Holiday Returns Workflow: RMA Steps, Disposition Rules and Fraud Checks for the Second Peak

The holiday rush has a quiet second act. Orders slow down, the marketing emails stop, and then the boxes start coming back. For many sellers, the post-holiday returns wave is where margin quietly disappears: items sit unprocessed, refunds are issued without inspection, good stock is not put back on the shelf, and nobody can say how much cash will leave in January.

The numbers explain why it deserves a plan. In the National Retail Federation (NRF) and Happy Returns 2025 Retail Returns Landscape report, as summarized by trade press, retailers estimated total US returns at about $849.9 billion in 2025, or 15.8 percent of annual sales. Online returns were expected to reach 19.3 percent of online sales, retailers expected about 17 percent of holiday sales to come back, and about 9 percent of returns were estimated to be fraudulent. Those are survey-based benchmarks from a past season and a specific population of retailers, not a forecast for your store. Their value is as a reason to measure your own numbers and plan around them.

This guide is for operations teams, e-commerce managers and small-business owners who handle returns, including sellers who fulfill through a supplier or dropshipping partner. It gives you a complete workflow you can write down, assign and test before the wave arrives.

Why returns are a second peak

Returns do not arrive on the same day as sales. They lag. A gift bought in late November may be opened in late December and returned in January. A PYMNTS report on the post-holiday period quotes the idea that the period after Christmas marks the start of a second peak that moves in reverse, and it cites UPS as marking the peak of returns on the Monday after New Year's in 2025. Carrier pricing and surcharges may also run into January, as the same report notes.

Three operational consequences follow:

• Your busiest returns weeks fall after your peak staffing ends. Seasonal hires leave just as volume turns around.
• Cash goes out after cash came in. Revenue booked in November and December can be refunded in January.
• Policies set in a hurry are hard to change. A return window or promise made during Black Friday applies when the returns arrive, not when you made it.

Step 1: Set the return policy before the rush

A returns workflow starts with a policy that staff can apply without guessing, and customers can read without a lawyer.

Decide and write down:

1. Return window. The number of days, and what date it starts from. Many sellers extend windows for holiday purchases. If you do, state the end date plainly, for example "items bought between November 1 and December 24 can be returned until January 31." Show it at checkout and in the order confirmation.
2. Condition rules. Unused, in original packaging, tags attached, or whatever applies to your products. List exceptions, such as final sale, hygiene items, personalized items.
3. Refund method. Original payment method, store credit, or exchange. Many teams make exchange or store credit the default offer while keeping cash refunds available, where law and policy require it.
4. Who pays for return shipping. Free, deducted from the refund, or conditional on reason. Decide it up front and calculate what it costs.
5. Proof required. Order number, email, photos of damage.
6. Contact path. One email address or form, with a response time you can meet.

Local consumer law may limit what you can require or exclude, and rules differ between places. Check your obligations with a qualified adviser.

If you dropship: your customer's return policy has to work with your supplier's. Before peak season, get written answers to these questions from every supplier you rely on:

• Do they accept returns at all, and within what window?
• Who pays for return shipping, and who inspects?
• Do they refund you, issue credit or charge a restocking fee?
• Where do returns go, and is there a local return address?
• How long does it take them to process a return and confirm a refund?

If a supplier cannot accept returns, your policy must say so honestly, or you need a plan for absorbing the cost. A policy that promises more than your supplier will honor creates losses and unhappy customers.

Step 2: Build the RMA flow

Every return should have an authorization record from the start. Without it, you cannot match a parcel to an order, you cannot see how many are in transit, and you cannot spot patterns.

A simple RMA flow:

1. Request. The customer fills in a form or emails you: order number, items, reason, preferred outcome.
2. Check eligibility. Is it inside the window, and does the item qualify under policy? Use a short decision rule, and only escalate exceptions.
3. Issue the RMA. Create a record with a number, the order link, items, reason code, and expected action. Send the customer return instructions and a label if you provide one.
4. Track inbound. Watch for the parcel. Flag returns not received within a set number of days.
5. Receive and scan. Match the parcel to the RMA, record the date and who received it.
6. Inspect and grade. Apply the grading scale in Step 3.
7. Decide disposition. Apply the rules in Step 4.
8. Settle. Issue the refund, credit or exchange, and close the record.
9. Record the reason. Use the data later to fix products and descriptions.

Reason codes worth using: wrong size or fit, not as described, damaged in transit, defective, arrived late, changed mind, received duplicate, gift not wanted. Keep the list short, so staff pick one quickly and the data stays usable.

Targets to set: time from receipt to inspection, time from receipt to refund, and share of returns with a complete record. Pick numbers you can meet. The goal is predictability.

