Shopping with a Platform AI Assistant: Who Adopts, When in the Journey, and What For

arXiv cs.AI / 3/27/2026

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Key Points

The paper analyzes 31 million users on Ctrip to study “Wendao,” an LLM-based AI shopping assistant embedded in an e-commerce platform.
It finds adoption is highest among older consumers, female users, and already highly engaged users—contrasting with the younger, male-dominated patterns often seen for general-purpose AI tools.
It shows AI chat is used in the same broad stage of the purchase journey as traditional search and is frequently interleaved with search before orders are placed.
Users mainly leverage the assistant for exploratory, hard-to-keyword tasks, with “attraction” queries making up 42% of chat requests, and chat intent varying by timing and eventual product category.
Overall, the study suggests embedded shopping AI complements conventional search by enabling exploratory discovery rather than simply replacing search behavior.

Abstract

This paper provides some of the first large-scale descriptive evidence on how consumers adopt and use platform-embedded shopping AI in e-commerce. Using data on 31 million users of Ctrip, China's largest online travel platform, we study "Wendao," an LLM-based AI assistant integrated into the platform. We document three empirical regularities. First, adoption is highest among older consumers, female users, and highly engaged existing users, reversing the younger, male-dominated profile commonly documented for general-purpose AI tools. Second, AI chat appears in the same broad phase of the purchase journey as traditional search and well before order placement; among journeys containing both chat and search, the most common pattern is interleaving, with users moving back and forth between the two modalities. Third, consumers disproportionately use the assistant for exploratory, hard-to-keyword tasks: attraction queries account for 42% of observed chat requests, and chat intent varies systematically with both the timing of chat relative to search and the category of products later purchased within the same journey. These findings suggest that embedded shopping AI functions less as a substitute for conventional search than as a complementary interface for exploratory product discovery in e-commerce.