{"id":4815,"date":"2025-12-22T12:00:00","date_gmt":"2025-12-22T12:00:00","guid":{"rendered":"https:\/\/www.rithum.com\/?p=4815"},"modified":"2025-12-18T22:10:23","modified_gmt":"2025-12-18T22:10:23","slug":"returns-season-is-here-and-ai-raises-the-stakes","status":"publish","type":"post","link":"https:\/\/www.rithum.com\/blog\/returns-season-is-here-and-ai-raises-the-stakes\/","title":{"rendered":"Returns season is here. And AI\u00a0raises the\u00a0stakes\u00a0"},"content":{"rendered":"Reading Time: <\/span> 3<\/span> minutes<\/span><\/span>\n

During peak season, more than $1.5B in Cyber 5 sales moved through Rithum<\/a> across channels, regions, and categories. That\u2019s the first wave. The second arrives after the shipping notifications stop: returns season<\/a>. Calendars reset, staffing normalizes, and inventory shifts back toward warehouses. For the shopper, the decision often happened earlier\u2014at checkout, when they relied on whatever the product page made clear and whatever it didn\u2019t. <\/p>\n\n\n\n

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)<\/a>, 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\u2019t easily trace a return back to a missing detail, a bad summary, or a mismatched listing. <\/p>\n\n\n\n

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. <\/p>\n\n\n\n

What returns show\u00a0<\/h2>\n\n\n\n

In the U.S., online returns will top $363B, driven by a 24.5% return rate, according to Rithum\u2019s 2025 global returns & profit impact <\/em>report<\/em><\/a>.  <\/p>\n\n\n\n

Rithum\u2019s consumer research shows the same drivers repeating:<\/a> 61% cite poor fit and over a third say the item didn\u2019t match the description or photos. <\/p>\n\n\n\n

Fit plays out differently by category. The \u201cdidn\u2019t match\u201d driver applies across categories. <\/p>\n\n\n\n

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<\/a>. <\/p>\n\n\n\n

In January, returns cluster. The same products come back through the same channels for the same missing details. In a Rithum returns fireside chat<\/a> featuring Fabian Ortmann<\/a>, Head of Returns, ZEOS; Kevin Brown<\/a>, Director, Sales and Strategic Partnerships, Essendant Fulfillment Services; and Louis Camassa<\/a>, Director of Product, Rithum, Ortmann argued that returns should be treated as a data source<\/a>, not just a cost center, one of the clearest ways to pinpoint where expectations broke by product, channel, and market. <\/p>\n\n\n\n

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. <\/p>\n\n\n\n

Where\u00a0channel listings diverge\u00a0<\/h2>\n\n\n\n

Channel differences start costing money in January. During peak, products are listed, reformatted, and rewritten to fit each marketplace\u2019s format. That\u2019s expected. The risk is what gets lost. <\/p>\n\n\n\n

One channel may carry a compatibility field. Another may not. One listing spells out what\u2019s included, while another assumes it\u2019s obvious. One channel enforces short titles that drop (important) qualifiers where another keeps them in tact.  <\/p>\n\n\n\n

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\u2019t and that can lead to a higher rate of returns. <\/p>\n\n\n\n

Why timing changes the math\u00a0<\/h2>\n\n\n\n

Returns also don\u2019t happen on your schedule. In the same conversation, Essendant\u2019s Kevin Brown described the gap between decision and action. Customers often decide quickly whether they\u2019re keeping something. The return itself may take weeks to arrive. <\/p>\n\n\n\n

That delay narrows options. Inventory comes back later. Resale windows shrink. Seasonal goods lose flexibility. The cost isn\u2019t only the refund. It\u2019s what the business can no longer do with the product. <\/p>\n\n\n\n

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 \u201ccorrect.\u201d <\/p>\n\n\n\n

Rithum\u2019s 2026 commerce readiness index<\/em><\/a> 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. <\/p>\n\n\n\n

When product records aren\u2019t consistent or trustworthy, January becomes more triage than correction. <\/p>\n\n\n\n

What policy can and\u00a0can\u2019t\u00a0do\u00a0<\/h2>\n\n\n\n

Return policy shapes demand, but it doesn\u2019t explain repeat returns. Policy still matters because shoppers read it before they buy, especially when they\u2019re unsure. <\/p>\n\n\n\n

Rithum\u2019s research shows return policies influence 41% of purchase decisions,<\/a> 88% expect free returns, and 47% won\u2019t click \u201cbuy\u201d without them. <\/p>\n\n\n\n

Most preventable returns aren\u2019t caused by policy. They\u2019re caused by uncertainty that existed before policy mattered. <\/p>\n\n\n\n

What to\u00a0check first\u00a0<\/h2>\n\n\n\n

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<\/a>. <\/p>\n\n\n\n

Compare the same SKU across your top channels<\/a>. Don\u2019t assume it matches your internal record. Check titles, key attributes, images, what\u2019s-included language, and variants. <\/p>\n\n\n\n

Track \u201cdidn\u2019t match description\/photos\u201d returns by channel. Rithum found that 33%<\/a> of shoppers cite mismatch with the description or photos as a reason for returns. <\/p>\n\n\n\n

Then move from insight to change quickly. If updates take weeks to reach every listing, the same mismatch keeps shipping<\/a>. <\/p>\n\n\n\n

Where AI fits\u00a0<\/h2>\n\n\n\n

AI won\u2019t fix messy product data. The eTail report\u2019s<\/a> preparedness finding reinforces the same issue returns already expose: only 2% of organizations describe themselves as fully prepared with structured product feeds and governance. <\/p>\n\n\n\n

Rithum\u2019s 2026 commerce readiness index<\/a> 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.  <\/p>\n\n\n\n

Whether the buyer is human, a marketplace algorithm, or an emerging shopping interface, the requirement doesn\u2019t 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<\/a>. <\/p>\n\n\n\n

What\u00a0to do next\u00a0<\/h2>\n\n\n\n

The goal is to reduce the avoidable ones and recover value faster on the rest. <\/p>\n\n\n\n

The brands and retailers who do this well\u00a0don\u2019t\u00a0wait for the next peak. They use January to tighten product truth, align it across channels, and push corrections while the signal is still fresh.\u00a0Learn how\u00a0Rithum\u00a0can help.<\/p>\n\n\n\nTalk to our team<\/a>\n\n\n\n

Jordan Christensen<\/em><\/a> is Director, Client Experience, Retailers at Rithum.<\/em> <\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Reading Time: <\/span> 3<\/span> minutes<\/span><\/span>During peak season, more than $1.5B in Cyber 5 sales moved through Rithum across channels, regions, and categories. That\u2019s the first wave. The second arrives after the shipping notifications stop: returns season. Calendars reset, staffing normalizes, and inventory shifts back toward warehouses. 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