Returns Are No Longer a CX Feature – They’re a Balance Sheet Liability

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E-commerce returns have exploded in scale, and retailers are grappling with the cost. What was once a customer-friendly “free returns” policy is now being reined in: major brands are imposing flat fees or stricter rules. Industry data show this is no flash in the pan. For example, the National Retail Federation (NRF) and Happy Returns report finds 72% of U.S. retailers now charge customers for at least one return method, and nearly three-quarters of all stores levy some kind of return fee. In practice, major retailers have quietly added fixed costs to returns: Marshall’s and T.J. Maxx each deduct $11.99 per returned package, Macy’s charges $9.99 per return, and JCPenney and J.Crew roughly $8. In short, returns have shifted from being a cost of customer service to becoming a line item on the balance sheet.

  • Industry Benchmarks: ~72% of retailers charge for at least one return method; nearly 75% of stores have return fees (NRF).
  • Retailer Examples: Marshall’s/TJ Maxx – $11.99 per return; Macy’s – $9.99; JCPenney – $8; J.Crew – $7.50.

These figures mark a sharp change. Consumer shopping sites and news outlets report that many leading chains introduced return fees in late 2024/2025. ABC News (via ABC15) confirms that “J. Crew, Macy’s, JCPenney and more have fees for some returns on holiday gifts”. Similarly, a Money magazine analysis notes that over the past year “retailers have slowly been rolling back one of online shopping’s biggest perks: free returns,” as nearly three-quarters of retailers now charge for returns. These fees include shipping surcharges, restocking charges, and other handling fees, all aimed at recouping the hidden cost of returns.

Ecommerce Returns: Why Free-Returns Policies Broke at Scale

The U.S. online shopping boom has made ecommerce returns a massive operational burden. During the pandemic and beyond, consumers ordered more and returned more. According to the NRF/Happy Returns 2025 report, the average ecommerce return rate was 16.9% in 2024, as reported by the National Retail Federation, and is estimated to reach 15.8% of annual sales in 2025—roughly $850 billion of merchandise. The NRF report also notes that collectively, consumers returned products worth a staggering $890 billion in 2024. The average ecommerce return rate can vary significantly by product category, with clothing and shoes typically having higher rates. (For perspective, returns accounted for 10.6% of U.S. retail sales in 2020 and jumped to 16.6% in 2021.) Handling this torrent of returned goods has become expensive. In 2025, approximately 19.3% of all online sales are expected to be returned, making effective management essential for protecting profit margins and maintaining customer loyalty.

Returns impose multiple overlapping costs on retailers. Transportation and logistics are especially costly. As one analysis notes, each $100 returned item costs a retailer about $32 to process and resell. Retailers effectively pay twice to handle the same item: they ship it to the customer, and then pay again for the return transit and processing. Warehouse labor, inspection, and repackaging add more expenses. In the aggregate, the NRF estimates returns now cost “almost $890 billion each year” to U.S. retailers. Retailers are predicted to spend 8.1% of total sales on reverse logistics. Even that colossal figure likely understates the burden, since many returned items cannot be sold at full price. Returns often incur additional markdowns or liquidations, eroding margins further. In fact, studies show a significant share of returned merchandise (often cited around 10–25%) cannot go back to inventory at full value. The environmental impact from high return volumes also contributes significantly to carbon emissions and landfill waste, especially in fast fashion and electronics.

In summary, free returns became unsustainable once volumes grew large. Retailers report that the principal drivers of new fees are soaring carrier costs and the expense of reverse logistics. One supply-chain analysis observes, “Returns also drain resources because they require reverse logistics: shipping, inspecting, restocking, and often repackaging items”. Third-party logistics (3PL) providers can handle the entire order fulfillment process, including returns, to help streamline operations. The result is that retailers can no longer absorb returns as a marketing perk without jeopardizing profitability. As Happy Returns CEO David Sobie puts it, “Return policies and their overall process have transformed into a strategic touchpoint”, forcing retailers to modernize how they manage returns. A clear and generous ecommerce return policy, including a well-defined return window and specifying the purchase date, can increase sales without increasing the volume of returns. Retailers may also implement restocking fees to recover costs for large or costly items. Ecommerce return fraud is a growing concern, making it critical for online retailers to monitor customer returns closely. Many customers, especially online shoppers, review return policies before making online purchases, and making returns easy builds trust and encourages repeat business. Many customers prefer to return items in-store if a physical or brick and mortar store is available, which can enhance their shopping experience and lead to additional in store sales. Returns can be an opportunity for more business if handled well, as customers may return to shop again after a positive return experience, supporting customer loyalty and future sales. The evolution of return policies now trends toward a generous ecommerce returns policy, which is key to attracting potential customers and maintaining a competitive edge, especially during the holiday season when ecommerce sales and customer returns surge.

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Returns and Customer Satisfaction Are a Balance-Sheet Liability

For retailers (and Shopify brands), returns are a balance-sheet liability, not merely a customer-experience feature. Every return ties up inventory and triggers costs: outbound shipping credits to the customer, inbound transportation for the return, labor to sort and inspect the item, and restocking or writing it off. Among these, transportation is a “biggest expense”. As one logistics executive observes, retailers are seeing “about 20 to 25% more” of them charge for returns this year – explicitly as a way to recoup these mounting costs.

