The January Returns Wave: Why Q4 Planning Ends Too Early

Retail peak season planning follows a familiar shape. Forecast the surge and hire seasonal staff in October. Run hard through Cyber Week and December. Release the team in the first week of January. Most retailers execute that plan competently every year.

The problem is what arrives next. According to the National Retail Federation, the vast majority of returns occur in January, after the winter holidays. Post-holiday returns land precisely when the seasonal team has gone.

Stat check: NRF and Happy Returns found that 43% of retailers hire seasonal staff specifically to handle returns. Those contracts typically end in early January, which is the month the returns actually arrive.

So this guide covers what the returns data shows and why the staffing timing fails. It also covers how to build capacity that runs through February.

What the Returns Data Actually Shows

The headline figures give the scale. NRF and Happy Returns put total US retail returns at $849.9 billion, a rate of 15.8% across all retail. That improves slightly on the 16.9% recorded the previous year. The trend is moving in the right direction.

Ecommerce runs considerably higher at an estimated 19.3%. Retailers surveyed expect roughly 17% of holiday sales to come back. That figure has stayed consistent across recent years. Those three numbers describe a predictable, plannable volume rather than a surprise.

Generational behaviour sharpens the picture. Consumers aged 18 to 30 averaged 7.7 online returns in twelve months. No other age group comes close. That cohort also grows fastest as a share of ecommerce spend. The pressure builds rather than eases.

Fraud adds another layer. Around 9% of all returns count as fraudulent. Close to two-thirds of consumers admit at least one costly returns behaviour. Just under half consider bending the truth acceptable when making a return. Dissatisfaction makes that far more likely.

The Staffing Mismatch Nobody Plans Around

Now the timing problem, which is where most of the avoidable cost sits. NRF found that 43% of retailers hire seasonal staff specifically to handle returns. Another 49% lean harder on third-party logistics partners, and 37% extend their return windows. All three responses acknowledge the same thing. Returns require dedicated capacity.

Look at when that capacity exists. Seasonal contracts overwhelmingly run October through the first week of January. Retailers size them around Black Friday, Cyber Week, and December delivery volume. They end as the holiday period closes. Then compare that against when returns arrive. NRF states plainly that the vast majority land in January, after the holidays. The staffing curve and the returns curve sit roughly a month out of phase.

The result is predictable. Returns hit a team that has just shrunk, at exactly the moment volume peaks and complexity rises. Processing times stretch, refunds slow, and customers who were satisfied in December become frustrated in January. Available capacity is also tightening. NRF expected retailers to hire between 265,000 and 365,000 seasonal workers, against 442,000 the prior year. Less seasonal labour enters a season where return rates have barely moved.

Why January Returns Cost More Than December Orders

A returns contact and an order contact consume different amounts of everything. The gap runs wider than most capacity models assume.

Start with the emotional starting point. An order enquiry comes from someone anticipating a purchase. A return comes from someone whose purchase disappointed them, or who received a gift they did not want. The second conversation begins lower and takes more work to recover. Complexity compounds it. Returns involve policy interpretation, condition assessment, refund method decisions, exchange offers, and occasionally fraud verification. Very few resolve in one scripted exchange, and each extra step extends handle time.

There is also a capacity conflict that rarely appears in planning documents. Happy Returns research found that 60% of retailers report having to choose between processing returns and fulfilling new orders. Those two workloads compete for the same people and hours. Fulfilment usually wins, because it generates revenue.

That trade-off is understandable and expensive. Every delayed return extends the period a customer spends unhappy and unrefunded. They spend it deciding whether to shop with you again.

The Verification Burden Lands on Your Thinnest Team

Returns fraud deserves separate attention, because the timing makes it particularly awkward.

Roughly 9% of returns are fraudulent. Common tactics include overstated return quantities, empty-box returns, and decoy returns containing counterfeit items. Detecting any of these requires an agent who is paying attention and knows what to look for.

January is when that attention is scarcest. The experienced seasonal agents have gone, the remaining team is stretched, and volume sits at its annual peak. Fraud detection is the first discipline to slip under those conditions. Meanwhile, the cost of over-correcting is equally real. An agent who treats a legitimate customer as a fraudster does real damage. That relationship took years of marketing spend to build. Getting this right needs judgment rather than a rule, and judgment comes from training and tenure.

Returns Have Become a Retention Moment, Not an Ending

The strategic framing has shifted, and two industry leaders have put it directly.

Katherine Cullen is NRF Vice President of Industry and Consumer Insights. She observes that “returns are no longer the end point of a transaction.” They instead offer a chance to create a positive experience, which can translate into brand loyalty.

David Sobie, co-founder and chief executive of Happy Returns, makes the point about acquisition rather than retention. He notes that return policies “have transformed into a strategic touchpoint for retailers.” They now influence how younger consumers shop from the outset.

