Product Choice with Large Assortments: A Scalable Deep-Learning Model

38 Pages Posted: 21 Jun 2019 Last revised: 29 Dec 2020

Date Written: December 13, 2020

Abstract

Personalized marketing in retail requires a model to predict how different marketing actions affect product choices by individual customers. Large retailers often handle millions of transactions daily, involving thousands of products in hundreds of categories. Product choice models thus need to scale to large product assortments and customer bases, without extensive product attribute information. To address these challenges, we propose a custom deep neural network model. The model incorporates bottleneck layers to encode cross-product relationships, calibrates time-series filters to capture purchase dynamics for products with different interpurchase times, and relies on weight sharing between the products to improve convergence and scale to large assortments. The model applies to loyalty card transaction data without predefined categories or product attributes to predict customer-specific purchase probabilities in response to marketing actions. In a simulation, the proposed product choice model predicts purchase decisions better than baseline methods by adjusting the predicted probabilities for the effects of recent purchases and price discounts. The improved predictions lead to substantially higher revenue gains in a simulated coupon personalization problem. We verify predictive performance using transaction data from a large retailer with experimental variation in price discounts.

Keywords: Product Choice Model, Neural Networks, Deep Learning, Cross-Category Choice, Retail Analytics

JEL Classification: M30, C53, C51, M20

Suggested Citation

Gabel, Sebastian and Timoshenko, Artem, Product Choice with Large Assortments: A Scalable Deep-Learning Model (December 13, 2020). Available at SSRN: https://ssrn.com/abstract=3402471 or http://dx.doi.org/10.2139/ssrn.3402471

Sebastian Gabel

Humboldt University of Berlin - School of Business and Economics ( email )

Spandauer Str. 1
Berlin, D-10099
Germany

Artem Timoshenko (Contact Author)

Kellogg School of Management, Northwestern University ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

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