A key step in the ecommerce returns process is the return merchandise authorization (RMA), which allows retailers to manage and track returns efficiently. The RMA process often includes generating a return shipping label and ensures that each return is properly documented, helping streamline operations and improve customer satisfaction. The return process typically begins when the customer initiates return through an online portal or help form, making a seamless experience essential for customer retention. Implementing self-service online returns portals can reduce customer service workload and increase processing speed, while returns management software and returns platforms automate the process, including label generation, approval workflows, and inventory updates. Automation and data analytics further help solve operational challenges, flag return abuse, and provide flexible options for loyal and honest customers, ensuring that fraud prevention measures do not unfairly penalize good shoppers.

Returns also carry hidden capital costs. While cash may be refunded to the customer immediately, the item often requires new handling. Many returned products are not in pristine condition: they need relabeling, repackaging or even discounting. When managing refunds, offering alternatives like store credit instead of cash refunds can help prevent fraud and retain value. Industry analysis finds that processing returns adds both labor and operational expenses. Retailers are adapting by dedicating more resources to returns: NRF data show 49% of retailers plan to rely more on third-party logistics providers for returns, and 43% plan to hire extra seasonal staff to handle the volume. All of this indicates that returns impact inventory, headcount, and cash flow – hallmarks of a balance-sheet liability.

Key cost factors include:

  • Reverse logistics costs: Inbound shipping, return shipping labels, and handling fees (often 20–30% of an item’s price).
  • Labor and facilities: Sorting, inspection and repackaging by warehouse teams, plus administrative handling.
  • Inventory recovery loss: A portion of returned goods can’t be resold at full price, necessitating markdowns or liquidation.
  • Fraud prevention and overhead: Although not shopper-blame, retailers note return fraud adds to the cost base (roughly 9% of returns) and must be countered with systems or policies that balance fraud prevention with not penalizing honest customers. Data analytics can help identify serial returners while providing flexible options for loyal customers.

As a result, retailers are explicitly factoring online returns into margins. For example, one study reports that 40% of merchants cite operational costs of processing returns as a reason to start charging fees, and another 40% cite carrier shipping costs. In other words, return fees directly offset the very expenses incurred on the balance sheet.

Accurate product descriptions, high-quality images, AR/VR tools, and authentic customer reviews with real-life photos and videos are crucial in reducing returns, especially since fit-related issues account for approximately 67% of fashion returns. Leveraging these strategies, along with collecting and analyzing feedback on return reasons, helps retailers identify trends, improve product offerings, and enhance trust among potential buyers. Providing tracking information for return shipments and a hassle-free return process—something 58% of customers want—can significantly improve satisfaction and loyalty. Notably, up to 23% of customers who receive instant refunds will shop again immediately, and many expect their credit processed within five days. Clear and accessible return policies further enhance trust, satisfaction, and repeat business.

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The Role of Reverse Logistics

Reverse logistics is the backbone of ecommerce returns management, encompassing every step required to move products from the customer back to the online retailer. When a customer initiates a return, the reverse logistics process begins: items are received, inspected for quality, and processed for either restocking, refurbishment, or disposal. Depending on the retailer’s return policy, customers may receive a cash refund, exchange, or store credit—each requiring precise coordination behind the scenes.

For online retailers, a streamlined reverse logistics process is essential to meet customer expectations for a hassle free return policy. Shoppers today expect a smooth, transparent return process, whether they are seeking a replacement, store credit for future purchases, or a prompt refund. Efficient reverse logistics not only helps manage costs by minimizing unnecessary shipping and labor expenses, but also plays a direct role in customer satisfaction and loyalty. When returns are handled quickly and fairly, customers are more likely to become repeat customers and leave positive online reviews, boosting the reputation of the ecommerce store.

Moreover, effective reverse logistics allows ecommerce businesses to recover value from returned merchandise, whether by restocking items for future inventory or offering them at a discount. This capability is especially important during peak periods like the holiday shopping season, when return volume surges and customer expectations are at their highest. By investing in robust ecommerce returns management and leveraging technology such as returns software and online portals, online merchants can save time, reduce hidden fees, and ensure that the returns management process supports both operational efficiency and customer retention.

In today’s competitive online shopping landscape, reverse logistics is no longer just an operational necessity—it’s a strategic differentiator that helps online retailers manage returns, control costs, and deliver the level of service that customers expect.

Major Retailers’ Return Policy and Fee Policies

Today’s return fee policies are often spelled out on retailer websites. Having a clear and accessible ecommerce return policy is crucial, as half of online shoppers review a retailer’s returns policy before buying. Recent policy language confirms the new charges:

  • Macy’s: Store returns remain free, and members of its Star Rewards program get free return shipping. All other customers have a $9.99 return shipping fee (tax added) deducted from their refund. In short, only loyalty members or in-store returns are truly free – mail-in returns for other shoppers incur the fee. Macy’s ecommerce return policy also clearly defines the return window for eligible items.
  • JCPenney: Its online return portal clearly states a flat $8 UPS fee for any mail-in return, streamlining the process by allowing customers to generate return labels and manage returns easily. (In-store returns remain free.) The return window and any applicable restocking fees are outlined in their ecommerce return policy, helping set clear expectations for customers.
  • J.Crew: The official returns FAQ notes that using the prepaid return label costs $7.50 for any number of items, which is deducted from the refund. Exchanges, however, are offered at no charge. Their policy details the return window and any restocking fees for certain items.
  • T.J. Maxx/Marshalls: Both retailers’ sites say that any return by mail incurs an $11.99 shipping-and-handling fee (per package). Again, in-store returns for online orders remain free of charge. Their ecommerce return policies specify the return window and clarify when restocking fees may apply.

