Return Lens
Your returns data, audited like an operator would.
A ranked, dollar-quantified read of your returns program — serial refunders, size-bracketing, wardrobing signals, and high-return SKUs — computed from your own order and return exports. No account access, no API key. Delivered as a branded report.
$1,000/ audit · delivered within days
What we check
Six categories, every figure grounded.
Outputs are review cohorts, never an auto-deny list — where the data to assess something isn’t available, the report says so.
Cross-email identity resolution
Customers checking out under more than one email at the same shipping address — the simplest way a per-account return limit gets defeated.
Serial-refunder scoring
Every repeat customer scored against this store’s own cohort average return rate, not an industry guess.
Size-bracketing detection
Orders that buy multiple sizes of the same style and return most of them — apparel/footwear’s single most common return-abuse pattern.
Wardrobing timing signal
Returns filed in the window that looks like "wore it, then returned it" — a signal to investigate, never an auto-deny basis.
High-return SKU clustering
Products whose return rate outpaces the rest of the catalog, with the dominant return reason — usually a sizing or listing problem, not a customer one.
Policy-tier recommendation
A concrete cutoff: who keeps instant refunds, who moves to an inspection-required tier — based on this store’s own numbers.
Sample finding
Not a checklist. An answer.
Illustrative — the format every real finding follows.
high · serial-refunder scoring vs. cohort baseline
2 customers return at 3x+ the cohort average (21%) — the highest returns 75% of their orders.
Next step: Move flagged customers to an inspection-required refund tier for their next order; re-score after 90 days.
$450 in refunds among flagged customersRequest your audit
Tell us where to send it.
We’ll follow up by email with instructions for a 12-month order + return CSV export.