Analyzing Consumer Footprints on E-Commerce Platforms: A Multichannel Sequential Search Model with Reference Price
构建了一个多渠道序贯搜索模型，分析消费者在电商原生应用和小程序渠道上的点击与购买足迹，发现消费者在渠道偏好和参考价格敏感性上存在显著差异，并建议平台在应用和小程序之间实施平均32%的促销折扣差以提升利润。
Accompanied by the popularity of mini-programs, consumers can easily browse products across both the e-commerce native app channel and the mini-program channel, leaving behind extensive click-through and purchase records across various channels. These multichannel footprints contain rich information about consumers’ search preferences, which offer great potential for optimizing platforms’ multichannel management. However, prior studies predominantly focus on single-channel contexts, which may not directly apply to analyzing consumers’ multichannel footprints. This research develops a multichannel sequential search (MSS) model to characterize consumers’ multichannel footprints as a threefold search process consisting of cross-channel, cross-product, and cross-page search, where consumers dynamically update their cross-channel reference prices as they navigate their search journeys. The estimation results of the MSS model reveal that consumers exhibit significant heterogeneity in channel preferences, cross-channel costs, and sensitivity to cross-channel reference prices, contributing to their diverse multichannel footprints. Drawing on a comprehensive understanding of the MSS process, our optimal policy recommends that the platform implement a promotion gap with an average discount percentage of 32% between the app and mini-program channels. The channel-specific promotion strategy fosters a strong cross-channel reference price effect, which uses a smaller promotion to achieve a significant profit improvement over the state-of-the-art model.