Analysis / Ecommerce and retail
Find products with unusually high return rates
Compare product return rates using sold unit denominators and mature windows, showing reason evidence, refund amounts and unmatched return records.
Ecommerce operator · Retail analyst
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Your starting prompt.
Join [uploaded return export] to [order lines] and calculate return rates by product, category and purchase cohort. Use sold units as the denominator and allow [return window] to mature before comparing cohorts. Show recorded reasons, refund amounts and unmatched returns. Produce a product review list with sample sizes and evidence, avoiding claims of product defects or customer misuse from reason codes alone.
Copy this starting point, then replace the placeholders with your own inputs.
Method and review
Keep the assumptions visible.
Reconcile orders, refunds, costs and fulfillment at a consistent grain. Use supplied exports when a native Connection is unavailable, and distinguish revenue from contribution.
For this task, check
Products sold recently have incomplete return windows and should not be compared as mature cohorts.
This starting prompt has no published worked example yet. Begin with a small sample and check the calculations before relying on the result.
Before you begin
- Bring exports you are allowed to upload, or data your Workspace can already query.
- Choose the reporting period and agree what each record represents.
Work with permitted files or queryable data. Verify the result against the original records.
Take the next step
Make it your investigation.
Copy the prompt above, create or open your Workspace, and paste it into Chat. Add the inputs you have permission to use and adapt the assumptions with your agent.
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