Retail Case Study

A practical retail analytics case study combining relational datasets across customer demographics, transaction logs, and product hierarchies. The project focuses on multi-table data merging, cleaning, sales performance metrics calculation, store channel evaluation, and customer purchasing pattern identification.

• Merged multiple relational datasets (Customer demographics, Product categories, and Transaction records) using primary and foreign keys.
• Validated transactions, separated returns and cancellations, and normalized time-series date formats.
• Calculated key retail metrics: total sales revenue, average transaction value, product return rates, and customer purchase frequency.
• Evaluated sales performance across store channels (e-Store vs. brick-and-mortar formats) and customer demographic tiers.
• Visualized monthly sales trends, top revenue-generating categories, and channel distribution.

• Strong command of relational data manipulation and multi-table joining in Python.
• Direct understanding of standard retail metrics (AOV, return rates, category contribution).
• Translates transactional data into strategic merchandising and operational takeaways.