In each case, the flat fee mirrors the amounts reported in the press. For example, ABC News notes “Marshall’s and T.J. Maxx charge $11.99 per package… Macy’s charges $10” (the $9.99 is often rounded to $10 in coverage). These updated policy details illustrate how return fees have moved from rumor to reality. (Retailers emphasize that avoiding the fee is possible by returning in-store, but that still means accepting returns as a cost of operations. Clear policies on when and how merchants accept returns, including any restocking fees, are essential for compliance and customer trust.)

Industry Outlook: Retail and logistics surveys indicate this trend will continue. In a recent NRF report summary, 64% of merchants said updating their returns process is a priority. Retailers are striking a new balance: maintaining customer goodwill while protecting their margins. For mid-market brands, the lesson is clear: treat returns as a cost center, not a free bonus. Expect return fees, tighter deadlines or other policies such as clearly defined return windows and restocking fees to roll out as standard practice, and plan your operations accordingly. Having a return policy that is easy to find and understand can reduce customer frustration and increase sales. Clear return policies that are easy to find and understand improve customer experiences, build trust, and encourage repeat business.

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Frequently Asked Questions

Why are returns costly for retailers?

Returns touch almost every part of the retail operation. Every return generates a host of expenses before a refund is even issued. Key cost components of handling a return include:

  • Return shipping: Even if retailers use a prepaid label, they ultimately pay the carrier. This often runs on the order of $7–$15 per package, depending on weight and distance.
  • Processing labor: Warehouse teams must unpack, verify, and inspect returned items. Typical labor costs add roughly $5–$9 per return (about 12–18 minutes of work).
  • Restocking and materials: If original packaging is damaged, retailers spend on replacement materials, labels, and packing (often $1–$3 extra). Even time spent relabeling or condition-checking adds to cost.
  • Inventory impact: While an item is being returned (often 7–14 days in transit and processing), it sits off the market and cannot generate new sales. This delay can mean lost revenue—imagine a dress returned at Christmas, which might have sold again if it were immediately available. One analysis quantified this “down time” cost as tens of thousands of dollars in foregone margin for a mid-size retailer.
  • Markdowns and write-offs: Not all returns can be resold at full price. Studies show 15–25% of returned goods require discounting or disposal. At a 40% markdown, a $45-margin item loses $18 margin; at worst it loses the full $45 if unsaleable. Over a year, markdowns can add hundreds of thousands in hidden losses for a mid-sized retailer.
  • Refund fees: The financial transaction itself has a cost. Returns incur payment processing fees (around $2–$3) since the retailer is refunding money and still paying credit-card networks.
  • Customer service: Each return can spawn multiple service interactions. Industry benchmarks suggest 2–3 inquiries per return (authorization, status check, refund query), with up to 10–12 minutes of support time each. This represents a non-trivial operational expense.

When tallied together, these costs convert returns from a small blip into a significant drag on profits. Bizowie’s breakdown demonstrates how the “gross margin” on a sale can evaporate once reverse logistics are factored in. Retailers might earn a 45% margin on a fashion item, only to see it cut by 55–65% after return handling, markdowns, and fees. In balance-sheet terms, returns directly shrink net revenue and increase selling expenses.

What sources were leveraged for return policy and cost data?

The information above is drawn from official retailer sites and industry reports. For example, Macy’s, JCPenney and J.Crew customer-service pages explicitly show their return shipping fees. T.J. Maxx and Marshalls policy pages list the $11.99 return fee. News coverage and industry surveys provide context and stats: Good Morning America (via ABC15) reports retailer fees for Macy’s, JCPenney, J.Crew and others;  and CBS News cite NRF/Happy Returns data (72% of retailers charging fees, cost breakdowns); and a Supply & Demand Chain Executive summary of the 2025 NRF returns report provides detailed percentages on return costs and policies.

  • https://www.abc15.com/news/smart-shopper/what-to-know-about-new-return-fees-timelines-from-retailers-for-holiday-gifts#:~:text=This%20holiday%20gift,fee%2C%20which%20is%20money%20custom
  • https://www.cbsnews.com/philadelphia/news/holiday-shopping-extended-return-policy/#:~:text=Return%20and%20restocking%20fees
  • https://www./news/saving-smarter/holiday-shoppers-face-growing-return-fees-as-retailers-cut-back-on-free-policies#:~:text=A%20new%20National%20Retail%20Federation,items%20back%20in%20many%20cases
  • https://online-shopping-free-return-policies/#:~:text=According%20to%20a%20report%20released,last%20year
  • https://www.modernretail.co/operations/the-case-for-and-against-return-fees/#:~:text=One%20of%20the%20biggest%20reasons,19%20pandemic
  • https://erp-for-high-return-ecommerce-managing-reverse-logistics-without-margin-erosion#:~:text=The%20economics%20are%20sobering,attributable%20to%20reverse%20logistics%20costs

Written By:

Manish Chowdhary

Manish Chowdhary

Manish Chowdhary is the founder and CEO of Cahoot, the most comprehensive post-purchase suite for ecommerce brands. A serial entrepreneur and industry thought leader, Manish has decades of experience building technologies that simplify ecommerce logistics—from order fulfillment to returns. His insights help brands stay ahead of market shifts and operational challenges.

