Detect High-Refund Dropshipping Products Before They Drain Your Profit
An n8n workflow that every week calculates each product's refund, dispute and return rate, and flags the ones crossing your danger threshold with an AI-summarized reason from the refund notes — so you spot the quietly toxic products and pause or fix them before they eat the profit your winners make.
- 1
Run weekly
A
Schedule Triggerset toWeeks(e.g. Monday morning) pulls a trailing window of orders and refunds. Weekly cadence smooths out noise and gives enough volume for meaningful rates. - 2
Fetch orders and refunds
HTTP Requestnodes pull the period's orders and refunds/disputes from the Shopify API, including each refund's reason or customer note — the raw material for both the rate and the cause analysis. - 3
Compute per-product refund rate
A
Codenode groups by product, dividing refunds by orders to get each product's refund rate over the window, and keeps only products above your threshold with enough orders to be significant. - 4
Summarize the cause with Claude
For each flagged product, an
HTTP Requestsends its refund reasons to Claude, which returns the dominant cause and a suggested action — fix the listing, add a sizing note, change supplier, or discontinue. - 5
Send the danger report
A
Slacknode posts the flagged products with their refund rate, AI-summarized cause and recommendation, ranked worst-first. You get a five-minute weekly review that protects the profit your winning products work so hard to make.
Frequently asked questions
Why track refund rate by product, not overall?
An overall refund rate hides the problem. Your store might sit at a healthy 4% while one product is refunding at 30% and silently erasing the margin from three good products. Because dropshipping margins are thin, a single high-refund item — wrong sizing, misleading photos, a defect-prone supplier — can turn your whole store unprofitable while the top-line looks fine. Per-product visibility is the only way to find and cut it.
How does it figure out WHY a product is refunding?
Refunds and disputes usually carry a note or reason (customer complaint, 'not as described', 'defective', 'never arrived'). The workflow feeds those reasons for each flagged product to Claude, which clusters them into the dominant cause. 'Runs two sizes small' is fixable with a sizing note; 'arrives broken' means a supplier problem; 'not as pictured' is a listing issue. Knowing the cause tells you whether to fix or drop.
What threshold should flag a product?
It's yours to set, but a common rule is flagging any product above roughly 2-3x your store's average refund rate, with a minimum order count so a single refund on a new product doesn't trigger a false alarm. The workflow computes each product's rate over a trailing window and compares against that threshold, which you keep in a config value.
Does it pause products automatically?
No — it recommends, a human decides. Some high-refund products are worth keeping with a fix (a sizing chart, better photos); others should be cut. The weekly report gives you the rate, the AI-summarized cause and a suggested action per product, so you make an informed call in minutes rather than never noticing the problem at all.
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