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The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis

Author

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  • Patrick Bachmann

    (University Research Priority Program Social Networks, University of Zurich, 8050 Zurich, Switzerland)

  • Markus Meierer

    (University Research Priority Program Social Networks, University of Zurich, 8050 Zurich, Switzerland)

  • Jeffrey Näf

    (Department of Mathematics, Eidgenössische Technische Hochschule (ETH) Zurich, 8092 Zurich, Switzerland)

Abstract

Customer base analysis of noncontractual businesses builds on modeling purchases and latent attrition. With the Pareto/NBD model, this has become a straightforward exercise. However, this simplicity comes at a price. Customer-level predictions often lack precision. This issue can be addressed by acknowledging the importance of contextual factors for customer behavior. Considering contextual factors might contribute in two ways: (1) by increasing predictive accuracy and (2) by identifying the impact of these determinants on the purchase and attrition process. However, there is no generalization of the Pareto/NBD model that incorporates time-varying contextual factors. Preserving a closed-form maximum likelihood solution, this study proposes an extension that facilitates modeling time-invariant and time-varying contextual factors in continuous noncontractual settings. These contextual factors can influence the purchase process, the attrition process, or both. The authors further illustrate how to control for endogenous contextual factors. Benchmarking with three data sets from the retailing industry shows that explicitly modeling time-varying contextual factors significantly improves the accuracy of out-of-sample predictions for future purchases and latent attrition.

Suggested Citation

  • Patrick Bachmann & Markus Meierer & Jeffrey Näf, 2021. "The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis," Marketing Science, INFORMS, vol. 40(4), pages 783-809, July.
    • Handle: RePEc:inm:ormksc:v:40:y:2021:i:4:p:783-809
    DOI: 10.1287/mksc.2020.1254
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    References listed on IDEAS

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    1. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    2. Siddharth Singh & Sharad Borle & Dipak Jain, 2009. "A generalized framework for estimating customer lifetime value when customer lifetimes are not observed," Quantitative Marketing and Economics (QME), Springer, vol. 7(2), pages 181-205, June.
    3. David C. Schmittlein & Albert C. Bemmaor & Donald G. Morrison, 1985. "Technical Note—Why Does the NBD Model Work? Robustness in Representing Product Purchases, Brand Purchases and Imperfectly Recorded Purchases," Marketing Science, INFORMS, vol. 4(3), pages 255-266.
    4. Glady, Nicolas & Lemmens, Aurélie & Croux, Christophe, 2015. "Unveiling the relationship between the transaction timing, spending and dropout behavior of customers," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 78-93.
    5. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    6. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
    7. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    8. Peter Ebbes & Dominik Papies & Harald J. van Heerde, 2011. "The Sense and Non-Sense of Holdout Sample Validation in the Presence of Endogeneity," Marketing Science, INFORMS, vol. 30(6), pages 1115-1122, November.
    9. Ryan Dew & Asim Ansari, 2018. "Bayesian Nonparametric Customer Base Analysis with Model-Based Visualizations," Marketing Science, INFORMS, vol. 37(2), pages 216-235, March.
    10. Hruschka, Harald, 2010. "Considering endogeneity for optimal catalog allocation in direct marketing," European Journal of Operational Research, Elsevier, vol. 206(1), pages 239-247, October.
    11. repec:tiu:tiutis:52e91e47-4a2d-4e7b-bb23-3926b842ae30 is not listed on IDEAS
    12. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    13. Feihong Xia & Rabikar Chatterjee & Jerrold H. May, 2019. "Using Conditional Restricted Boltzmann Machines to Model Complex Consumer Shopping Patterns," Marketing Science, INFORMS, vol. 38(4), pages 711-727, July.
    14. Jeffrey M. Wooldridge, 2015. "Control Function Methods in Applied Econometrics," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 420-445.
    15. Abel P. Jeuland & Frank M. Bass & Gordon P. Wright, 1980. "A Multibrand Stochastic Model Compounding Heterogeneous Erlang Timing and Multinomial Choice Processes," Operations Research, INFORMS, vol. 28(2), pages 255-277, April.
    16. Makoto Abe, 2009. "“Counting Your Customers” One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 28(3), pages 541-553, 05-06.
    17. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    18. Jian Ni & Scott A. Neslin & Baohong Sun, 2012. "Database Submission--The ISMS Durable Goods Data Sets," Marketing Science, INFORMS, vol. 31(6), pages 1008-1013, November.
    19. Sungho Park & Sachin Gupta, 2012. "Handling Endogenous Regressors by Joint Estimation Using Copulas," Marketing Science, INFORMS, vol. 31(4), pages 567-586, July.
    20. Schröder, Nadine & Hruschka, Harald, 2016. "Investigating the effects of mailing variables and endogeneity on mailing decisions," European Journal of Operational Research, Elsevier, vol. 250(2), pages 579-589.
    21. van Oest, Rutger & Knox, George, 2011. "Extending the BG/NBD: A simple model of purchases and complaints," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 30-37.
    22. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
    23. Kinshuk Jerath & Peter S. Fader & Bruce G. S. Hardie, 2011. "New Perspectives on Customer "Death" Using a Generalization of the Pareto/NBD Model," Marketing Science, INFORMS, vol. 30(5), pages 866-880, September.
    24. 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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