Car Dealership Revenue Analysis
Transforming $371M+ in sales data and 13,000+ transactions into an interactive Power BI dashboard that enables data-driven pricing and regional strategy decisions.
The Challenge
A multi-location car dealership network needed clarity on which pricing strategies actually drove revenue. Were high-volume discount models outperforming premium strategies? How did demand vary across regions? Without answers, capital allocation was based on intuition rather than evidence.
Approach & Methodology
- Data Modeling: Structured raw transactional data into a star schema optimized for analytical queries — separating fact tables (transactions) from dimension tables (products, locations, time).
- SQL Preprocessing: Cleaned and transformed data, handling missing values and outliers. Created calculated fields for profit margins, seasonal indices, and moving averages.
- Dashboard Architecture: Designed a multi-page Power BI report with executive summary, regional drill-down, pricing analysis, and trend views — each page answering specific business questions.
- DAX Measures: Built complex calculated measures for YoY growth, rolling averages, market share by region, and dynamic pricing comparisons.
- Strategy Segmentation: Classified dealerships into high-volume (competitive pricing, higher turnover) vs. premium (higher margin, lower volume) to compare performance.
Key Insights
The analysis revealed that high-volume pricing strategies outperformed premium strategies in 3 of 5 regions, but premium strategies dominated in urban markets with higher average income. This geographic nuance was invisible in aggregate reporting.
Demand-driven price fluctuations showed strong seasonal patterns — enabling predictive inventory management. The dashboard identified underperforming product-region combinations ripe for reallocation.