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Modeling Repeat Purchases in the Internet when RFM Captures Past Influence of Marketing

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  • Reimer, Kerstin
  • Albers, Sönke

Abstract

Predicting online customer repeat purchase behavior by accounting for the marketing-mix plays an important role in a variety of empirical studies regarding individual customer relationship management. A number of sophisticated models have been developed for different forecasting purposes based on a – mostly linear – combination of purchase history, so called Recency-Frequency-Monetary Value (RFM)-variables and marketing variables. However, these studies focus on a high predictive validity rather than ensuring that their proposed models capture the original effects of marketing activities. Thus, they ignore an explicit relationship between the purchase history and marketing which leads to biased estimates in case these variables are correlated. This study develops a modeling framework for the prediction of repeat purchases that adequately combines purchase history data and marketing-mix information in order to determine the original impact of marketing. More specifically, we postulate that RFM already captures the effects of past marketing activities and the original marketing impact is represented by temporal changes from the purchase process. Our analysis highlights and confirms the importance of adequately modeling the relationship between RFM and marketing. In addition, the results show superiority of the proposed model compared to a model with a linear combination of RFM and marketing variables.

Suggested Citation

  • Reimer, Kerstin & Albers, Sönke, 2011. "Modeling Repeat Purchases in the Internet when RFM Captures Past Influence of Marketing," EconStor Preprints 50730, ZBW - German National Library of Economics.
  • Handle: RePEc:zbw:esprep:50730
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    References listed on IDEAS

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    1. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    2. Jie Zhang & Lakshman Krishnamurthi, 2004. "Customizing Promotions in Online Stores," Marketing Science, INFORMS, vol. 23(4), pages 561-578, June.
    3. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    4. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    5. Sharad Borle & Siddharth S. Singh & Dipak C. Jain, 2008. "Customer Lifetime Value Measurement," Management Science, INFORMS, vol. 54(1), pages 100-112, January.
    6. Joel H. Steckel & Wilfried R. Vanhonacker, 1993. "Cross-Validating Regression Models in Marketing Research," Marketing Science, INFORMS, vol. 12(4), pages 415-427.
    7. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    8. G. S. Maddala, 1987. "Limited Dependent Variable Models Using Panel Data," Journal of Human Resources, University of Wisconsin Press, vol. 22(3), pages 307-338.
    9. Puhani, Patrick A, 2000. " The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    10. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    11. Romana Khan & Michael Lewis & Vishal Singh, 2009. "Dynamic Customer Management and the Value of One-to-One Marketing," Marketing Science, INFORMS, vol. 28(6), pages 1063-1079, 11-12.
    12. Roland T. Rust & Peter C. Verhoef, 2005. "Optimizing the Marketing Interventions Mix in Intermediate-Term CRM," Marketing Science, INFORMS, vol. 24(3), pages 477-489, December.
    13. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    14. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
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    More about this item

    Keywords

    Repeat Purchase Forecasting Models; Marketing Actions; Generalized Bass Model; Media Downloads;

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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