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Using Conditional Restricted Boltzmann Machines to Model Complex Consumer Shopping Patterns

Author

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  • Feihong Xia

    (University of Rhode Island College of Business Administration, Kingston, Rhode Island 02881)

  • Rabikar Chatterjee

    (Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

  • Jerrold H. May

    (Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

Abstract

Marketers have recognized that the probability of a consumer’s (or household’s) purchase in a particular product category may be influenced by past purchases in the same category and also, purchases in other related categories. Past studies of crosscategory effects have focused on a limited number of product categories, and they have often ignored intertemporal effects in their analyses. Those studies have generally used multivariate logit or probit models, which are limited in their ability to analyze enormous data sets that contain consumer purchase records across a large number of categories and time periods. The availability of such enormous consumer shopping data sets and the value of analyzing the complex relationships across categories and over time (for example, for personalized promotions) suggest the need for computationally efficient modeling and estimation methods. Such models can capture associations among buying decisions across all product categories and over all time periods for which data are available, but they must also have a tractable and clear model structure that permits meaningful interpretation of the results. We explore the nature of intertemporal crossproduct patterns in an enormous consumer purchase data set using a model that adopts the structure of conditional restricted Boltzmann machines (CRBMs). Our empirical results demonstrate that our proposed approach using the efficient estimation algorithm embodied in the CRBM enables us to process very large data sets and capture the consumer decision patterns for both predictive and descriptive purposes that might not otherwise be apparent. In addition to persistent intertemporal within-category effects, we find that there are also significant intertemporal cross effects between product categories.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormksc:v:38:y:2019:i:4:p:711-727
    DOI: 10.1287/mksc.2019.1162
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    References listed on IDEAS

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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Sri Duvvuri & Thomas Gruca, 2010. "A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities Across Categories," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 558-578, September.
    3. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    4. P. Seetharaman & Siddhartha Chib & Andrew Ainslie & Peter Boatwright & Tat Chan & Sachin Gupta & Nitin Mehta & Vithala Rao & Andrei Strijnev, 2005. "Models of Multi-Category Choice Behavior," Marketing Letters, Springer, vol. 16(3), pages 239-254, December.
    5. Nitin Mehta, 2007. "Investigating Consumers' Purchase Incidence and Brand Choice Decisions Across Multiple Product Categories: A Theoretical and Empirical Analysis," Marketing Science, INFORMS, vol. 26(2), pages 196-217, 03-04.
    6. Karsten Hansen & Vishal Singh & Pradeep Chintagunta, 2006. "Understanding Store-Brand Purchase Behavior Across Categories," Marketing Science, INFORMS, vol. 25(1), pages 75-90, 01-02.
    7. Yasemin Boztuğ & Lutz Hildebrandt, 2008. "Modeling Joint Purchases with a Multivariate MNL Approach," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 60(4), pages 400-422, October.
    8. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    9. Sri Devi Duvvuri & Asim Ansari & Sunil Gupta, 2007. "Consumers' Price Sensitivities Across Complementary Categories," Management Science, INFORMS, vol. 53(12), pages 1933-1945, December.
    10. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Cited by:

    1. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    2. Harald Hruschka, 2022. "Analyzing joint brand purchases by conditional restricted Boltzmann machines," Review of Managerial Science, Springer, vol. 16(4), pages 1117-1145, May.
    3. 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.
    4. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    5. Bruno Jacobs & Dennis Fok & Bas Donkers, 2021. "Understanding Large-Scale Dynamic Purchase Behavior," Marketing Science, INFORMS, vol. 40(5), pages 844-870, September.
    6. Hou, Jianwei & Elliott, Kevin, 2021. "Mobile shopping intensity: Consumer demographics and motivations," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    7. von Zahn, Moritz & Bauer, Kevin & Mihale-Wilson, Cristina & Jagow, Johanna & Speicher, Max & Hinz, Oliver, 2022. "The smart green nudge: Reducing product returns through enriched digital footprints & causal machine learning," SAFE Working Paper Series 363, Leibniz Institute for Financial Research SAFE, revised 2022.
    8. Andreas Falke & Harald Hruschka, 2022. "Analyzing browsing across websites by machine learning methods," Journal of Business Economics, Springer, vol. 92(5), pages 829-852, July.

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