Learning User Real-Time Intent to Reduce Shopping Cart Abandonment

Many e-commerce websites struggle to turn visitors into real buyers, because many users abandon their online shopping carts and exit sites without purchasing. Understanding online users’ real-time intent and dynamic shopping cart choices may have important implications in this realm. This study presents an individual-level, dynamic model with concurrent page adaptation that reflects users’ real-time, unobserved intent according to their online cart choices, then immediately performs web page adaptation to enhance the conversion of users into buyers. To understand shopping cart abandonment and suggest strategies for concurrent page adaptation, the model analyses each user’s browsing behaviour and tests the effectiveness of different marketing and web stimuli, as well as comparison shopping activities at other sites. 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 interventions greatly reduce shopping cart abandonment and increase purchase conversions. If the concurrent, intent-based page transformation for the focal site starts after the first page view, shopping cart abandonment declines by 22.4%, and purchase conversion improves by 4.2%. The optimal timing for the site to intervene is after three page views, to achieve efficient learning of users’ intent and early intervention simultaneously.