SuperStore Sales Insights Analysis Dashboard System

Abstract

Retail transaction records contain valuable evidence about revenue, customer behaviour, product demand, shipping efficiency and regional performance, yet raw CSV exports are difficult to interpret without a reproducible analytical workflow. This paper presents the SuperStore Sales Insights Analysis Dashboard System, an end-to-end Business Intelligence solution integrating Python, MySQL and Microsoft Power BI. Pandas and NumPy clean 9,994 order-line records spanning 2015–2018 by correcting date types, removing incomplete postal-code records, eliminating a duplicate transaction and validating order-to-shipment consistency. The cleaned data are stored in MySQL, where fourteen analytical queries use aggregation, common table expressions and window functions to answer business questions. Power BI then provides coordinated Sales, Customer and Product dashboards driven by reusable DAX measures and interactive slicers. The system reports approximately $2 million in revenue across 4,916 orders, 793 customers and 1,860 products. Technology contributes 36.67% of sales, followed by Furniture at 32.11% and Office Supplies at 31.22%. The completed workflow demonstrates traceable, decision-ready retail analytics for non-technical stakeholders.