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11/07/2026 8:30 pm
TL;DR: Serial return abuse cost retailers an estimated $46 billion in 2024, while serial returners make up just 11% of shoppers and still distort demand signals, customer value models, and return workflows, according to Signifyd. Behaviour-based segmentation, not blanket friction, is the control point that changes outcomes.
NHIMG editorial — based on content published by Signifyd: What are serial returners in ecommerce and how to combat them
By the numbers:
- Serial returners make up just 11% of shoppers, but their return behaviour can still distort demand signals and customer value models.
- 40% of shoppers admit to committing at least, least one type of return abuse, according to the 2024 Global Consumer Returns Survey.
Questions worth separating out
Q: What breaks when ecommerce return controls do not separate loyal customers from serial returners?
A: When return controls do not distinguish between loyal customers and serial returners, merchants usually overcorrect.
Q: Why do serial returners create a data governance problem as well as a fraud problem?
A: Serial returners create a data governance problem because their behaviour changes the signals merchants use to make decisions.
Q: How can merchants tell the difference between a genuine shopper and a serial returner?
A: The best indicator is pattern consistency over time, not a single return.
Practitioner guidance
- Implement behaviour-based return segmentation Separate trusted shoppers from high-risk returners using frequency, reason-code consistency, SKU patterns, and timing.
- Centralise return, refund, and customer data Connect return systems, fulfilment records, and customer history into one decision layer so teams can compare pre-purchase and post-purchase behaviour.
- Use product-level return analysis Track which SKUs are returned most often and why, then separate product-quality issues from customer-driven abuse.
What's in the full article
Signifyd's full article covers the operational detail this post intentionally leaves for the source:
- The six serial return abuse patterns, including staging, wardrobing, bracketing, and product switching.
- The specific return data signals used to separate abuse from legitimate shopping behaviour.
- The centralised returns workflow that ties return, refund, exchange, and appeasement data together.
- The practical examples of how segmentation changes return policy decisions by customer type and SKU.
👉 Read Signifyd's analysis of ecommerce serial returners and return abuse →
Ecommerce serial returners: what teams need to act on now?
Explore further
11/07/2026 9:00 pm
Behavioural return abuse is a governance problem disguised as customer convenience. Merchants tend to optimise checkout fraud and overlook post-purchase trust decisions, which is where serial returners operate. The absence of joined-up identity, transaction, and return context means policies are tuned for the wrong stage of the customer lifecycle. Practitioners should treat post-purchase abuse as a distinct control domain, not a customer service exception.
A question worth separating out:
Q: Should merchants add more friction to returns when abuse rises?
A: Only selectively. Broad friction often punishes low-risk customers and weakens trust, while targeted friction lets merchants focus scrutiny on the shoppers and products most likely to be abused. The right approach is risk-based control, backed by centralised return data and clear segment rules.
👉 Read our full editorial: Ecommerce serial returners expose the governance gap in return controls
