Choice Overload and the Long Tail: Consideration Sets and Purchases in Online Platforms

• 成果类型: Article
• 署名作者: Aparicio, Diego; Prelec, Drazen; Zhu, Weiming
• 署名单位: University of Navarra; IESE Business School; Massachusetts Institute of Technology (MIT); University of Hong Kong
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2021.0318
• 发表日期: 2025
• 关键词: OM-marketing interface; Empirical Research; consumer behavior; experiments

摘要

Problem definition: This paper examines frictions in the shopping funnel using empirical clickstream data from an online travel platform. We analyze (a) customers' heterogeneous search and purchase behaviors and (b) their reactions to changes in assortment size. We then develop a consider-then-choose model to generalize our findings. Methodology/results: We characterize the online customer journey as a two-stage consider-thenchoose framework. In the consider stage, we analyze the consideration set formation and show that heterogeneity-familiarity with the assortment-amplifies the number of options; in the purchase stage, it drives preferences for niche versus popular choices. A real-world high-stakes field experiment reveals that shrinking the menu produces mixed results: highlighting the market for the long-tail for some customers and reflecting choice overload for others. Finally, we build a psychologically rich consider-then-choose model with (a) heterogeneous preferences for product features and (b) heterogeneous search costs moderated by search fatigue, theoretically characterizing the impact on consideration sets and conversion rates. Managerial implications: Identifying frictions in the shopping funnel is critical for online platforms, especially when pain points hurt click-through or conversion rates. Which options matter to which users? What is the right assortment size? Although online platforms can offer virtually unlimited assortments, managers may assume frictionless environments-which is not always the case. Our findings offer insights into improving the customer journey by considering heterogeneous preferences and boundedly rational heuristics.