IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v38y2019i4p711-727.html
   My bibliography  Save this article

Using Conditional Restricted Boltzmann Machines to Model Complex Consumer Shopping Patterns

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mksc.2019.1162
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2019.1162?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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).
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Bruno Jacobs & Dennis Fok & Bas Donkers, 2021. "Understanding Large-Scale Dynamic Purchase Behavior," Marketing Science, INFORMS, vol. 40(5), pages 844-870, September.
    4. 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.
    5. Harald Hruschka, 2022. "Analyzing joint brand purchases by conditional restricted Boltzmann machines," Review of Managerial Science, Springer, vol. 16(4), pages 1117-1145, May.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vithala R. Rao & Gary J. Russell & Hemant Bhargava & Alan Cooke & Tim Derdenger & Hwang Kim & Nanda Kumar & Irwin Levin & Yu Ma & Nitin Mehta & John Pracejus & R. Venkatesh, 2018. "Emerging Trends in Product Bundling: Investigating Consumer Choice and Firm Behavior," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 107-120, March.
    2. Ma, Yu & Seetharaman, P.B. & Narasimhan, Chakravarthi, 2012. "Modeling Dependencies in Brand Choice Outcomes Across Complementary Categories," Journal of Retailing, Elsevier, vol. 88(1), pages 47-62.
    3. Gary Koop & Dimitris Korobilis, 2023. "Bayesian Dynamic Variable Selection In High Dimensions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1047-1074, August.
    4. Lukasz Huminiecki, 2018. "Modelling of the breadth of expression from promoter architectures identifies pro-housekeeping transcription factors," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-28, June.
    5. Timothy J. Richards, 2017. "Analysis of Umbrella Branding with Crowdsourced Data," Agribusiness, John Wiley & Sons, Ltd., vol. 33(2), pages 135-150, April.
    6. Sanghak Lee & Jaehwan Kim & Greg M. Allenby, 2013. "A Direct Utility Model for Asymmetric Complements," Marketing Science, INFORMS, vol. 32(3), pages 454-470, May.
    7. Raluca M. Ursu & Daria Dzyabura, 2020. "Retailers’ product location problem with consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 18(2), pages 125-154, June.
    8. Rajiv Sambasivan & Sourish Das & Sujit K. Sahu, 2020. "A Bayesian perspective of statistical machine learning for big data," Computational Statistics, Springer, vol. 35(3), pages 893-930, September.
    9. Harald Hruschka, 2017. "Analyzing the dependences of multi-category purchases on interactions of marketing variables," Journal of Business Economics, Springer, vol. 87(3), pages 295-313, April.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
    12. Harald Hruschka, 2017. "Multi-category purchase incidences with marketing cross effects," Review of Managerial Science, Springer, vol. 11(2), pages 443-469, March.
    13. Niloy Biswas & Anirban Bhattacharya & Pierre E. Jacob & James E. Johndrow, 2022. "Coupling‐based convergence assessment of some Gibbs samplers for high‐dimensional Bayesian regression with shrinkage priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 973-996, July.
    14. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    15. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    16. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
    17. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    18. Hou-Tai Chang & Ping-Huai Wang & Wei-Fang Chen & Chen-Ju Lin, 2022. "Risk Assessment of Early Lung Cancer with LDCT and Health Examinations," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    19. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    20. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:38:y:2019:i:4:p:711-727. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.