Return Reasons Analysis From Exported Data
Find patterns that reduce returns.
The Open buttons send the prompt text as currently shown below to that site only when you click. Fill in the placeholders first.
Analyze these return records and tell me what to fix. Data (anonymized; columns such as SKU, reason, date, cost, customer notes): [PASTE_RETURN_DATA]. Total orders in the same period: [TOTAL_ORDERS]. Average order value and margin: [ORDER_VALUE_AND_MARGIN]. Return: 1. A summary table: overall return rate = returns / total orders; return rate by SKU and by reason category; cost per return including shipping and lost margin. Show the formulas. 2. The top five problems by total cost, each with a likely root cause (listing mismatch, quality, sizing, shipping damage, expectations). 3. Evidence for each cause from customer notes, as short quotes. 4. For each problem, one fix to try, how to measure it, and a target improvement. 5. SKUs to change, relist, or remove, with reasons. 6. Caveats: small samples, mixed reasons, missing data. If the data is insufficient for a claim, say so instead of guessing. End with three new fields to capture in the returns form.
Fill in before use
Optional: type your details and the prompt above updates. Nothing is saved or sent.
Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.