Sales Performance
Analysis

Why Were Some Regions Outperforming Others?

The sales team noticed significant performance gaps between regions but couldn't pinpoint the cause. Initial assumptions pointed to market size differences and varying levels of competition, but surface-level metrics weren't telling the full story.

Leadership needed a data-driven answer — not guesses — to decide whether to invest in underperforming regions or reallocate resources to high-performers.

Structured Analysis, Not Assumptions

Rather than jumping to conclusions, I designed a systematic analysis pipeline:

• Data Collection: Aggregated 18 months of transactional data from the company's SQL database, covering all regions and product categories.
• Cleaning & Preparation: Used Python (Pandas) to handle missing values, standardize category names, and create derived features like revenue-per-product and regional market share.
• Exploratory Analysis: Built variance decomposition models to isolate which factors most influenced regional performance differences.
• Visualization: Created an interactive Power BI dashboard enabling stakeholders to explore the data by region, product, and timeframe.

Product Mix Was the Real Driver

The analysis revealed a counterintuitive finding: regions with smaller markets but the right product mix consistently outperformed larger markets with generic inventories. Product mix alone explained 70% of regional sales variance.

This meant that the underperforming regions weren't "bad markets" — they were simply stocking the wrong products. The solution wasn't expansion; it was optimization.