Product2Vec: Understanding Product-Level Competition Using Representation Learning

• Activity Number: 236 - 2022 ASA Statistics in Marketing Doctoral Dissertation Best Papers Presentation
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Marketing
• Author(s): Fanglin Chen* and Xiao Liu and Davide Proserpio and Isamar Troncoso
• Companies: New York University and New York University and University of Southern California and University of Southern California
• Keywords: product competition; market structure; machine learning; representation learning

Abstract

Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. We introduce Product2Vec, a method based on representation learning, to study product-level competition when the number of products is large. The proposed model takes shopping baskets as inputs and generates a low-dimensional vector for every product that preserves important product information. Using these product vectors, we first create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Furthermore, we combine these vectors with traditional choice models to study product-level competition. We show that, compared with state-of-the-art choice models, our approach is faster and can produce more accurate demand forecasts and price elasticities. Lastly, we present two applications of Product2Vec to marketing problems: 1) analyzing intra- and inter-brand competition and 2) analyzing market structure.