SQL · Aggregate
Total Returns Analysis
Used SQL COUNT aggregate functions to calculate total ecommerce returns across the dataset.
📊 Result: 1,000 total returns identified — establishing the baseline for all further analysis.
SQL · GROUP BY
Most Returned Products
Used GROUP BY and COUNT to identify products with highest return frequency, ordered by total returns DESC.
📊 Crop Tops (115) and Cargo Pants (110) showed highest return rates — indicating possible sizing or quality concerns.
SQL · Analysis
Return Reason Analysis
Analysed customer return reasons using SQL grouping and sorting to surface the most impactful return drivers.
📊 Changed Mind (185) and Late Delivery (180) were the top reasons — pointing to logistics and UX improvement opportunities.
SQL · Comparison
Payment Method vs Returns
Compared return volume across multiple payment methods to identify correlation between payment type and returns.
📊 Credit Card (270) and Debit Card (254) showed higher return counts vs UPI (229) — useful for COD risk analysis.
SQL · AVG Function
Average Refund Delay
Calculated average refund processing time using the AVG() function to evaluate customer refund experience quality.
📊 Average refund delay: 5.56 days — a benchmark for improving refund SLA and customer satisfaction.