In e-commerce operations, respecting user needs and providing true value are the core of sustainable development. The following are compliance recommendations to improve the user experience through data analysis, rather than inducing non-essential consumption： 1. **Demand insight analysis** - Identify potential needs through data such as purchase history and browsing time (such as categories that are frequently browsed but not purchased). -Example: Push personalized recommendations for matching exercise clothes for users who have repeatedly viewed yoga clothes. 2. **Scenario recommendation optimization** -Construct user portraits and analyze life scenes (professional mothers/students, etc.), and recommend related products. -Tools: Use clustering algorithms to identify user group characteristics, such as recommending stationery sets for students during the back-to-school season. 3. **Content marketing strategy** -Push tutorial content according to reading preferences (supporting makeup tools are recommended in the beauty tutorial). -Data support: A/B testing shows that the conversion rate of tutorial +product portfolio has increased by 35%. 4. **Social incentive mechanism** -Analyze and share behavioral data to optimize the selection of products in the group (the data shows that the success rate of maternal and child products in the group is higher). -Note: The discount rules need to be clearly marked to avoid misleading. 5. **Personalized promotion design** -Push differentiated discounts based on the price sensitivity model (provide limited-time full discounts for discount-sensitive users). -Bottom line: All promotions must comply with the express terms of the "E-Commerce Law". Important principles： -All recommendations need to provide true value -Comply with the "Personal Information Protection Law" to obtain data authorization -Set up a convenient return and exchange channel -Regularly review algorithms to avoid bias It is recommended to adjust the goal to: "How to accurately meet the unambiguous potential needs of female users through data analysis", which can not only enhance business value, but also meet the requirements of business ethics.