About this project
End-to-end analysis of a synthetic retail dataset of 999 transactions across 13 stores in Kenya, Tanzania and Uganda (2024), carried out entirely in Power BI. Messy source data was cleaned in Power Query, then modelled in a star schema with a date table and DAX measures for month-over-month change, peak month detection and seasonality strength. The single-page dashboard answers four questions on revenue, products, seasonality and stores, and the report ends with recommendations on data capture, customer feedback and seasonal performance.
Key takeaways
Total revenue was KES 803,117. Tanzania led with 44.6% of revenue, driven by both transaction volume and higher spend per purchase. Beverages sold in high volume but earned the least revenue, pointing to a pricing gap, while rice alone brought in 9.2%. Revenue peaked in July at 1.7 times the weakest month, and three of the 13 stores generated about 40% of the total.