Qianru YANG

Construction of sales forecasting model: I used SQL to clean member consumption data (purchase/return frequency, average order value). Created Excel pivot tables, analyzed competitor trends, and wrote a sales performance report. Identified high-return categories and churn points, proposed product optimizations that improved slow-selling inventory. User Preference Analysis:I utilized CRM system data to analyze VIP customer preferences using the RFM model, Segmenting customers based on their purchasing behaviors, identifying high-value groups. By collaborating with the store team, we developed personalized marketing strategies (belts and wallets are bundled for men, and shoes and bags are bundled for women) and implemented regular customer engagement plans, resulting in a 12% increase in repurchase rates among VIP clients in 3 months. Pop-up event effect tracking: Participated in the development of a customer satisfaction evaluation form, focusing on three key dimensions: shopping experience, purchase frequency, and price sensitivity. A pilot survey was conducted in core stores to assess customer behavior and validate consistency. Based on the findings, we optimized the sales strategy for products with price adjustments.

Construction of sales forecasting model: Based on historical sales data (SKU dimension), daily transaction flow and inventory turnover data, I used Python to clean the data and Excel to build a linear regression model to predict the sales trend of core products. Based on the prediction results, assisted in formulating the marketing strategy for the flash mob event. The sales of the flash sales event increased by 20% compared to the same event last year. User Preference Analysis: Through transaction data and user feedback forms, I performed cluster analysis on customer purchasing behaviors to identify the preferences of high-value customer segments. I proposed recommendations for bundling high-frequency products (101801 + scarf) and assisted the store in optimizing its layout strategy, successfully achieving an 18% increase in the repurchase rate. Pop-up event effect tracking: By establishing a comprehensive indicator tracking system from multiple dimensions such as average daily foot traffic, in-store conversion rate and transaction amount, the effect of the activity was accurately evaluated. Compared with the pop-up events of the same period last year, the in-store rate and purchase conversion rate of this year's event have increased by 30% and 25% respectively, and the ROI of the event has reached 400%. In addition, I also summarized the key performances through the data visualization report, providing specific suggestions and references for the optimization of subsequent activities.

Summer Promotion - Inventory Optimization: During the summer promotion period, to address inventory backlog affecting cash flow and sales space, I introduced product bundles aligned with seasonal trends. Additionally, I utilized Python and SQL to clean and analyze the previous day's sales data, forecasting the current day's inventory levels. By coordinating with regional managers and other stores, we ensured the supply of high-demand products and the redistribution of slow-moving items. Letting to a 20% improvement in inventory turnover and a 30% reduction in unsold stock, optimizing inventory management and enhancing the overall customer shopping experience.