This project analyzes retail transaction data to identify sales trends across different product lines, cities, and customer segments. Using SQL for data extraction and Excel for visualization, this dashboard provides actionable insights into revenue drivers.
- Top Performer: "Notebooks" generate the highest total revenue (~$13,700) and also have the highest average transaction value.
- Opportunity: "Shampoo" and "Orange Juice" have lower revenue figures, suggesting a need for bundling strategies or marketing adjustments.
- Competitive Landscape: Revenue is tightly distributed across major cities. Los Angeles leads slightly ($19,894), followed closely by New York and Chicago. This indicates a consistent market demand across all three regions.
- Membership Split: The customer base is fairly balanced, with Member (54%) slightly outpacing Normal (46%) customers. This suggests a successful loyalty program adoption.
- SQL: Used to clean the raw data, handle null values, and aggregate sales by category and location.
- Excel: Used for Pivot Tables and generating the final visualization charts.
- Total Revenue by Product Line & City.
- Average Transaction Value (ATV) per product category.
- Customer Distribution (Member vs. Non-Member).
retail_sales_analysis.sql: Contains the SQL queries used for data aggregation.sales_data.xlsx: The raw dataset and pivot tables.images/: Folder containing dashboard visualizations.