Read those together, and the January team stops looking like a cost centre. That team handles the last interaction many holiday customers have with your brand. They handle it while those customers decide whether to come back. Retailers appear to be absorbing this. Nearly two-thirds of merchants called updating their returns process a six-month priority. Reducing return rates ranked among their top objectives for the year.

Building Returns Capacity That Runs Through February

Four adjustments close the gap between when you staff and when returns arrive.

Extend seasonal coverage to February, not January: This single change addresses most of the mismatch. If your contracts end January 7, you are releasing trained staff into the heaviest fortnight of the year. Extending to late February costs less than the service failure it prevents. Our nearshore pricing breakdown covers how coverage length moves a quote.

Model returns volume separately from order volume: Most forecasts treat post-holiday returns as a tail on the Q4 curve. They are a distinct curve with a different peak, a different contact mix, and a longer average handle time. Forecasting them together guarantees under-staffing one of them. The same discipline applies to WISMO contact forecasting during the delivery peak.

Retain your best seasonal agents specifically for returns: The agents who handled December volume well already know your catalogue, your systems, and your policies. Offering a subset an extension into February is cheaper than recruiting and training a separate returns team.

Prepare for exchange conversion, not just refund processing: An agent offering the right size, an alternative, or store credit converts a refund. That becomes a retained sale. That requires product knowledge and a little authority, neither of which a rushed temporary hire tends to have. Our work on apparel returns operations covers where that conversion happens most reliably.

When Returns Capacity Becomes the Constraint

Every recommendation above assumes you can find and keep the people. That assumption is getting harder to hold.

Seasonal labour availability keeps declining, and January and February are the least attractive weeks to staff. Retailers competing for the same shrinking pool in October rarely have anyone left to extend into February. The work itself is also more demanding than order-taking. Returns need policy fluency, judgment on fraud, and the composure to handle a disappointed customer. Building that capability in three weeks is difficult. Losing it in January is expensive.

Extending capacity externally solves the timing problem without carrying headcount through a quiet spring. Trained teams cover the full curve from October through February. They scale up for Cyber Week and stay through the returns wave. Our peak season support programs are built around that full-season shape rather than the December peak alone.

Category matters here too. Apparel carries the highest return rates in retail, while consumer electronics returns skew toward setup difficulty rather than fit. Those need different agent skills, and staffing them identically wastes capacity on one and starves the other.

Frequently Asked Questions

When do post-holiday returns actually peak?

January. NRF states that the vast majority of returns occur in January, after the winter holidays. That timing matters. Seasonal staffing contracts typically end in the first week of January, a month before returns volume normalizes.

What percentage of holiday sales get returned?

Retailers surveyed by NRF and Happy Returns expect around 17% of holiday sales back. That figure stays consistent across recent years. Across all US retail, the 2025 return rate was 15.8%, totalling $849.9 billion. Ecommerce specifically runs higher at an estimated 19.3%.

How many retailers hire seasonal staff for returns?

Some 43% hire seasonal staff specifically to handle returns. A further 49% increase their use of third-party logistics partners, and 37% extend return windows. Most of that seasonal capacity ends before the January returns peak arrives.

Why are returns contacts harder than order contacts?

They start from disappointment rather than anticipation. They also involve policy interpretation, condition assessment, refund decisions, exchange offers, and sometimes fraud verification. Few resolve in a single scripted exchange, so handle times run longer, and agent skill matters more.

How much retail returns fraud is there?

Around 9% of all returns count as fraudulent. Common tactics include overstated return quantities, empty-box returns, and decoy returns containing counterfeit goods. Close to two-thirds of consumers admit to at least one costly returns behaviour.

How long should returns capacity run?

Through February rather than ending in January. Extending seasonal coverage by six to eight weeks addresses most of the timing mismatch. Retaining a subset of experienced December agents costs less than recruiting and training a separate returns team.

Should returns support be outsourced?

It depends on your volume curve and seasonal labour access. Seasonal hiring availability is declining, and January and February are the hardest weeks to staff. Many retailers extend capacity externally across the full October to February window. That avoids carrying headcount into a quiet spring.

Conclusion: Plan the Curve, Not the Peak

Retail peak season planning has become genuinely sophisticated. Most sizeable retailers handle volume forecasting, channel mix modelling, surge staffing and carrier capacity well. The discipline is real. It simply stops about a month too early. The plan covers the sales peak thoroughly, then ends. The returns wave builds behind it and lands on whoever remains.

That timing is the whole problem, and it is unusually easy to fix. Extending seasonal coverage into February costs very little. A January service failure costs refunds delayed, exchanges missed, and customers who never come back.

So the question worth raising in your Q4 planning meeting is direct. On what date does your seasonal support coverage end, and what date does your returns volume actually peak? If those two answers are more than a week apart, you already know where next January goes wrong.