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During peak season, more than $1.5B in Cyber 5 sales moved through Rithum across channels, regions, and categories. That’s the first wave. The second arrives after the shipping notifications stop: returns season. Calendars reset, staffing normalizes, and inventory shifts back toward warehouses. For the shopper, the decision often happened earlier—at checkout, when they relied on whatever the product page made clear and whatever it didn’t.
And as shopping shifts from product pages to AI agents that summarize and recommend, returns get harder to manage. When an agent gives the highlights, shoppers may never scan the full product detail page (PDP), compare specs side by side, or catch details that prevent a mismatch. If the summary is wrong, the purchase can still go through, and the retailer still absorbs the return. The harder part is accountability and diagnosis. If shoppers never see the original product page, you lose the trail of what they were told and you can’t easily trace a return back to a missing detail, a bad summary, or a mismatched listing.
How can you prepare for returns season? Use January to audit the promises you made during peak: which products were misunderstood, which channels dropped key details, and which fixes would have prevented repeat returns.
What returns show
In the U.S., online returns will top $363B, driven by a 24.5% return rate, according to Rithum’s 2025 global returns & profit impact report.
Rithum’s consumer research shows the same drivers repeating: 61% cite poor fit and over a third say the item didn’t match the description or photos.
Fit plays out differently by category. The “didn’t match” driver applies across categories.
Electronics returns can trace back to compatibility or what was included. Home returns follow scale and finish. Parts and industrial returns surface around fitment, specifications, or installation assumptions. Across categories, the same thing happens: unclear listings leave customers to guess.
In January, returns cluster. The same products come back through the same channels for the same missing details. In a Rithum returns fireside chat featuring Fabian Ortmann, Head of Returns, ZEOS; Kevin Brown, Director, Sales and Strategic Partnerships, Essendant Fulfillment Services; and Louis Camassa, Director of Product, Rithum, Ortmann argued that returns should be treated as a data source, not just a cost center, one of the clearest ways to pinpoint where expectations broke by product, channel, and market.
That framing changes how brands and retailers should use January. For returns season, the work should shift from cleanup to finding out what went wrong.
Where channel listings diverge
Channel differences start costing money in January. During peak, products are listed, reformatted, and rewritten to fit each marketplace’s format. That’s expected. The risk is what gets lost.
One channel may carry a compatibility field. Another may not. One listing spells out what’s included, while another assumes it’s obvious. One channel enforces short titles that drop (important) qualifiers where another keeps them in tact.
Louis described this in the fireside chat: when listings leave room for interpretation, customers answer the question themselves. Sometimes they guess right. Sometimes they don’t and that can lead to a higher rate of returns.
Why timing changes the math
Returns also don’t happen on your schedule. In the same conversation, Essendant’s Kevin Brown described the gap between decision and action. Customers often decide quickly whether they’re keeping something. The return itself may take weeks to arrive.
That delay narrows options. Inventory comes back later. Resale windows shrink. Seasonal goods lose flexibility. The cost isn’t only the refund. It’s what the business can no longer do with the product.
January is also when slow fixes compound. If product information is inconsistent across channels and updates move slowly, the same mismatch keeps shipping while teams debate which version of the truth is “correct.”
Rithum’s 2026 commerce readiness index points to why this is common. 91% of retailers and 78% of brands report poor data quality. Nearly 75% say inaccurate data leads to bad decisions.
When product records aren’t consistent or trustworthy, January becomes more triage than correction.
What policy can and can’t do
Return policy shapes demand, but it doesn’t explain repeat returns. Policy still matters because shoppers read it before they buy, especially when they’re unsure.
Rithum’s research shows return policies influence 41% of purchase decisions, 88% expect free returns, and 47% won’t click “buy” without them.
Most preventable returns aren’t caused by policy. They’re caused by uncertainty that existed before policy mattered.
What to check first
Start with the returns that teach you something. Look at SKUs that return quickly after delivery. Fast returns tend to signal expectation breaks, not end-of-season cleanouts.
Compare the same SKU across your top channels. Don’t assume it matches your internal record. Check titles, key attributes, images, what’s-included language, and variants.
Track “didn’t match description/photos” returns by channel. Rithum found that 33% of shoppers cite mismatch with the description or photos as a reason for returns.
Then move from insight to change quickly. If updates take weeks to reach every listing, the same mismatch keeps shipping.
Where AI fits
AI won’t fix messy product data. The eTail report’s preparedness finding reinforces the same issue returns already expose: only 2% of organizations describe themselves as fully prepared with structured product feeds and governance.
Rithum’s 2026 commerce readiness index shows how widespread the gap still is: 91% of retailers and 78% of brands report poor data quality, and nearly 75% say inaccurate data leads to bad decisions.
Whether the buyer is human, a marketplace algorithm, or an emerging shopping interface, the requirement doesn’t change. Product truth has to be consistent, category-appropriate, and fast to update. AI will move decisions faster, but it will also scale whatever your data gets wrong.
What to do next
The goal is to reduce the avoidable ones and recover value faster on the rest.
The brands and retailers who do this well don’t wait for the next peak. They use January to tighten product truth, align it across channels, and push corrections while the signal is still fresh. Learn how Rithum can help.
Talk to our teamJordan Christensen is Director, Client Experience, Retailers at Rithum.
