This project built a reusable data analytics and reporting framework centered on operations monitoring and product selection analysis. It integrates sales, inventory, page traffic, refunds, and product-dimension data from multiple business systems, performing data cleaning, field standardization, and metric alignment to create a stable analytical dataset. Based on this foundation, a product-performance-centric selection metrics framework was designed, systematically covering key operational indicators including sales volume, revenue, growth rate, page views, conversion performance, and refund rate, with structured output as daily operations briefs to provide the team with rapid decision-making dashboards.

During analysis, the focus was on performance gaps at both product and category levels. Trend analysis and comparative analysis were used to identify sales fluctuations and anomalies, with special attention to products with abnormal refund rates. By combining product attributes, sales rhythm, and inventory status, potential root causes were identified to help the business team promptly adjust operations strategies and product management plans. Additionally, the metrics framework was applied to product selection evaluation, providing data support for candidate products across three dimensions: growth potential, stability, and risk performance, offering quantitative evidence for new product introduction and product portfolio optimization.