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What Is Reverse Logistics? How Ecommerce Returns Actually Flow Back

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Introduction: Online shoppers return a significant portion of what they buy, making efficient returns handling a critical part of ecommerce operations. In fact, U.S. consumers sent back about 14.5% of all purchases in 2022 – representing $743 billion in lost sales for retailers. This is where reverse logistics comes in. Reverse logistics manages how those returned products flow from the customer back to the seller or manufacturer. Rather than goods moving outbound to shoppers, reverse logistics handles the opposite direction – bringing items back through the supply chain after a return, repair, or recycling need arises.

What Is Reverse Logistics in Ecommerce?

Reverse logistics refers to the end-to-end process for handling products that come back into the supply chain after a sale. In ecommerce, this typically means managing everything that happens after a customer initiates a return or exchange. The process starts with the consumer and works backward, making efficient management of returned merchandise crucial for optimizing costs and customer satisfaction.

An effective reverse logistics system aims to regain as much value as possible from returns—through restocking, refurbishing, recycling, or responsible disposal. By investing in good reverse logistics practices, ecommerce companies can reduce waste, maintain customer trust, and recover revenue.

Forward vs. Reverse Logistics: What’s the Difference?

Forward logistics covers the movement of products from suppliers to customers. Reverse logistics moves products in the opposite direction—from customers back to retailers. Reverse logistics is more unpredictable, more labor-intensive, and often 2–3 times more expensive per parcel than outbound shipping.

Common Steps in Reverse Logistics and Returns Management Process

  • Initiation & Shipping: Customer initiates a return and ships the item back.
  • Receiving & Inspection: Items are checked, verified, and graded.
  • Processing & Restocking: Resalable items return to inventory.
  • Repair or Refurbishment: Fixing items with recoverable value.
  • Resale or Secondary Markets: Liquidation, clearance, or recommerce.
  • Disposal & Recycling: Responsible handling of unsellable goods.

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Key Cost Drivers in Reverse Logistics

  • Labor-intensive processing
  • Reverse shipping costs
  • Inventory value loss over time

According to the National Retail Federation, returns cost retailers an average of $0.21 per $1 of returned sales. Many retailers offset this by charging restocking fees.

Technology and Reverse Logistics

Technology enables automation across the reverse logistics process, from return portals to inventory updates and analytics. Tools such as returns platforms and warehouse management software help reduce costs and improve customer satisfaction.

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Improving Reverse Logistics: Operational Takeaways

  • Design for fewer returns
  • Streamline workflows with automation
  • Optimize disposition decisions
  • Leverage secondary markets and partners
  • Monitor metrics and customer feedback

Well-managed reverse logistics can transform returns from a cost center into a driver of customer loyalty and sustainability.

Frequently Asked Questions

What is reverse logistics?

Reverse logistics is the process of moving goods from customers back to sellers after purchase, including returns, repairs, recycling, or disposal.

Why is reverse logistics important?

It helps recover value from returns, reduce waste, control costs, and maintain customer satisfaction.

Written By:

Indy Pereira

Indy Pereira

Indy Pereira helps ecommerce brands optimize their shipping and fulfillment with Cahoot’s technology. With a background in both sales and people operations, she bridges customer needs with strategic solutions that drive growth. Indy works closely with merchants every day and brings real-world insight into what makes logistics efficient and scalable.

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Why Holiday Returns Are Hitting Earlier Than Expected – and What That Means for Ecommerce Operations

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Introduction

Holiday returns still peak after Christmas, but a growing share of holiday-season returns is now occurring during the peak season itself. In other words, more shoppers are sending back items before Christmas Day than in years past. Many retailers have introduced an extended return window for holiday purchases, but are also increasingly implementing return fees. This shift in timing means ecommerce operations must handle significant return volumes in December, concurrently with fulfilling record outbound orders, straining fulfillment networks, carrier capacity, and reverse logistics processes.

According to Seel’s 2025 Returns and Refunds Report, return activity during November and December is 16% higher compared to other months. In short, consumers are buying earlier, returning sooner, and expecting faster refunds. The operational impact is compounded by the need to process returns under new fee structures and longer return windows. While the traditional post-holiday return rush (in the week after Christmas and early January) remains massive, a notable portion of returns is now hitting ahead of Christmas. This article explores what’s driving holiday returns to hit earlier, why it’s not just about gifts, and what it all means for ecommerce operations’ cash flow, inventory, and fulfillment systems.

The Early Returns Trend: A New Holiday Season Pattern

Not long ago, “holiday returns” essentially meant post-Christmas returns. Industry data shows nearly 18% of all holiday purchases are typically returned between December 26 and January 31. Retailers even coined events like National Returns Day in early January to brace for the flood of unwanted gifts. That peak still exists – but now another returns wave is swelling before Christmas.

Logistics insiders first saw this pattern emerging several years ago. UPS surprised many by predicting holiday returns would peak before Christmas in 2018 – and it did. On December 19, 2018 (a week before Christmas), UPS handled a record 1.6 million return packages that day. This was higher than the returns on the traditional early-January peak in prior years. UPS and others observed a two-peak returns season: one spike just before Christmas, then the usual surge right after New Year’s.

Most retailers now offer extended return windows for items purchased as early as October, with most major retailers extending their return windows to late January 2026 for the 2025 holiday season. Amazon allows most holiday purchases made between Nov. 1 and Dec. 31 to be returned until Jan. 31, 2026.

