Model-based Purchase Predictions for Large Assortments

Bruno Jacobs, Bas Donkers and Dennis Fok

ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam

Abstract: Being able to accurately predict what a customer will purchase next is of paramount importance to successful online retailing. In practice, customer purchase history data is readily available to make such predictions, sometimes complemented with customer characteristics. Given the large assortments maintained by online retail- ers, scalability of the prediction method is just as important as its accuracy. We study two classes of models that use such data to predict what a customer will buy next: A novel approach that uses latent Dirichlet allocation (LDA), and mixtures of Dirichlet-Multinomials (MDM). A key benefit of a model-based approach is the potential to accommodate observed customer heterogeneity through the inclusion of predictor variables. We show that LDA can be extended in this direction while retaining its scalability. We apply the models to purchase data from an online re- tailer and contrast their predictive performance with that of a collaborative filter and a discrete choice model. Both LDA and MDM outperform the other meth- ods. Moreover, LDA attains performance similar to that of MDM while being far more scalable, rendering it a promising approach to purchase prediction in large assortments.

Keywords: model-based predictions; large scale purchase prediction; scalability; purchase history data; latent Dirichlet allocation; mixture of Dirichlet-Multinomials (search for similar items in EconPapers)
Pages: 44
Date: 2016-02-18
New Economics Papers: this item is included in nep-mkt
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Citations: View citations in EconPapers (29)

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Journal Article: Model-Based Purchase Predictions for Large Assortments (2016)
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