Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation

Many e-commerce websites struggle to turn visitors into real buyers. Understanding online users’ realtime intent and dynamic shopping cart choices may have important implications in this realm. This study presents an individual-level, dynamic model with concurrent optimal page adaptation that learns users’ real-time, unobserved intent from their online cart choices, then immediately performs optimal webpage adaptation to enhance the conversion of users into buyers. To suggest optimal strategies for concurrent page adaptation, the model analyzes each individual user’s browsing behavior, tests the effectiveness of different marketing and web stimuli, as well as comparison shopping activities at other sites, and performs optimal webpage transformation. Data from an online retailer and a laboratory experiment reveal that concurrent learning of the user’s unobserved purchase intent and real-time, intent-based optimal interventions greatly reduce shopping cart abandonment and increase purchase conversions. If the concurrent, intent-based optimal page transformation for the focal site starts after the first page view, shopping cart abandonment declines by 32.4%, and purchase conversion improves by 6.9%. The optimal timing for the site to intervene is after three page views, to achieve efficient learning of users’ intent and early intervention simultaneously.