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Improving customer profit predictions with customer mindset metrics through multiple overimputation

Author

Listed:
  • Rajkumar Venkatesan

    (University of Virginia)

  • Alexander Bleier

    (Assistant Professor of Marketing, Frankfurt School of Finance & Management)

  • Werner Reinartz

    (University of Cologne)

  • Nalini Ravishanker

    (University of Connecticut)

Abstract

Research and practice have called for the incorporation of customer mindset metrics (CMMs) to improve the accuracy of models that predict individual customer profits. However, as CMMs are self-reported data, collected through customer surveys, they are seldom available for a firm’s entire customer database and in addition always measured with some degree of error. Their usage in models for individual-level predictions of customer profit has therefore proven challenging. We offer a solution through a new method called multiple overimputation (MO). MO treats missing data as an extreme form of measurement error and imputes the CMMs for both customers with observed, albeit with measurement error, as well as missing values, that are then included as predictors in a model of individual customer profits. Through a simulation study, empirical application in the pharmaceutical industry, and a customer selection exercise, we demonstrate the predictive and economic value of applying MO in the context of CRM.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joamsc:v:47:y:2019:i:5:d:10.1007_s11747-019-00658-6
    DOI: 10.1007/s11747-019-00658-6
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    3. Collins Kankam-Kwarteng & Appiah Sarpong & Ofosu Amofah & Stephen Acheampong, 2021. "Marketing performance of service firms: Recognizing market sensing capability and customer interaction orientation," Post-Print hal-03376959, HAL.
    4. 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.
    5. Ravula, Prashanth & Jha, Subhash & Biswas, Abhijit, 2022. "Relative persuasiveness of repurchase intentions versus recommendations in online reviews," Journal of Retailing, Elsevier, vol. 98(4), pages 724-740.

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