Optimizing the product recommendation system to increase the purchase volume of female customers requires a combination of data analysis, behavioral insights, and personalized strategies. The following is a step-by-step solution： --- ### **1. Data collection and cleaning** -**Key data sources**： -**Purchase history**: order amount, category preference, repurchase cycle, customer unit price, promotion sensitivity. -**Browsing behavior**: Page residence time, search keywords, click on heat maps, add purchased items. -**User portrait**: Age, region, device (mobile/PC), registration source (social media/advertising). -**Feedback data**: evaluation, reason for return, customer service consultation records. -**Data cleaning**: Remove outlier values (such as non-target user behavior) and fill in missing values (such as interest tags for new users). --- ### **2. Customer segmentation and behavior analysis** -**Subdivided dimensions**： -**Demographic statistics**: Age (such as 18-24-year-old students, 25-35-year-old workplace women, maternal and child groups). -**Consumption level**: High customer unit price users vs. Price sensitive users. -**Behavior mode**: High frequency and low order (hoarding type) vs. Low frequency and high customer orders (gifts/large purchases). -**Insights into shopping habits**： -**Time-sensitive type**: Pay attention to limited-time discounts and holiday promotions (such as March 8th, Double 11th). -**Social-driven**: I prefer the same style of Internet celebrities and popular products recommended by user-generated content (UGC). -**Scenario demand type**: such as workplace wear, fitness equipment, maternal and child necessities. --- ### **3. Recommend system optimization strategy** - **