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

2026-03-25

AI Summary

This study investigates user adoption and usage patterns of embedded AI shopping assistants in e-commerce contexts, with a focus on user demographics, timing of use, and task types. Leveraging behavioral logs from 31 million users on Ctrip—the largest online travel platform in China—and integrating natural language understanding, conversational intent classification, and empirical econometric analysis, the research reveals that adoption patterns of embedded shopping AI significantly differ from those of general-purpose AI tools. Specifically, older users, female users, and highly active users are more likely to engage with the assistant. Notably, 42% of interactions involve exploratory queries about tourist attractions, and AI chat complements traditional keyword-based search, particularly excelling in discovery-oriented tasks that are difficult to articulate through conventional search terms.

Technology Category

• Humans and AI: User Experience and Usability
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

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.

Problem

• AI adoption
• shopping AI
• e-commerce
• consumer behavior
• LLM-based assistant

Innovation

• platform-embedded AI
• LLM-based assistant
• consumer adoption
• exploratory search
• purchase journey