What caused that early return spike? A combination of shoppers buying earlier and returning earlier. In 2018, retail analysts noted that consumers had started shopping for holiday deals sooner – over 55% of shoppers had begun buying by early November – and consequently, some returns were initiated well before December 25. In effect, “buy earlier, return earlier” became a new behavior pattern. For the 2025 season, items purchased in October are often included in these extended holiday return policies offered by most retailers. Fast-forward to 2025, and that pattern has only grown. As one report summarizes, “shoppers are buying earlier, returning sooner and expecting faster refunds”. Holiday returns still spike after Christmas, but now much more return activity is pulled into December than anyone expected a decade ago.

Gift Returns vs. Behavior-Driven Returns

How can returns increase before Christmas without contradicting the obvious fact that people haven’t received their gifts yet? The key is understanding that not all holiday-season returns are gift returns. In fact, the early returns surge is largely driven by behavior-driven returns (from shoppers themselves) rather than recipients returning unwanted gifts.

Gift returns are essentially calendar-locked: Most gifts aren’t even unwrapped until Christmas, so any return or exchange by the recipient will naturally happen after December 25. Retailers accommodate this with extended holiday return windows – for example, Amazon, Walmart and others allow most items bought in October–December to be returned until late January. This means a sweater bought for Dad on Black Friday can still be returned after the holidays, so there’s no reason (and usually no ability) for the recipient to return it before Christmas. Historically, about 45% of gift returns happen in the week between Dec 26 and New Year’s, and roughly 50% more occur in January. Gift returns still follow that cadence, tied to the holiday calendar.

Many major retailers have specific extended holiday return windows for 2025-2026: Walmart extends its return policy for items purchased between Oct. 1 and Dec. 31 to Jan. 31, 2026; Target extends its return window for most Target purchases made between Nov. 1 and Dec. 24 to Jan. 24, 2026; Best Buy allows returns for items purchased between Oct. 31 and Dec. 31 until Jan. 15, 2026; Macy’s extends its return policy for items purchased between Oct. 6 and Dec. 31 to Jan. 31, 2026; Kohl’s extends its return policy for purchases made between Oct. 5 and Dec. 31 to Jan. 31, 2026; Sephora extends its return policy for purchases made between Oct. 31 and Dec. 30 to Jan. 30, 2026; and Ulta Beauty allows returns for purchases made between Nov. 1 and Dec. 31 until Jan. 31, 2026. Note that Target Plus items may have different return windows than most Target purchases, and some categories—such as Apple products and Beats products—may be excluded from these extensions or have shorter return periods. Retailers often specify exclusions for certain items, such as “excluding Apple,” and Beats products may have their own unique return policies.

Behavior-driven returns, on the other hand, follow a different logic. These are returns generated by the purchaser’s own decisions and shopping behavior during the season, not by gift recipients. Several modern trends have supercharged these returns during the holidays:

  • Self-Gifting and Early Deals – Holiday sales now start early (think October Prime Days, Black Friday in early November), and shoppers often buy items for themselves alongside gifts. By mid-season, they may decide to return items they bought for personal use – for example, returning a splurge purchase or an upgraded gadget they second-guessed. The first wave of holiday returns “is thought to be more from people shopping for themselves… ‘It’s not just about shopping for gifts,’” noted one returns executive. Shoppers jump on early discounts and, if they experience buyer’s remorse or find a better deal later, they send those items back before Christmas.
  • Bracketing and Try-Before-You-Buy – It’s increasingly common to order multiple variants (sizes, colors) of a product with the intention to keep one and return the rest. Shoppers treat generous return policies as a chance to “try before you buy.” For example, a customer might order three party dresses in early December, keep the one that fits best, and return the other two immediately. Nearly one-third of shoppers now return at least one item a year, and many see returns as a normal part of shopping. This behavior isn’t tied to gift-giving at all – it’s driven by the buyer’s own preference to sample and send back. As UPS’s Happy Returns noted, when people shop for themselves they often buy multiple sizes or options knowing they’ll return the extras, whereas for gifts they typically pick one item and wait (the gift “won’t be opened until Christmas and returned later”). The result? More returns mid-December from “change of mind” purchases and bracketing.
  • Higher Expectations (Instant Refunds & Convenience) – Shoppers today expect frictionless and fast returns. Many retailers and fintech services now offer immediate refunds (or refunds upon package drop-off) and encourage quick turnaround. Knowing they can get their money back fast, consumers are quicker to initiate returns rather than holding onto an item. Seel’s research emphasizes that “fast and fair refunds” are now considered part of the product experience, and slow refund processes will push shoppers away. This has created a mentality of “buy with confidence – you can always return it”, which naturally boosts return volume during the season. Customers don’t feel the need to wait; if an item isn’t right, back it goes, even if it’s mid-December, because they trust they’ll get their refund promptly.
  • Earlier Delivery Cutoffs & Missed Gifts – Many consumers try to avoid the last-minute shipping crunch (and the risk of gifts arriving late). Ironically, this can generate returns in December: if a shopper ordered a gift early but then discovers by mid-December that it’s not suitable, they might preemptively return or exchange it before Christmas. For instance, ordering a toy in November but then returning it in December upon realizing the child already has it, in order to buy a different gift. In the past, they might have waited to handle it post-holiday, but today’s free-and-easy return policies encourage resolving it now. Additionally, if a backup gift is purchased because of shipping uncertainty, the redundant item might be returned before year-end once the primary gift arrives on time.

