Working Papers

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

Sebastian Gabel

Artem Timoshenko

Northwestern University

Dec 15, 2020

Develops a scalable product choice model that predicts customer-specific purchase probabilities for all products in an assortment in response to personalized discounts. The deep learning architecture (1) applies to raw transaction data from loyalty programs; (2) scales to large product assortments and customer bases; and (3) accounts for cross-product relationships and the effects of marketing interventions within and across categories.

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