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Product2Vec: Understanding Product-Level Competition Using Representation Learning
Product2Vec: Understanding Product-Level Competition Using Representation Learning
Product2Vec: Understanding Product-Level Competition Using Representation Learning
Product2Vec: Understanding Product-Level Competition Using Representation Learning
27Sep
Seminar Calendar, Marketing

Product2Vec: Understanding Product-Level Competition Using Representation Learning

27 Sep 2021 | 9:00 p.m. — 10:30 p.m.
online seminar
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SPEAKER

Ms. Fanglin Chen
Ph.D. Candidate in Marketing
NYU Stern School of Business
New York University

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.

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Semiparametric Identification of the Discount Factor and Payoff Function in Dynamic Discrete Choice Models
Semiparametric Identification of the Discount Factor and Payoff Function in Dynamic Discrete Choice Models
Semiparametric Identification of the Discount Factor and Payoff Function in Dynamic Discrete Choice Models
Semiparametric Identification of the Discount Factor and Payoff Function in Dynamic Discrete Choice Models
Semiparametric Identification of the Discount Factor and Payoff Function in Dynamic Discrete Choice Models
18Jun
Economics | Seminar
Semiparametric Identification of the Discount Factor and Payoff Function in Dynamic Discrete Choice Models
18 June 2025 | 2:15 PM - 3:30 PM
KK 910 K. K. Leung Building, HKU
Prof. Katsumi Shimotsu
photo_Fanglin_0
About Speaker
Ms. Fanglin Chen

Ph.D. Candidate in Marketing | NYU Stern School of Business | New York University

Product2Vec: Understanding Product-Level Competition Using Representation Learning
27 Sep 20219:00 p.m. — 10:30 p.m.
online seminar
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