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Introduction
Product discovery is a critical component of success in China’s e-commerce ecosystem. With millions of SKUs across platforms, AI-driven recommendation engines have become the secret weapon for increasing conversions and basket size. For overseas brands, integrating with China’s advanced recommendation systems—or building their own localized engines—can significantly boost relevance and revenue.
1. Understand Platform-Level Recommendation Ecosystems
1.1 Tmall and JD.com Algorithm Mechanics
Platforms like Tmall and JD.com use AI to surface products based on user behavior, purchase history, browsing time, and social sharing. To succeed, ensure your product data (titles, tags, specs) aligns with these algorithms.
1.2 Leverage Douyin’s Content-Commerce Synergy
On Douyin, AI engines recommend both content and products based on video engagement. Brands should work with influencers who generate high watch-through rates to trigger product recommendations under videos.
2. Implement First-Party AI Recommendations for DTC Channels
2.1 Train Models Based on Onsite Behavior
SaaS-driven e-commerce brands operating their own WeChat stores or websites can use platforms like Youzan or SHOPLAZZA with embedded AI modules. These track clicks, dwell time, and cart patterns to customize product suggestions.
2.2 Localization: Recommend Based on Regional Preferences
AI can cluster users by city, climate, and festival behavior. For instance, promoting insulated water bottles in northern cities during winter, or skincare products with SPF in southern regions year-round.
3. Personalize Based on Demographics and Lifecycle Data
3.1 Age, Gender, and Device Segmentation
Use AI models that understand demographic cohorts. A Gen Z buyer on Xiaohongshu will respond differently than a 40+ customer on JD. Personalizing SKUs and language by segment drives better CTR and conversions.
3.2 Past Purchase Behavior and LTV
Use machine learning to recommend replenishment (e.g., skincare) or complementary categories (e.g., phone case after a phone). Build strategies based on predicted lifetime value to improve retention.
4. A/B Test and Optimize Recommendations Continuously
4.1 Test Product Positioning and Timing
AI lets brands test different recommendations in various parts of the funnel—from homepage to checkout. Measure what drives actual conversions vs clicks to refine logic.
4.2 Use Feedback Loops for Improvement
Incorporate user actions (e.g., dismissals, wishlist saves) to improve future recommendations. A smart system learns not just from what converts but from what users reject.
Case Study: Korean DTC Skincare Brand Increases AOV with Smart Recommender
A Korean brand operating a WeChat Mini Program store embedded an AI engine that recommended skincare bundles based on customer skin type (self-declared in a quiz) and seasonality. Users received product suggestions after checkout for subscription refills and seasonal add-ons. The brand saw a 24% increase in average order value and 38% rise in repeat purchase rate over three months.
PLTFRM is an international brand consulting agency that works with companies such as Red, TikTok, Tmall, Baidu, and other well-known Chinese internet e-commerce platforms. We have been working with Chile Cherries for many years, reaching Chinese consumers in depth through different platforms and realizing that Chile Cherries’ exports in China account for 97% of the total exports in Asia. Contact us, and we will help you find the best China e-commerce platform for you. Search PLTFRM for a free consultation!
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