AI-Empowered Retail Product Selection: Data-Driven Strategy Triples Hit Rate
How a mid-sized fashion e-commerce company (annual GMV of 50 million RMB) replaced manual product selection intuition with AI, leveraging social media trends, competitor dynamics, and historical sales data to boost new product hit rate from 12% to 38%, while reducing inventory turnover days from 85 to 52.
Steps
- 1
Build trend monitoring system: Use Python to scrape popular content tags from Xiaohongshu, Douyin, and Instagram, integrate Google Trends API, and automatically generate daily 'Trending Keyword Rankings' and 'Emerging Style Direction' reports.
- 2
AI-powered product selection analysis: Input trend reports, competitor new product data, and historical similar product sales data into ChatGPT to generate comprehensive product selection recommendations including 'recommendation rationale', 'predicted sales range', and 'risk warnings'.
- 3
Small-batch test orders: Place initial orders of 50 units for AI-recommended products, and decide whether to restock based on 7-day sales velocity (daily average sales + repurchase rate + add-to-cart rate).
- 4
Dynamic replenishment forecasting: Build an ARIMA + seasonal adjustment model that automatically pushes replenishment suggestions based on current sales velocity, holiday factors, and inventory days, reducing stockouts and overstock.
- 5
Slow-moving product handling: AI identifies products at risk of becoming slow-moving (no sales in 30 days), automatically generates markdown suggestions (based on competitor pricing and inventory days), and triggers promotional campaign workflows.
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Also available in 中文.