In summary, gift returns haven’t moved up – those still largely happen after the holidays (thanks to extended return periods and the nature of gift giving). It’s the non-gift returns that have “shifted left.” Shoppers’ proactive behaviors, personal purchases, and flexible buying tactics are generating early returns well before Santa’s sleigh departs. This explains how overall return volumes can climb in early/mid-December without defying the timing of gift exchanges. Retailers are essentially dealing with two waves of returns: one driven by consumer behavior (before Christmas) and one driven by gifts (after Christmas).

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Why Returns Are Increasing Before Christmas

Several industry analyses point to root causes behind the rise in December return activity. It’s not just one factor – it’s a confluence of changes in consumer behavior and retail practices:

  • Longer Holiday Shopping Season – The holiday shopping season has stretched out. With major sales starting as early as October, consumers are buying (and thus potentially returning) over a longer period. They are no longer concentrating all purchases in late December. A longer timeline naturally spreads out return events too. (Notably, in years when the calendar made the shopping period shorter, returns peaked more after Christmas; in longer seasons, some returns hit earlier.)
  • “Buy Now, Decide Later” Mindset – Economic jitters and abundant options have made shoppers more indecisive during peak season. Seel’s 2025 report notes that recession fears, job uncertainty, and buyer’s remorse are influencing shoppers to think extra hard about purchases. Many will buy an item just in case (perhaps to lock in a deal or ensure they have something in time), then later return it if they change their mind. The rise of free returns has essentially turned many holiday purchases into conditional trials. This wasn’t the case years ago when more sales were final.
  • Heightened Post-Purchase Standards – With retailers competing on customer experience, return policies have become very lenient (free return shipping, extended windows, no-questions-asked returns). Shoppers know this and hold retailers to high standards. If any issue arises – a product doesn’t meet expectations or a better alternative appears – they won’t hesitate to send an item back immediately. The vast majority of shoppers now say they wouldn’t even purchase an item if it lacked a return option. In short, easy returns are part of the deal, and consumers use them liberally during holiday season.
  • Delivery Issues and Weather Glitches – One big driver of early returns is delivery problems during peak season. When an item is delayed in transit or a package goes missing in mid-December, customers often react by reordering a replacement or buying an alternative locally – and then returning/canceling the late shipment when it eventually arrives (or filing for a refund because it never arrived on time). Seel’s data shows that delivery failures (late or undelivered packages) have become the dominant driver of return requests, accounting for about three-quarters of all return reasons on its platform. During the holidays, shipping carriers are stretched thin and weather events can wreak havoc on timetables. For example, the 2017 Christmas season saw major snowstorms that delayed deliveries, which in turn prompted many returns and taught consumers a lesson about not waiting until the last minute. In 2023 and 2024, some regions experienced blizzards and severe weather in mid-December; when gifts didn’t arrive by Christmas, customers often returned or refunded those orders. However, weather alone is not the primary cause of the broad shift toward earlier returns – it’s more of an amplifier. A storm in one year might spike delivery-related returns regionally, but the overall trend of returns creeping into December is happening even in normal years. (Still, it’s worth noting: in categories like fashion, “delivered too late” returns jumped 124% year-over-year, and missing-package claims rose 42%, indicating how late deliveries can translate to return volume. Bad weather in peak season just pours fuel on that fire.)
  • Consumer Awareness and Habits – Shoppers have become savvy about returns. Many people now plan for returns as part of holiday shopping. Surveys show consumers consciously factor in return options before purchasing, and many will initiate a return as soon as they decide an item isn’t working out, rather than procrastinating. There’s also a trend of immediate exchanges – for instance, buying two competing products (such as two different electronics) intending to return one once they compare. In years past, a customer might have waited until after the holidays to do this comparison and return; now it often happens in real time during December.

In essence, today’s holiday shopper is more flexible and less patient. If something’s not right, they’ll return it right away – peak season or not. As Laura Huddle of Seel puts it, retailers are facing shoppers who “be more thoughtful and take extra time thinking through purchases” and leverage trends like try-before-you-buy, which means more mid-season returns. All these factors have shifted some of the returns burden into the heart of the holiday period.

Operational Impact: Returns Strain During Peak Season

For operations and logistics teams, earlier holiday returns are a double whammy. Peak season (November through late December) is already the most intense time for fulfilling orders and managing inventory. Now, with returns hitting earlier, reverse logistics tasks overlap with the busiest outbound shipping weeks. To offset these operational costs, many retailers are adding fees or return fees, making it more expensive for customers to return items during the holiday season. This presents several challenges:

Shipping costs and certain fees are often non-refundable, meaning customers may not be reimbursed for these expenses when returning items. About 72% of retailers now charge for some returns, up from 66% last year. Many retailers charge return shipping fees for items returned by mail; for example, Macy’s charges a $9.99 return shipping fee unless the customer is a Star Rewards member. To avoid return shipping fees, customers should return items in-store whenever possible.

