
    • Handle: RePEc:eee:ijrema:v:32:y:2015:i:2:p:195-206
    DOI: 10.1016/j.ijresmar.2015.02.004
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    Cited by:

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    5. Blessing, Gerald & Natter, Martin, 2019. "Do Mystery Shoppers Really Predict Customer Satisfaction and Sales Performance?," Journal of Retailing, Elsevier, vol. 95(3), pages 47-62.
    6. J. Andrew Petersen & V. Kumar & Yolanda Polo & F. Javier Sese, 2018. "Unlocking the power of marketing: understanding the links between customer mindset metrics, behavior, and profitability," Journal of the Academy of Marketing Science, Springer, vol. 46(5), pages 813-836, September.
    7. Sven Baehre & Michele O’Dwyer & Lisa O’Malley & Nick Lee, 2022. "The use of Net Promoter Score (NPS) to predict sales growth: insights from an empirical investigation," Journal of the Academy of Marketing Science, Springer, vol. 50(1), pages 67-84, January.
    8. Gijsenberg, Maarten & Verhoef, Pieter, 2018. "Moving Forward," Research Report 2018003-MARK, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    9. Rahhal El Makkaoui, 2023. "Nature of Customer Experience in the Sharing Economy [La nature de l'expérience client dans l'économie collaborative]," Post-Print hal-04450139, HAL.
    10. Hafiz Muhammad Naveed & Yao Hongxing & Muhammad Akhtar & Muhammad Usman Anwer & David Alemzero, 2020. "The Impact of Customer Feedback on Organizational Health when Employee Empowerment works as a moderator: Evidence from Pakistani Fast Food Industry," Business and Economic Research, Macrothink Institute, vol. 10(3), pages 65-89, September.
    11. Jonas R. Jahnert & Hato Schmeiser, 2022. "The relationship between net promoter score and insurers’ profitability: an empirical analysis at the customer level," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(4), pages 944-972, October.
    12. Thomas A. Burnham & Jeffrey A. Wong, 2018. "Factors influencing successful net promoter score adoption by a nonprofit organization: a case study of the Boy Scouts of America," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 15(4), pages 475-495, December.
    13. Peter C. Verhoef & Martin Heijnsbroek & Joost Bosma, 2017. "Developing A Service Improvement System for the National Dutch Railways," Interfaces, INFORMS, vol. 47(6), pages 489-504, December.
    14. Sunghun Chung & Donghyuk Shin & Jooyoung Park, 2022. "Predicting Firm Market Performance Using the Social Media Promoter Score," Marketing Letters, Springer, vol. 33(4), pages 545-561, December.
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    16. Agag, Gomaa & Durrani, Baseer Ali & Shehawy, Yasser Moustafa & Alharthi, Majed & Alamoudi, Hawazen & El-Halaby, Sherif & Hassanein, Ahmed & Abdelmoety, Ziad H., 2023. "Understanding the link between customer feedback metrics and firm performance," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    17. Raassens, N. & Haans, Hans, 2017. "NPS and online WOM investigating the relationship between customers’ promoter scores and eWOM behavior," Other publications TiSEM 931e7761-7c6e-40ee-8976-2, Tilburg University, School of Economics and Management.
    18. Rajkumar Venkatesan & Alexander Bleier & Werner Reinartz & Nalini Ravishanker, 2019. "Improving customer profit predictions with customer mindset metrics through multiple overimputation," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 771-794, September.
    19. Cambra-Fierro, Jesús & Gao, Lily (Xuehui) & Melero-Polo, Iguácel & Trifu, Andreea, 2021. "How do firms handle variability in customer experience? A dynamic approach to better understanding customer retention," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    20. Huayan Shen & Zhiyong Ou & Kexin Bi & Yu Gao, 2023. "Impact of Customer Predictive Ability on Sustainable Innovation in Customized Enterprises," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
    21. Cambra-Fierro, Jesús & Gao, Lily (Xuehui) & Melero-Polo, Iguácel, 2021. "The power of social influence and customer–firm interactions in predicting non-transactional behaviors, immediate customer profitability, and long-term customer value," Journal of Business Research, Elsevier, vol. 125(C), pages 103-119.
    22. Lily (Xuehui) Gao & Evert Haan & Iguácel Melero-Polo & F. Javier Sese, 2023. "Winning your customers’ minds and hearts: Disentangling the effects of lock-in and affective customer experience on retention," Journal of the Academy of Marketing Science, Springer, vol. 51(2), pages 334-371, March.

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