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Bayesian Nonparametric Customer Base Analysis with Model-Based Visualizations

Author

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  • Ryan Dew

    (Columbia Business School, Columbia University, New York, New York 10027)

  • Asim Ansari

    (Columbia Business School, Columbia University, New York, New York 10027)

Abstract

Marketing managers are responsible for understanding and predicting customer purchasing activity. This task is complicated by a lack of knowledge of all of the calendar time events that influence purchase timing. Yet, isolating calendar time variability from the natural ebb and flow of purchasing is important for accurately assessing the influence of calendar time shocks to the spending process, and for uncovering the customer-level purchasing patterns that robustly predict future spending. A comprehensive understanding of purchasing dynamics therefore requires a model that flexibly integrates known and unknown calendar time determinants of purchasing with individual-level predictors such as interpurchase time, customer lifetime, and number of past purchases. In this paper, we develop a Bayesian nonparametric framework based on Gaussian process priors, which integrates these two sets of predictors by modeling both through latent functions that jointly determine purchase propensity. The estimates of these latent functions yield a visual representation of purchasing dynamics, which we call the model-based dashboard, that provides a nuanced decomposition of spending patterns. We show the utility of this framework through an application to purchasing in free-to-play mobile video games. Moreover, we show that in forecasting future spending, our model outperforms existing benchmarks.

Suggested Citation

  • Ryan Dew & Asim Ansari, 2018. "Bayesian Nonparametric Customer Base Analysis with Model-Based Visualizations," Marketing Science, INFORMS, vol. 37(2), pages 216-235, March.
  • Handle: RePEc:inm:ormksc:v:37:y:2018:i:2:p:216-235
    DOI: 10.1287/mksc.2017.1050
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    Cited by:

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    2. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    3. Angelovska, Nina, 2021. "Analysis Of Customer Activity, The Importance Of Timing For Effective Marketing Actions: Case Of Group Buying Site, Grouper," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 12(2), pages 156-170.
    4. Soumya Mukhopadhyay & V Kumar & Amalesh Sharma & Tuck Siong Chung, 2022. "Impact of review narrativity on sales in a competitive environment," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2538-2556, June.
    5. 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.
    6. Robert W. Palmatier & Andrew T. Crecelius, 2019. "The “first principles” of marketing strategy," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 5-26, June.
    7. Bruno Jacobs & Dennis Fok & Bas Donkers, 2021. "Understanding Large-Scale Dynamic Purchase Behavior," Marketing Science, INFORMS, vol. 40(5), pages 844-870, September.
    8. Bae, Joonho & Park, Jinkyoo & Choi, Jeonghye & Bum Soh, Seung, 2023. "A recommending system for mobile games using the dynamic nonparametric model," Journal of Business Research, Elsevier, vol. 167(C).
    9. Rutz, Oliver & Aravindakshan, Ashwin & Rubel, Olivier, 2019. "Measuring and forecasting mobile game app engagement," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 185-199.
    10. Jeffrey D. Shulman & Olivier Toubia & Raena Saddler, 2023. "Editorial: Marketing’s Role in the Evolving Discipline of Product Management," Marketing Science, INFORMS, vol. 42(1), pages 1-5, January.
    11. Vahideh Sadat Abedi & Oded Berman & Fred M. Feinberg & Dmitry Krass, 2022. "Strategic new product media planning under emergent channel substitution and synergy," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2143-2166, May.

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