This is an end-to-end Data Analytics and Engineering project. I extracted a raw dataset of over 541,000+ retail transactions, performed data cleaning and transformation using MySQL, and built an RFM (Recency, Frequency, Monetary) statistical model to segment customers. The final output is an interactive, premium-UI dashboard built in Power BI.
- Database Management & ETL: MySQL (Data Cleaning, Transformation)
- Data Modeling: SQL Aggregations, Date/Time formatting, RFM Logic
- Data Visualization & BI: Power BI, DAX (Data Analysis Expressions)
(Here is the preview of the final Power BI Dashboard)
- The "Whales" (Top Customers): Successfully identified 4,338 unique VIP customers. The scatter plot reveals that a small fraction of recent, high-spending customers generate the majority of the $8.91M revenue.
- Sales Trend: The monthly revenue trend shows consistent growth throughout 2011, peaking massively in November 2011 just before the dataset cutoff.
- Product Performance: Filtered and visualized the Top 10 revenue-generating products out of thousands of SKUs to help the business focus on high-yield inventory.
- Data Cleaning (SQL): Handled missing
Customer IDs, removed negative quantities (refunds), and bypassed MySQL safe-update modes to convert raw text dates into standardDATETIMEformats. - RFM Modeling (SQL): Wrote complex queries to aggregate data per customer, calculating their Recency (days since last purchase), Frequency (total orders), and Monetary value (total spend).
- Visualization (Power BI): Connected Power BI directly to the local MySQL server. Engineered new DAX columns for Revenue calculation and designed a custom dark-mode UI for executive presentation.