Product2Vec: Understanding Product-Level Competition Using Representation Learning

55 Pages Posted: 7 Feb 2020

See all articles by Fanglin Chen

Fanglin Chen

New York University (NYU) - New York University (NYU), Leonard N. Stern School of Business, Department of Marketing, Students

Xiao Liu

New York University (NYU) - Leonard N. Stern School of Business

Davide Proserpio

Marshall School of Business, University of Southern California

Isamar Troncoso

University of Southern California - Marshall School of Business

Date Written: January 14, 2020

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, for every product, generates a low-dimensional vector that preserves important product information. Using these product vectors, we present several findings. First, we show that these vectors can recover analogies between product pairs. Second, we create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Third, we combine these vectors with traditional choice models to study product-level competition. To accurately estimate price elasticities, we modify the representation learning algorithm to remove the influence of price from the product vectors. 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. Fourth, we present two applications of Product2Vec to marketing problems: 1) analyzing intra- and inter-brand competition and 2) analyzing market structure. Overall, our results demonstrate that machine learning algorithms, such as representation learning, can be useful tools to augment and improve traditional marketing methods.

Keywords: machine learning, product competition, representation learning, choice models

Suggested Citation

Chen, Fanglin and Liu, Xiao and Proserpio, Davide and Troncoso, Isamar, Product2Vec: Understanding Product-Level Competition Using Representation Learning (January 14, 2020). NYU Stern School of Business, Available at SSRN: https://ssrn.com/abstract=3519358 or http://dx.doi.org/10.2139/ssrn.3519358

Fanglin Chen (Contact Author)

New York University (NYU) - New York University (NYU), Leonard N. Stern School of Business, Department of Marketing, Students ( email )

Henry Kaufman Ctr
44 W 4 St.
New York, NY
United States

Xiao Liu

New York University (NYU) - Leonard N. Stern School of Business ( email )

Suite 9-160
New York, NY
United States

Davide Proserpio

Marshall School of Business, University of Southern California ( email )

701 Exposition Blvd
Los Angeles, CA Los Angeles 90089
United States

HOME PAGE: http://faculty.marshall.usc.edu/Davide-Proserpio/

Isamar Troncoso

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA 90089
United States

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