Apparel
·
Lookback
A 60% restock rate was the signal: $300K in return abuse and $125K of VIP revenue left on the table.
A 12-month lookback across 85K orders. A 15% return rate looked ordinary. 40% of returned items never being resold did not.
in return abuse
written off a year
abuse detection rate
The challenge
40% of returned items could never be resold
This DTC women's fashion brand processes about 85K orders a year at an average of roughly $160. A 15% return rate by value is ordinary for the category and produced $1.9M in returns a year. The restock rate was not ordinary. Only 60% of returned items went back into inventory, and $760K was written off every year. A gap that size points to wardrobing, damage and abuse, but the team had no way to connect the write-offs to the customers causing them. Abusive returns also consumed about 230 hours of CX time a year. Each abusive refund was a loss twice over: once for the money returned, and again for stock that could not be sold.
What Pinch found
$300K of abuse, 13% of all returns
Connecting returns to what happened to each item showed where the write-offs came from. Most of the abuse sat with suspect returners: customers with extreme return rates who exploited the full return window. Egregious abuse, clear fraud and wardrobing, was smaller and the easiest to act on. Stopping that tier alone would recover $90K and free 70 CX hours. Promo abuse ran through multi-account discount schemes. Meanwhile 2.8% of customers drove 12% of purchases, about $1.7M a year, and saw the same return experience as everyone else.
Suspect abusers with extreme return rates who exploited the return window.
Egregious abuse: clear fraud and wardrobing.
Promo abuse through multi-account discount schemes.
Net VIP revenue upside. 2.8% of customers drive 12% of purchases.
Suspect abusers exploiting the return window were the largest pool. On the other side, 2.8% of customers drove 12% of purchases with no better experience.
The response
Block the egregious tier, reward the top 2.8%
Pinch recommended a tiered response. Egregious abusers would be denied returns and lose future orders. Suspect returners would be refunded only when the item reaches the warehouse, with photos and closer inspection, so a worn item is caught before money goes out. Linked accounts would be blocked from reusing discounts. Loyalists would get instant refunds at label scan and exchange rebates, an estimated $125K in net new revenue. In total, $450K+ a year was addressable.
Egregious abusers
Block
Deny returns and cancel future orders.
Suspect returners
Friction
Refund on warehouse receipt, require photos and inspect more closely.
Promo abuse
Block
Stop linked accounts from reusing discounts.
Best customers
Reward
Instant refunds at label scan and exchange rebates.
How Pinch worked
Write-down data trained the models
This brand had disposition data, and Pinch put it to work. Ensemble models were trained on 12 months of order, return and disposition data, then validated on a hold-out set, reaching 98% accuracy and a 90% abuse detection rate. Write-down intelligence connected each return pattern to the item's outcome, so the models learned which behavior led to stock that could not be resold. Pinch ID clustered emails, addresses, phones, devices and payment methods into single customers.
Pinch ID
Identity clustering across emails, addresses, phones, devices and payment methods.
Write-down intelligence
Connects return patterns to what actually happened to the item.
Ensemble abuse models
Trained on 12 months of data and validated on a hold-out set.
VIP uplift
A better return experience for loyalists.
The takeaway
Brand details are anonymized. Figures come from the brand’s own order and return data.
Industry
Apparel
Size
$10M to $25M GMV
Platform
Shopify
Solutions
Return Abuse
Lookback
12 months
At a glance
~15%
Return rate by value
~$160
Average order value
$1.9M
Annual returns
$760K
Inventory written off a year
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Free lookback · for qualified brands
See what you lost last year. Free.
01
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02
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What you lost to return and claims abuse, and who took it.
03
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