市场营销系学术讲座系列2021年第14讲

主 题：Product2Vec: Understanding Product-Level Competition Using Representation Learning

主讲人: Fanglin Chen（纽约大学）

时 间：2021-09-23 10:00

地 点：明商1007会议室

语 言：中英文

讲座摘要：

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.

主讲人简介：

Fanglin Chen is a Ph.D. candidate in Marketing at New York University Stern School of Business. Her main research interests center around the intersection of marketing and machine learning, specifically in the areas of product competition and consumer targeting. In one research stream, she focuses on extracting information from product co-purchase patterns to understand product relationships and market structure. The other research stream focuses on developing sequential targeting strategies to boost customer retention via machine learning. She is also interested in studying media consumption, including both traditional media and digital media.

She received her B.S. in Economics and Finance as well as in Mathematics from Tsinghua University.