Holiday Returns: How to Prepare for the Post-Christmas Wave

Retail’s holiday headlines are about sales, but the bigger operational question is what comes back. The National Retail Federation (NRF) and Happy Returns estimated that $849.9 billion in merchandise would be returned in 2025, about 15.8% of annual sales. Online is heavier: an estimated 19.3% of online sales are returned.

Fraud rides along with that volume. The same report found that 9% of all returns are fraudulent, and retailers who track it report growth in overstated return quantities, empty-box returns, and decoy items.

This post covers four things worth knowing before the season starts: when returns actually arrive, what abuse looks like, how to set policy that rewards good customers, and what your return data can tell you the rest of the year.

The wave arrives after Christmas, not during Cyber Week

Returns concentrate in the last week of December, which catches many teams when they are thinnest. Adobe reported that one in eight returns in the 2024 holiday season happened between December 26 and 31, and it expected the same pattern in 2025.

2026 followed that script. Through December 12, Adobe saw returns running 2.5% below 2024, and in the seven days after Cyber Week they were down just 0.1%. Then returns rose 4.7% year over year from December 26 to 31. Adobe had forecast a 25% to 35% jump over the season-to-date level in that window, with volumes staying 8% to 15% above early-season levels through the first two weeks of January.

Two things follow from that timing. First, the cost of this season’s decisions lands on a different calendar than the revenue: refunds, restocking, and write-offs arrive after the sales numbers have been celebrated. Second, longer return windows stretch the tail. NRF found that 37% of retailers planned to extend return windows for the holidays, so a gift bought in early November may not come back until the new year.

What returns and refund abuse actually looks like

Return abuse is a spectrum, and treating it as one problem leads to blunt policy. At one end are outright fraud schemes. At the other are customers who simply stretch the rules.

NRF’s survey found retailers who track fraud seeing increases in three practices: overstating the quantity returned (71%), sending back empty boxes (65%), and returning counterfeit or decoy items in place of the real product (64%). On the shopper side, 45% told NRF that bending the truth on a return is acceptable in some situations. Keep the 9% fraud figure in proportion, though: it also means the vast majority of returns are legitimate.

A Retail TouchPoints piece adds the refund-side patterns: false damage claims, claims that an order never arrived, and serial refunds. It also names signals worth watching:

• repeated non-delivery claims from the same customer
• return weights that don’t match the item
• the same product returned across several accounts
• a customer who regularly disputes high-value orders

Then there is the gray zone. Bracketing (ordering several sizes or colors to keep one) and wardrobing (wearing an item and sending it back) are not fraud in the usual sense, but they create the same costs. The distinction matters because the fixes differ: fraud calls for verification and enforcement, while bracketing is mostly a product-information and policy problem.

Policy design: generous where it counts, guarded where it matters

The goal is to add guardrails the good customer never notices. NRF found that 82% of consumers say free returns are an important consideration when shopping online, up from 76% the year before, so blanket tightening carries a real conversion risk.

Here are approaches worth weighing. These are our recommendations rather than findings from the sources, and each has tradeoffs.

• Segment by risk, not by customer type. Shoppers with long, clean order histories get instant approval and prepaid labels, while unusual patterns get a quick manual look. The Retail TouchPoints piece describes retailers moving this way.
• Match the window to the product. One 30-day policy rarely fits both a $20 accessory and a $2,000 furniture order. Higher-value or higher-abuse categories can justify shorter windows or inspection before refund.
• Lead with exchanges and store credit. Both protect revenue and are usually cheaper to process than a refund. Present them as the fast option, not the restricted one.
• Let customers keep low-value items. For inexpensive products, a “keep it” refund can cost less than shipping, inspecting, and restocking the item.
• Make the rules easy to find. Clear policies reduce disputes and give your team something to point to when a claim is denied. They also matter beyond your site: Shopify’s agent discovery files include your store policies, so AI shopping assistants read them too.

Return fees are the bluntest tool. Used narrowly, they can discourage bracketing in a single category. Used broadly, they push away loyal customers, so test on one category before rolling out.

Planning the December 26 to mid-January window

Treat the post-Christmas stretch like a small peak with its own staffing plan. It is also the period when teams are most likely to be pulled onto other work, which is why it needs an owner before the season starts.

NRF found that retailers planned to handle holiday returns and fraud by leaning on third-party logistics partners (49%) and hiring seasonal returns staff (43%). Whatever your setup, a few decisions are worth making now. Again, these are our suggestions:

• Name an owner and a backup for returns from December 26 through mid-January.
• Set approval limits. Agree who can approve exceptions and large refunds so queues don’t stall.
• Write triage rules. Decide which returns are auto-approved, which get a quick review, and which go to a specialist.
• Check warehouse capacity. Ask your 3PL or warehouse how many returns per day they can process in those weeks and how inspection works.
• Decide when refunds are issued. Refunding at the carrier scan is fast and customer-friendly, but it removes your chance to catch empty-box and decoy returns. Refunding after warehouse receipt is safer but slower.
• Test the returns portal on desktop. Adobe found that 39.1% of returns happened on mobile, versus 52.4% of online spend between November 1 and December 12, so returns skew toward desktop more than purchases do.

The refund-timing decision is the one most worth discussing across teams. Support, finance, and operations often each own a piece of it, and the tradeoff between speed and verification belongs to all three.

Returns as data: what the pattern says about your catalog and systems

Every return reason is free product feedback, yet most merchants read returns only as a cost line. The Retail TouchPoints piece argues that leading brands feed return data back to merchandising and marketing, for example adjusting imagery or descriptions when a product shows a high fit-related return rate.

Three cuts of the data are worth reviewing after peak:

• By product: Return rate by SKU and variant, with reason codes. A high fit-related rate points to sizing guidance or photos, “not as described” points to content, and “arrived damaged” points to packaging or carrier choice.
• By customer: Repeat returners and repeat claimants compared with the long tail of occasional ones.
• By channel and date: Online versus in-store returns, and returns by original order date, since longer windows blur the picture.

There is also a systems question, and this part is our perspective. Fraud tends to slip through where systems don’t share information. If your storefront, order management, warehouse, and ERP each hold part of the story, nobody sees the same item returned across several accounts, or a refund issued before the package arrived.

The Retail TouchPoints piece flags the same blind spot, noting that without unified systems, teams can miss cross-channel return patterns. For merchants selling across DTC, retail, and wholesale, the practical question is whether returns data lands in one place, with consistent customer and order identifiers. If it doesn’t, that gap is worth closing before the next peak.

A Short Checklist Before Peak

Nothing below requires a build before Cyber Week. These are decisions and visibility, and most can be settled in a single working session.

• Review your return policy: window, fees, exceptions, and what changes for holiday purchases.
• Decide when refunds are issued and what gets verified first.
• Name a returns owner and backup for December 26 through mid-January.
• Confirm returns capacity and processing times with your 3PL or warehouse.
• Make sure return reasons are captured by SKU and variant, so January’s data is usable.
• Agree on which patterns trigger a manual review: repeat non-delivery claims, mismatched weights, the same item across accounts.
• Block time in mid-January to review what happened while it is fresh.

Returns will keep taking up more of the conversation as sales growth slows and customers lean on flexible policies. The merchants who handle it well are rarely the ones with the strictest rules. They are the ones who decided in advance how they would handle the wave, and who read their return data as feedback rather than loss. If you want a second set of eyes on root causes or on how returns data moves through your systems, that is the focus of our Returns Reduction Sprint.