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

Se Yan, Han Zhong, Zemin, Zhong, Wenyu Zhou

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.

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

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.

Paper Structure

This paper contains 7 sections, 16 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Context and Data
  3. Who Adopts AI
  4. When Consumers Use AI
  5. How Consumers Use AI
  6. Conclusion
  7. Additional Tables and Figures

Figures (16)

  • Figure 1: AI chat adoption rates by demographic and account characteristics
  • Figure 2: Density of event types over the course of the customer journey
  • Figure 3: Temporal distribution of orders relative to chat sessions by chat intent
  • Figure A1: Estimated marginal effects on AI chat adoption from linear probability, logit, and probit models
  • Figure A2: Permutation-based variable importance from a random forest model of AI chat adoption
  • ...and 11 more figures