Step 3: Grade items on arrival

Grading decides everything that follows. A simple scale keeps it fast.

Grade	Meaning	Typical next step
A	Unopened or like new, complete with packaging and accessories	Return to sellable stock
B	Opened, works, packaging worn or missing	Repackage, or sell as open-box
C	Cosmetic damage or missing parts, still functional	Refurbish if cost-effective, otherwise discount channel
D	Not functional, heavily damaged or unsafe	Claim from supplier or carrier where possible, then recycle or scrap
X	Does not match the order, suspected fraud, or wrong item sent back	Hold and escalate

Make grading consistent:

• Write the criteria for your top product categories, with photos of examples.
• Require a photo of any item graded C, D or X.
• Check serial numbers or lot codes for products where they matter.
• Separate the person who grades from the person who approves high-value refunds where you can.

Step 4: Write disposition rules

Disposition is where the money is. Without written rules, staff either restock everything, which puts bad items back in front of customers, or scrap too much, which burns margin.

A decision table you can adapt:

Condition	Value of item	Default action
Grade A	Any	Restock, update inventory immediately
Grade B	Above your repackaging cost	Repackage and restock, or list as open-box
Grade B	Below your repackaging cost	Discount channel or bundle
Grade C	Refurbish cost under a set share of resale value	Refurbish, then restock or sell as refurbished
Grade C	Refurbish cost above that share	Liquidate, donate or recycle
Grade D	Supplier or carrier will credit	File the claim, then recycle or dispose
Grade D	No claim possible	Recycle or dispose according to local rules
Grade X	Any	Hold, document, escalate before any refund

Set the cost thresholds from your own numbers, not a template. Local rules apply to disposal of certain products, such as electronics, batteries, cosmetics and food contact items, so check your obligations.

Update the system the same day. Items that are physically back on the shelf but not in inventory are lost sales. Items in inventory that are not physically sellable are future complaints.

Step 5: Triage for fraud without punishing honest customers

The NRF and Happy Returns report estimated about 9 percent of returns as fraudulent, and it said most retailers were using AI to detect or prevent return fraud. That figure is a survey estimate. Your own rate may be lower or higher, but it is a reason to build simple checks into the workflow.

Common patterns:

• Empty box or wrong item returned.
• A cheaper or used item swapped for the one sold.
• Repeated returns from the same customer, address or payment method.
• Returns claimed as not received, with delivery proof showing otherwise.
• Returned items that show signs of heavy use, such as worn clothing.
• Refunds requested after the window using unusual stories.

Proportionate controls:

1. Inspect on receipt before refunding for high-value orders or flagged customers. For low-value orders, consider refunding on carrier scan to save cost, where your numbers support it.
2. Review repeat returners against a threshold you set, and decide a response in advance.
3. Match serial numbers for products that have them.
4. Require photos for damage claims.
5. Keep records of every exception decision, with a reason, so decisions are consistent.
6. Separate duties. The person who approves refunds above a limit should not be the person who received the item.
7. Treat customers fairly. Most returns are honest. Do not accuse anyone without evidence, and give a clear path to appeal.

Be careful about automation here. Rules or tools that flag customers can be wrong and can be biased in ways that are hard to see. Keep a person in the loop for any decision that denies a refund, and review flagged cases regularly. Our guide to human-in-the-loop approvals covers how to design that check.

Step 6: Forecast returns with a lag

Returns are one of the most predictable parts of peak season if you use the right method. Take sales by week and apply a return rate with a delay.

A simple model:

1. Take your forecast or actual sales by week.
2. Choose a return rate. If you have history, use it by category. If you do not, use a range as a starting point, such as the benchmarks above, and replace it with your own numbers as data arrives.
3. Spread the returns over the following one to three weeks after each sales week, based on how long your customers and carriers take.
4. Convert dollars to units and to parcels so you can plan labor and space.
5. Update weekly with actual receipts.

An illustrative example. The numbers are invented to show the method. Suppose a store expects $200,000 in holiday sales and plans on a 17 percent return rate. That implies about $34,000 in merchandise coming back. If average order value is $80 and the average return is $60, that is roughly 570 returned parcels. If they arrive across five weeks with a peak in the first week of January, the team can see how many parcels per day they need to process in that week and staff for it.

Include the cash view. Returns reduce revenue after you booked it. Ask your accountant how to treat a returns reserve in your books, and make sure your cash plan includes January refunds. Do not let the first sign of the problem be a bank balance.

Step 7: Staff and space for the wave

Retailers planned a mix of responses in the NRF and Happy Returns report: about 49 percent planned to rely on third-party logistics partners, 43 percent planned to hire seasonal staff for returns, and 37 percent planned to extend return windows, as one summary of the report puts it. Those are intentions from a survey, not proof of results, but they show the standard levers.

