IDEAS home Printed from https://ideas.repec.org/a/eee/joreco/v85y2025ics096969892500089x.html
   My bibliography  Save this article

Multicategory choice modeling by recurrent neural nets

Author

Listed:
  • Hruschka, Harald

Abstract

In multicategory choice, a customer may purchase multiple products or product categories at the same time. Hidden variables of recurrent nets depend on current inputs and hidden variables of the previous period. We investigate the three main variants of recurrent neural nets, which we compare to multilayer perceptrons and multivariate logit models. Model evaluation is based on binary cross-entropies for a holdout sample. We restrict further analyses to the best non-recurrent model, a multilayer perceptron, and the best performing recurrent neural net, which both include category-specific advertising (features) as inputs. We interpret these two models looking at category dependences and feature effects. Category dependences measure the strength of either complementary or substitutive relations. We show what the stronger dependences inferred from the recurrent net imply for cross-selling decisions. We also compare what these two models imply for sales promotion by optimizing features. For the multilayer perceptron we obtain features for each category, which are constant across weeks, equaling either zero or the maximum value. For the recurrent net, features assume many intermediate values and vary considerably across weeks. To illustrate managerial implications of the recurrent net, we determine weekly features for six selected categories that differ as much as possible from each other. Finally, we discuss limitations of our approach and opportunities for future research.

Suggested Citation

  • Hruschka, Harald, 2025. "Multicategory choice modeling by recurrent neural nets," Journal of Retailing and Consumer Services, Elsevier, vol. 85(C).
  • Handle: RePEc:eee:joreco:v:85:y:2025:i:c:s096969892500089x
    DOI: 10.1016/j.jretconser.2025.104310
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096969892500089X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jretconser.2025.104310?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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. R. Dunn & S. Reader & N. Wrigley, 1983. "An Investigation of the Assumptions of the Nbd Model as Applied to Purchasing at Individual Stores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(3), pages 249-259, November.
    3. Schröder, Nadine & Falke, Andreas & Hruschka, Harald & Reutterer, Thomas, 2019. "Analyzing the Browsing Basket: A Latent Interests-Based Segmentation Tool," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 181-197.
    4. 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.
    5. Paramveer S. Dhillon & Sinan Aral, 2021. "Modeling Dynamic User Interests: A Neural Matrix Factorization Approach," Marketing Science, INFORMS, vol. 40(6), pages 1059-1080, November.
    6. Sebastian Gabel & Artem Timoshenko, 2022. "Product Choice with Large Assortments: A Scalable Deep-Learning Model," Management Science, INFORMS, vol. 68(3), pages 1808-1827, March.
    7. Jiyeon Hong & Paul R. Hoban, 2022. "Writing More Compelling Creative Appeals: A Deep Learning-Based Approach," Marketing Science, INFORMS, vol. 41(5), pages 941-965, September.
    8. 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.
    9. 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.
    10. 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.
    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. Hruschka, Harald, 2014. "Linking Multi-Category Purchases to Latent Activities of Shoppers: Analysing Market Baskets by Topic Models," University of Regensburg Working Papers in Business, Economics and Management Information Systems 482, University of Regensburg, Department of Economics.
    15. Bee, Marco & Espa, Giuseppe & Giuliani, Diego, 2015. "Approximate maximum likelihood estimation of the autologistic model," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 14-26.
    16. Sri Devi Duvvuri & Asim Ansari & Sunil Gupta, 2007. "Consumers' Price Sensitivities Across Complementary Categories," Management Science, INFORMS, vol. 53(12), pages 1933-1945, December.
    17. 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.
    18. 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.
    19. Harald Hruschka, 2008. "Neural Nets and Genetic Algorithms in Marketing," International Series in Operations Research & Management Science, in: Berend Wierenga (ed.), Handbook of Marketing Decision Models, chapter 0, pages 399-433, Springer.
    20. Philippe Aurier & Victor Mejia, 2014. "Multivariate Logit and Probit models for simultaneous purchases: Presentation, uses, appeal and limitations," Post-Print hal-02014789, HAL.
    21. 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.
    22. Puneet Manchanda & Asim Ansari & Sunil Gupta, 1999. "The “Shopping Basket”: A Model for Multicategory Purchase Incidence Decisions," Marketing Science, INFORMS, vol. 18(2), pages 95-114.
    23. Katrin Dippold & Harald Hruschka, 2013. "Variable selection for market basket analysis," Computational Statistics, Springer, vol. 28(2), pages 519-539, April.
    24. Sarkar, Mainak & De Bruyn, Arnaud, 2021. "LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 80-95.
    25. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    26. 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.
    27. 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.
    Full references (including those not matched with items on IDEAS)

