Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287284 
Year of Publication: 
2021
Citation: 
[Journal:] Review of Managerial Science [ISSN:] 1863-6691 [Volume:] 16 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 1117-1145
Publisher: 
Springer, Berlin, Heidelberg
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.
Subjects: 
Marketing
Retailing
Market basket analysis
machine Learning
Restricted Boltzmann machine
Multivariate logit model
JEL: 
M31
L81
D12
C45
C89
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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