Practical planning:

• Space. Returns need a staging area: unopened, to inspect, to refurbish, and to dispose. Mark zones clearly so items do not mix with sellable stock.
• People. Keep some seasonal capacity into January. Cross-train people from outbound so they can move across.
• Tools. Use a simple scan-and-grade process with a handheld scanner or a phone. A spreadsheet works for small volumes. Make sure scan-in, grade and disposition are recorded.
• Partners. If you use a third-party logistics provider, get dock capacity, turnaround and fees in writing. Our holiday peak readiness checklist covers wider peak planning.
• Customer service. Returns questions rise after the holidays. Prepare templates and an FAQ that match your policy.

Step 8: Report and learn

Returns data is a gift. It tells you what is wrong with your products, pages and sizing before reviews do.

A weekly returns dashboard:

Measure	Why it matters
Returns received, units and dollars	Tracks the wave against the forecast
Return rate by product and category	Finds problem products
Top reasons	Points to fixable causes: sizing, photos, descriptions
Time from receipt to refund	Customer experience and cash
Share restocked, refurbished, liquidated, scrapped	Recovery rate
Suspected fraud cases and outcomes	Whether controls help
Open RMAs older than your target	Backlog risk

Use it: fix product pages with the most "not as described" returns, update size guides, drop products with consistently high return rates or defects, and tell suppliers about quality problems, with data.

Common mistakes to avoid

• Setting the policy after the rush. Customers have already relied on what you showed them.
• Refunding without inspecting. It is fast, but it can be expensive for high-value items.
• Restocking everything. Bad items back on the shelf create a second return.
• No disposition rules. Staff improvise, and results vary by person.
• Ignoring the supplier's terms. A promise you cannot back with your supplier becomes your loss.
• No cash plan. January refunds hit when revenue is lowest.
• Treating all returns as fraud. Most are honest, and heavy-handed rules harm loyal customers.
• Letting data die in the inbox. Reason codes are only useful if someone reads them.

A one-page checklist

Copy this into your runbook.

• Return policy written, with window end date shown at checkout and in confirmations.
• Supplier or partner return terms confirmed in writing.
• RMA process documented, with reason codes and targets.
• Grading scale defined, with photo examples for key categories.
• Disposition table approved, with cost thresholds from our own numbers.
• Fraud triage rules written, with a human review for any refund denial.
• Weekly returns forecast built, with a one to three week lag.
• Returns reserve and January cash view agreed with finance.
• Staffing and space planned through January.
• Weekly returns dashboard owner named.
• Customer service templates match the policy.

Key takeaways

• Returns are a second peak that arrives after sales, often when seasonal staffing has ended, so plan into January.
• The NRF and Happy Returns 2025 report estimated about 17 percent of holiday sales returned and about 9 percent of returns fraudulent. Treat these as survey benchmarks and measure your own.
• Give every return an RMA record, grade items on arrival and apply written disposition rules.
• Use proportionate fraud checks, keep a human in the loop for refund denials and treat honest customers fairly.
• Forecast returns with a lag after each sales week, and book a returns reserve so January refunds are planned.
• If you dropship, confirm each supplier's return terms in writing before you promise customers anything.
• Use reason codes and a weekly dashboard to fix products, pages and supplier quality.

Sources and further reading

The pages below are where we checked the facts in this guide. Surveys and benchmarks change each year, so check for newer editions before you plan. Written by Sam at OpsGrit with AI assistance and an automated editorial review. If you spot something wrong, tell us.

• Chain Store Age: NRF consumers expected to return nearly $850B of merchandise in 2025. Trade-press summary of the NRF and Happy Returns 2025 Retail Returns Landscape figures.
• PYMNTS: Why peak retail no longer ends on Christmas Day. Post-holiday returns timing, carrier peak dates and cost pressures.
• ShipAid: NRF holiday returns benchmark and planning checklist. Retailer plans for third-party logistics, seasonal staff and extended windows, and a planning checklist. A third-party summary of the NRF report.
• FreightWaves: Spike in holiday returns highlights waste, inefficiency in reverse logistics. A December 2019 article, useful for context on reverse-logistics inefficiency, and not current data.

The policy checklist, RMA flow, grading scale, disposition table, example and dashboard are OpsGrit's own and are starting points. Adapt them to your products, suppliers and local law.

Related guides

• Holiday Peak Season Operations Readiness Checklist
• Human-in-the-Loop AI Approvals
• How to Write an SOP People Follow
• Operational Incident Response Runbook
• System of Record and Source of Truth