    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. Harald Hruschka, 2022. "Analyzing joint brand purchases by conditional restricted Boltzmann machines," Review of Managerial Science, Springer, vol. 16(4), pages 1117-1145, May.
    2. Harald Hruschka, 2024. "Relevance of dynamic variables in multicategory choice models," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(1), pages 109-133, March.
    3. Harald Hruschka, 2024. "Endogeneity of marketing variables in multicategory choice models," Journal of Business Economics, Springer, vol. 94(4), pages 639-657, May.
    4. Katrin Dippold & Harald Hruschka, 2013. "Variable selection for market basket analysis," Computational Statistics, Springer, vol. 28(2), pages 519-539, April.
    5. Dippold, Katrin & Hruschka, Harald, 2010. "Variable Selection for Market Basket Analysis," University of Regensburg Working Papers in Business, Economics and Management Information Systems 443, University of Regensburg, Department of Economics.
    6. Harald Hruschka, 2017. "Multi-category purchase incidences with marketing cross effects," Review of Managerial Science, Springer, vol. 11(2), pages 443-469, March.
    7. 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.
    8. Kim, Chul & Jun, Duk Bin & Park, Sungho, 2018. "Capturing flexible correlations in multiple-discrete choice outcomes using copulas," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 34-59.
    9. 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.
    10. Jean-Pierre Dubé, 2004. "Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks," Marketing Science, INFORMS, vol. 23(1), pages 66-81, September.
    11. Dippold Katrin & Hruschka Harald, 2013. "A Model of Heterogeneous Multicategory Choice for Market Basket Analysis," Review of Marketing Science, De Gruyter, vol. 11(1), pages 1-31, September.
    12. Ehrenberg, Andrew S. C. & Uncles, Mark D. & Goodhardt, Gerald J., 2004. "Understanding brand performance measures: using Dirichlet benchmarks," Journal of Business Research, Elsevier, vol. 57(12), pages 1307-1325, December.
    13. 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.
    14. Philippe Aurier & Victor D. Mejía, 2021. "The differing impacts of brand-line breadth and depth on customers’ repurchasing behavior of frequently purchased packaged goods," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1244-1266, November.
    15. Bonnet, Céline & Richards, Timothy J., 2016. "Models of Consumer Demand for Differentiated Products," TSE Working Papers 16-741, Toulouse School of Economics (TSE).
    16. Ma, Wanglin & Zheng, Hongyun & Gong, Binlei, 2022. "Rural income growth, ethnic differences, and household cooking fuel choice: Evidence from China," Energy Economics, Elsevier, vol. 107(C).
    17. Daniel Guhl & Friederike Paetz & Udo Wagner & Michel Wedel, 2024. "Predicting and optimizing marketing performance in dynamic markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(1), pages 1-27, March.
    18. Guhl, Daniel, 2024. "Tracking time-varying brand equity using household panel data," Journal of Business Research, Elsevier, vol. 182(C).
    19. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    20. Ta-Wei Huang & Eva Ascarza, 2024. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals," Marketing Science, INFORMS, vol. 43(4), pages 863-884, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:joreco:v:85:y:2025:i:c:s096969892500089x. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing-and-consumer-services .

    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.