Break down rising Wildberries returns by reason
Returns are visibly up and the dashboard reports won't say why. The rate gets counted by item and by reason, then converted into money.
Medium · 30 min · once a month
What you get
WB returns, May → June: 11.4% → 16.2% Reason Units Share Loss, ₽ Wrong size 412 38% 268,000 Defects and damage 190 17% 143,000 Doesn't match photos 151 14% 98,000 No reason given 338 31% — Return logistics counted separately: 121,000 ₽ Skipped: 47 items with under 20 orders in period
A sample on made-up data — your numbers will be your own.
Who it fits
- The warehouse complains about the flow of returns; sales is sure it's the listing.
- You need to work out whose area it is: the listing, the size chart, the packaging or the warehouse.
When it won't work
Items with one-off sales: on five orders a return rate means nothing at all.
How the agent does it
Choose the period
Say what we measure and what we compare against: last month, or the same month a year ago. That decides what turns out to be seasonality.
Run the breakdown
Returns are counted by item, category and warehouse. Defects, sizing and description mismatch stay apart — different people own each of them.
Filter out the noise
At small volumes a return rate jumps around on its own. Ask the agent to mark where a conclusion rests on twenty orders and where on five.
What you set
Connect Wildberries. From you: the period, what to compare against and how many orders an item needs before its number counts.
What you'll need
Starter prompt
Copy the prompt or open it straight in a chat with the agent.
Analyze Wildberries returns for [period] and compare with [the previous period]. Calculate the return rate against orders for each SKU, category and warehouse. Break it down by the reasons from the marketplace report; keep defects, "wrong size" and "doesn't match the description" separate — those are different areas of responsibility. Don't explain growth by seasonality until you show the same happened last year. If there is no data for last year, say so. Single out SKUs where the return rate is more than one and a half times the category average, and estimate the losses including logistics. Output a SKU table and the five items to start with.
Open in chatReview the analysis. List: — SKUs where the conclusion rests on fewer than [20] orders; — reasons you merged into one group, and on what basis; — cases where returns grew together with sales and nothing changed in relative terms; — losses with and without logistics — show both figures; — which of the conclusions can be verified from the data and which is your hypothesis.
A second prompt — the agent uses it to review its own work and show what's left for you.