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Will the frog change into a prince? Predicting future customer profitability

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  • Rust, Roland T.
  • Kumar, V.
  • Venkatesan, Rajkumar

Abstract

More and more companies have customer databases that enable them to analyze customer profitability over time. These companies often seek to determine the most important customers as indicated by their current or historical profitability and focus attention on them. Focusing on profitable customers can result in more efficient use of marketing resources, but this approach neglects the fact that customers can evolve over time. Some customers begin as low-profit customers but eventually develop into high-profit customers. Others may start out as high-profit customers but become unprofitable over time. Previous efforts to predict future profitability have been relatively unsuccessful, with relatively simple, naïve models often performing just as well as or better than more sophisticated ones. Our paper presents a new approach to predicting customer profitability in future periods that performs significantly better than naïve models. We estimate the models on data from a high-tech company in a business-to-business context and validate the models' predictive ability on a holdout sample.

Suggested Citation

  • Rust, Roland T. & Kumar, V. & Venkatesan, Rajkumar, 2011. "Will the frog change into a prince? Predicting future customer profitability," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 281-294.
    • Handle: RePEc:eee:ijrema:v:28:y:2011:i:4:p:281-294
    DOI: 10.1016/j.ijresmar.2011.05.003
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    References listed on IDEAS

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    Cited by:

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    7. Lhoest-Snoeck, Sietske & van Nierop, Erjen & Verhoef, Peter C., 2014. "For New Customers Only: A Study on the Effect of Acquisition Campaigns on a Service Company's Existing Customers' CLV," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 210-224.
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    14. Butori, Raphaëlle & De Bruyn, Arnaud, 2013. "So you want to delight your customers: The perils of ignoring heterogeneity in customer evaluations of discretionary preferential treatments," International Journal of Research in Marketing, Elsevier, vol. 30(4), pages 358-367.
    15. Leigh McAlister & Shameek Sinha, 2021. "A customer portfolio management model that relates company’s marketing to its long-term survival," Journal of the Academy of Marketing Science, Springer, vol. 49(3), pages 584-600, May.
    16. Kohsuke Matsuoka, 2020. "Exploring the interface between management accounting and marketing: a literature review of customer accounting," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(3), pages 157-208, September.
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    18. Ramazan Esmeli & Mohamed Bader-El-Den & Hassana Abdullahi, 2021. "Towards early purchase intention prediction in online session based retailing systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 697-715, September.
    19. Dongyun Nie & Michael Scriney & Xiaoning Liang & Mark Roantree, 2024. "From data acquisition to validation: a complete workflow for predicting individual customer lifetime value," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 321-341, June.
    20. Ćorić, Ivica, 2016. "Comparison of Multivariate Statistical Analysis and Machine Learning Methods in Retailing: Research Framework Proposition," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2016), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016, pages 76-82, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    21. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.

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