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Analyzing joint brand purchases by conditional restricted Boltzmann machines

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  • Harald Hruschka

    (University of Regensburg)

Abstract

We introduce the conditional restricted Boltzmann machine as method to analyze brand-level market basket data of individual households. The conditional restricted Boltzmann machine includes marketing variables and household attributes as independent variables. To our knowledge this is the first study comparing the conditional restricted Boltzmann machine to homogeneous and heterogeneous multivariate logit models for brand-level market basket data across several product categories. We explain how to estimate the conditional restricted Boltzmann machine starting from a restricted Boltzmann machine without independent variables. The conditional restricted Boltzmann machine turns out to excel all the other investigated models in terms of log pseudo-likelihood for holdout data. We interpret the selected conditional restricted Boltzmann machine based on coefficients linking purchases to hidden variables, interdependences between brand pairs as well as own and cross effects of marketing variables. The conditional restricted Boltzmann machine indicates pairwise relationships between brands that are more varied than those of the multivariate logit model are. Based on the pairwise interdependences inferred from the restricted Boltzmann machine we determine the competitive structure of brands by means of cluster analysis. Using counterfactual simulations, we investigate what three different models (independent logit, heterogeneous multivariate logit, conditional restricted Boltzmann machine) imply with respect to the retailer’s revenue if each brand is put on display. Finally, we mention possibilities for further research, such as applying the conditional restricted Boltzmann machine to other areas in marketing or retailing.

Suggested Citation

  • Harald Hruschka, 2022. "Analyzing joint brand purchases by conditional restricted Boltzmann machines," Review of Managerial Science, Springer, vol. 16(4), pages 1117-1145, May.
  • Handle: RePEc:spr:rvmgts:v:16:y:2022:i:4:d:10.1007_s11846-021-00478-5
    DOI: 10.1007/s11846-021-00478-5
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    References listed on IDEAS

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    1. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
    2. 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.
    3. Tetyana Kosyakova & Thomas Otter & Sanjog Misra & Christian Neuerburg, 2020. "Exact MCMC for Choices from Menus—Measuring Substitution and Complementarity Among Menu Items," Marketing Science, INFORMS, vol. 39(2), pages 427-447, March.
    4. Richards, Timothy J. & Hamilton, Stephen F. & Yonezawa, Koichi, 2018. "Retail Market Power in a Shopping Basket Model of Supermarket Competition," Journal of Retailing, Elsevier, vol. 94(3), pages 328-342.
    5. D. R. Cox, 1972. "The Analysis of Multivariate Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 113-120, June.
    6. Matthew Gentzkow, 2007. "Valuing New Goods in a Model with Complementarity: Online Newspapers," American Economic Review, American Economic Association, vol. 97(3), pages 713-744, June.
    7. Harald Hruschka, 2017. "Multi-category purchase incidences with marketing cross effects," Review of Managerial Science, Springer, vol. 11(2), pages 443-469, March.
    8. Kwak, Kyuseop & Duvvuri, Sri Devi & Russell, Gary J., 2015. "An Analysis of Assortment Choice in Grocery Retailing," Journal of Retailing, Elsevier, vol. 91(1), pages 19-33.
    9. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    10. Bruno J.D. Jacobs & Bas Donkers & Dennis Fok, 2016. "Model-Based Purchase Predictions for Large Assortments," Marketing Science, INFORMS, vol. 35(3), pages 389-404, May.
    11. Koen Bel & Dennis Fok & Richard Paap, 2018. "Parameter estimation in multivariate logit models with many binary choices," Econometric Reviews, Taylor & Francis Journals, vol. 37(5), pages 534-550, May.
    12. Harald Hruschka, 2021. "Comparing unsupervised probabilistic machine learning methods for market basket analysis," Review of Managerial Science, Springer, vol. 15(2), pages 497-527, February.
    13. Roger Betancourt & David Gautschi, 1990. "Demand Complementarities, Household Production, and Retail Assortments," Marketing Science, INFORMS, vol. 9(2), pages 146-161.
    14. Philippe Aurier & Victor Mejia, 2014. "Multivariate Logit and Probit models for simultaneous purchases: Presentation, uses, appeal and limitations [Les modèles Logit et Probit multivariés pour la modélisation des achats simultanés : pré," Post-Print hal-01976725, HAL.
    15. Philippe Aurier & Victor Mejia, 2014. "Multivariate Logit and Probit models for simultaneous purchases: Presentation, uses, appeal and limitations," Post-Print hal-02014789, HAL.
    16. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
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