Topic: AI-Generated Personalized Review Summaries to Reduce Product Returns: A Language Model Based Framework and Experiment

Speaker: Xie Ying, University of Texas at Dallas

Time: October 17, 2024, 10:30 AM

Venue: EMS 319

Abstract: The increasing rate of product returns presents a considerable challenge for online retailers. Online reviews provide valuable insights into user experiences that can potentially decrease product returns. However, the sheer abundance of reviews can overwhelm potential customers and hinder their access to useful information. This research proposes a framework that generates a personalized review summary for each customer-product pair, aiming to reduce return rates while preserving customer conversion and retention. We employ a multi-objective optimization approach to rank attributes and opinions within product reviews in a two-dimensional space. This information is then fed into a global attention-based abstractive summarizer as part of a small language model developed to produce personalized review summaries. The effectiveness of our framework was tested in a randomized, controlled digital experiment, which demonstrated a significant decrease in return rates across different product categories when customers were presented with personalized review summaries. These results offer valuable insights for online retailers seeking to reduce product return and develop optimal personalization strategies for review summaries.

Guest Bio: Ying Xie is Professor of Marketing at the Naveen Jindal School of Management, University of Texas at Dallas. Her research focuses on using quantitative methods to study the role of information in consumer decision making and derive implications for firms, regulators, and other stakeholders. She is especially interested in topics related to consumer learning, social influence, social media, content marketing, creator economy, and digital platforms. Her work has frequently appeared in top marketing and management journals including Journal of Marketing Research, Marketing Science, Management Science, and MIS Quarterly.