From Stars to Dogs: A Data Analytic Approach to Identifying Out-Of-Favor Products on E-Commerce Platforms

• 成果类型: Article
• 署名作者: Ivanov, Anton; Ghoshal, Abhijeet; Kumar, Akhil
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478241282327
• 发表日期: 2025
• 页码: 1346-1366
• 关键词: Product Perceived Quality Management; Informational Value; user-generated content; Trend Analysis; System Architecture Development; Sequence Model Forecasting

摘要

Online retail platforms are increasingly challenged by the proliferation of low-quality products, which may damage their reputation and sales. To address this problem, we propose a system architecture to proactively identify products that are likely to go out of favor. Our approach uses historical data to extract useful information from customer ratings and textual reviews. Available data are fed into a state-of-the-art deep learning sequence model to forecast future ratings. We then analyze rating trends, extracting hyperparameters that a binary classifier uses to label products as out-of-favor or not. We tested this system on an Amazon dataset comprising nearly 800,000 observations across 2826 electronics products. Our results show that the Long Short-Term Memory (LSTM) model excels in forecasting future product ratings compared to other benchmarks. Ablation analysis shows sentiment-related features significantly improve rating forecasts by up to 40%, with review topics adding 10% and other review characteristics, 4%. Counterintuitively, topic extraction from reviews does not provide substantial benefits, despite the heavy computational resources it requires. Finally, the two-stage classification process, which leverages time-series data and rating trends, offers a more stable and robust performance than conventional single-stage methods. We provide considerations for system architecture development through robustness checks ensuring its resilience to stressors. Our experiments indicate that rating trends can change in subtle ways over time, leading a promising star product to turn into a liability (dog). E-commerce platforms can use the proposed system architecture proactively to identify and remove potentially dubious products instead of waiting to take reactive action.