Fulfillment & Carrier Capacity Under Pressure

Warehouse and fulfillment centers that are calibrated to handle outbound order peaks in December are now seeing inbound return volumes at the same time. During a normal year, a retailer’s distribution center might shift focus to processing returns in early January, when order shipments slow down. But now those returns are arriving mid-December, when the facility is in full throttle picking, packing, and shipping mode for Christmas. The result is operational strain:

  • Overwhelmed Facilities – Processing returns (inspecting items, repackaging, updating inventory systems, etc.) requires labor and space. In December, both are at a premium. Many retailers simply lack the capacity to triage returns immediately during peak – leading to backlogs of unopened return packages piling up in corners of the warehouse. The influx can overwhelm return processing stations that were staffed for normal volumes. In 2025’s holiday season, many retailers discovered that their return systems, which functioned fine most of the year, broke under the peak load. As one analysis noted, 2025’s record online sales created a “tsunami of returns that exposed weaknesses in fulfillment systems”. Under the stress of simultaneous outbound and inbound surges, normal quality controls start to slip. There are reports of warehouse staff so busy rushing to meet ship deadlines that they make mistakes – sending wrong items, mislabeling packages – which in turn generate even more returns to process. It’s a vicious cycle: returns volume creates strain, strain causes errors, errors create more returns.
  • Carrier Networks Handling Returns – Shipping carriers (UPS, FedEx, USPS, etc.) also feel the impact. Their trucks and hubs in December are geared toward delivering gifts to customers; handling return pickups and shipments at the same time adds load. UPS observed that during the 2018 holiday push, returns doubled alongside outbound volume. In 2025, we’re seeing similar patterns. Carriers must allocate space for millions of return packages even as they race to get new orders delivered by Christmas. Most mailed returns may incur fees, as some retailers charge for mail-in returns, but many offer free in-store or designated drop-off options to help customers avoid these costs. This can lead to delays in return shipments (return packages moving slower through the network), which in turn slows down the refund process and can frustrate customers expecting fast refunds. It’s a delicate balance for carriers trying to optimize routes for both directions. In short, the reverse logistics pipeline goes into overdrive just when the forward logistics pipeline is at peak capacity.
  • In-Store Returns Lines – For retailers with physical stores, an earlier return trend means more people showing up to return items before Christmas. Customer service counters in December traditionally handle only sales and gift-wrapping, but now they may see customers bringing back online purchases or unwanted items in the middle of the holiday rush. This requires extra staffing and coordination at stores, which again is challenging when those same stores are crowded with shoppers. Some major retailers encourage bringing online returns to stores (to drive foot traffic or expedite processing), but in December that can backfire by adding to store staff workload during the critical sales weeks.

Cash Flow and Financial Timing

A less obvious but critical impact of returns shifting earlier is on cash flow and revenue recognition for retailers. In a typical cycle, many holiday purchases would be returned in January, effectively hitting the books in the next fiscal period (for many, Q1 of the new year). Now, with more returns in December, retailers are having to issue refunds before the year’s end, which can squeeze cash flow at a precarious moment:

  • Refund Outflows in Q4 – The holiday season brings a huge inflow of revenue in November and December. But if 10-15% (or more) of those sales are already boomeranging back as returns within December, that means a chunk of revenue is being reversed before year-end. Retailers must refund customers’ money (or credit their accounts) right when they are also spending heavily on marketing, shipping, seasonal labor, etc. For smaller ecommerce sellers, this can be a real liquidity crunch. They’ve paid to acquire the customer and ship the order, and now the sale falls through earlier than expected. One guide for Amazon sellers warns that the extended holiday return period can create “cash flow issues”, because Q4 sales aren’t truly settled until late January. When returns happen earlier, the uncertainty hits in Q4 itself. Retailers have less net cash from holiday sales on hand in December, which can disrupt buying budgets and year-end financial metrics.
  • Inventory and Revenue Uncertainty – Earlier returns also mean retailers have to account for potential markdowns and lost sales within the holiday quarter. For instance, an item returned on December 20 might be put back in stock (if processed quickly), but if it’s not resold by Christmas, it likely goes into clearance. The revenue loss or reduction from that return will hit Q4’s results, not Q1. This can make holiday quarter earnings less predictable. Retail finance teams now need to forecast return rates during the peak season and adjust revenue expectations accordingly. Essentially, the tidy separation where Q4 was “sales boost” and Q1 was “returns hit” is blurring. As a result, profit margins for Q4 can erode more than before due to earlier refunds and restocking costs.
  • Operational Expenses – Processing returns costs money: labor, inspection, repackaging, sometimes return shipping fees or disposal fees. If these costs ramp up in December, they add to an already expensive season (overtime wages, expedited shipping fees, etc.). Retailers might find their holiday operational costs increasing because they’re now doing both outbound fulfillment and reverse logistics concurrently. This dual burden can shrink the profitability of holiday sales. Many retailers count on the holiday spike to be their “black ink” period of the year – but heavier returns in-season chip away at that.

From a cash management perspective, businesses need to plan for higher reserve funds to cover refunds during December. Marketplaces like Amazon often hold a larger reserve from seller payouts in Q4 specifically to cover potential returns. Similarly, brands need enough liquidity to weather returns coming in early. If unprepared, a merchant could face a cash crunch fulfilling new orders while refunding others simultaneously.

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Inventory Recovery and Availability

One potential silver lining of earlier returns is the chance to get merchandise back in stock for resale during the holiday rush – but in practice this benefit is hard to capture with brittle systems. Ideally, if a customer returns a popular item on Dec 15, the retailer could quickly process it and have it available to sell to someone else by Dec 20 (still in time for last-minute shoppers). This would be great for recovering revenue and avoiding stockouts. However, the reality is that many retailers’ reverse logistics can’t move that fast in December. The result is a lot of valuable inventory tied up in return bins until January, which is effectively lost sales.

