This project focused on uncovering patterns in sales and operational efficiency to support two business goals: boosting revenue and reducing costs. The dashboards were built using clean, structured transaction data (24,404 rows), with a combination of product-level, customer-level, and geographic dimensions.
Before diving into analysis, data readiness was validated. All transactions were complete with no missing invoice information—ensuring trustworthy results throughout the exploration.
Figure 1: Transaction Row Count
Analyzing sales geographically revealed clear state-level variations in transaction count and customer value. States such as Illinois and Virginia exhibited high order volumes with customers demonstrating elevated lifetime value (LTV), signaling strong retention potential. These regional insights help prioritize localized marketing or fulfillment strategies.
Figure 2: Map with State Order Count
Visualized customer lifetime value (LTV) distribution by U.S. state to identify high-value regions and outliers. States like Illinois and Virginia showed both high median and extreme upper-bound LTVs, indicating concentrated clusters of premium buyers. These insights can guide targeted retention strategies and localized marketing efforts in regions with higher revenue potential per customer.
Figure 3: LTV by Order State
To understand purchasing behavior, average quantity per transaction was analyzed across product SKUs. A few key products—such as Sheba Perfect Portions and Temptations Treats—stood out with significantly higher average quantities, indicating strong customer pull or suitability for bundling. This insight supports decisions on inventory prioritization and promotion planning. Figure 4: Average Quantity per Product
In parallel, a quantity distribution analysis showed that while the majority of transactions were low volume (1–2 items), there were long-tail purchase patterns suggesting opportunities for upselling and bulk incentive programs.
Figure 5: % Sales by Quantity
