Sales Return Rate Analysis

I have a raw export of our sales transactions that flags every item returned by SKU, date, channel, and customer. My goal is to turn those rows of numbers into a clear picture of how each product is really performing, with return rates as the main metric. I need you to clean the data, calculate accurate return-rate percentages at SKU and category level, and surface any patterns—seasonal spikes, channel-specific issues, or correlations with shipping times—that explain why certain items come back more often than others.

Once the patterns are uncovered, translate them into plain-English insights I can hand straight to the merchandising team. Charts in Excel or Power BI are fine; if you prefer Python/Pandas or R, that works too as long as the workbook or notebook is included.

Deliverables:
• A concise report summarising key findings and recommended next steps
• An editable file (Excel, Power BI, Python notebook, or R script) containing the cleaned data, calculations, and visuals
• A brief walkthrough call or video recording so I understand exactly how you reached each conclusion

If this sounds straightforward to you and you have experience analysing sales data specifically for product return behaviour, let’s get started.