Key issues with inventory and earlier returns include:

  • Processing Speed – During off-peak times, a returned product might be inspected and restocked in a few days. During peak, that process can stretch to weeks. Each extra day a return sits unprocessed is margin lost on that item. For seasonal items or gifts, the time value is even higher – a toy or sweater returned on Dec 20 and not back on the shelf by Dec 24 is essentially missed opportunity. As one expert put it, “A seasonal item sitting unprocessed becomes unsellable” if it misses the window. Retailers with brittle systems might find themselves liquidating those items later at a huge discount, whereas if they had processed the return faster, they could have sold it at full price pre-Christmas.
  • Inventory Accuracy – The chaos of concurrent returns can mess with inventory tracking. Items in return limbo might not be counted correctly in stock systems. Some retailers have reported inventory “black holes” where returned units are in transit or sitting in a corner and therefore not available for sale on the website, even though there is demand. This inaccuracy hurts order fulfillment – e.g. overselling an item because the system didn’t account for a bunch of pending returns marked as back in stock, or conversely not selling an item because the returned stock wasn’t recorded. The lack of real-time visibility into returns in motion (coming back from customers or between stores and warehouses) leads to either lost sales or customer service headaches.
  • Storage Space – Returns take up physical space. During peak, warehouses are already bursting with outbound stock and incoming new inventory. A surge of returns can clog hallways and receiving docks, effectively reducing capacity for regular operations. If returned items aren’t immediately processed, they start occupying shelf space that could be used for fulfilling current orders. Some warehouses resort to renting additional storage or trailers to hold returns until January, adding cost and complexity.
  • Refurbishment and Repackaging – Many returned products require some prep before they can be resold (e.g., checking for damage, repackaging neatly, resetting electronics). Doing this work in December requires diverting skilled staff or setting up separate lines. If retailers lack bandwidth to do it, those returned items won’t make it back to stock in time. This particularly affects electronics and high-value items which often need testing before resale. The net effect is fewer available units to sell during the final sales surge. In contrast, those who can rapidly triage returns might win extra sales. For example, a returned tablet processed on Dec 22 can be sold on Dec 23 to a last-minute shopper. But without an efficient system, that tablet might sit until January and then be sold as open-box at a loss.

In summary, earlier returns expose how inflexible many inventory and returns systems are. Legacy post-holiday returns processes weren’t built to “turn on” until after Christmas, so they buckle under the ask of quick recovery in-season. Some leading retailers are investing in automations and forward-deployed return centers to improve this, but industry-wide it’s a challenge.

Customer Experience and Service Load

From the customer perspective, the ability to return early is a positive – but only if the retailer can handle it smoothly. During December, customer service teams are fielding inquiries about orders, and now also about returns (status of return, refund not yet issued, etc.). This adds to the support load at a critical time. Retailers have to ensure their return portals, RMA systems, and support scripts are up to the task:

  • System Uptime and Errors – With more customers initiating return requests in December, return management systems face peak traffic too. Any outages or glitches (e.g. a returns portal crashing) will frustrate shoppers at the worst time. IT teams need to monitor these systems just as closely as the e-commerce checkout systems during peak. Some metrics like return portal uptime and median time to issue refund become important to track in December, not just January.
  • Customer Support Training – Support agents must be prepared to handle questions like “Where’s my refund?” or “How do I return this gift I bought early?” even as they handle sales-related questions. Retailers who assumed those questions would mainly come in January might be caught understaffed or unprepared in December. This can lead to longer wait times and lower service quality, right when customer satisfaction is paramount (nobody wants an angry customer two days before Christmas because their return label email didn’t arrive).
  • Fraud and Policy Enforcement – Longer return windows and concurrent returns also open the door for return fraud during the holidays. With so much volume, it’s easier for fraudulent returns to slip in (e.g. wardrobing, returning used items, etc.). Retailers have to be vigilant even while overwhelmed. Some have implemented stricter checks or restocking fees on certain categories (for example, electronics or luxury items) to deter abuse. But enforcing those policies consistently in the holiday rush is tough – it can create conflict with customers in-store or confusion online. A delicate balance must be struck to prevent fraudulent or excessive returns without souring the experience for honest customers.

Return Policies and Fees: Shaping the Early Returns Landscape

The holiday season is a pivotal time for both shoppers and major retailers, and return policies play a central role in shaping the early returns landscape. As the National Retail Federation reports, the volume of returned items surges during the holidays, prompting many retailers to adapt their return policies to meet customer expectations and operational realities.

One of the most significant trends is the widespread adoption of extended return windows. Many major retailers now allow items purchased as early as October to be returned well into January, giving customers extra flexibility during the holiday shopping rush. This extended return period is especially important for gift-givers, but it also encourages early returns from those making in-store purchases or online buys for themselves. However, the details can vary widely—especially when it comes to electronics and entertainment items.

Electronics and entertainment items often come with stricter return policies, including restocking fees or specific requirements for original packaging. For example, Best Buy may charge restocking fees on certain electronics, and mailed returns can incur additional return shipping fees. In contrast, in-store returns are typically more straightforward and cost-effective. Many major retailers, including Target, offer free in-store returns for most items, making it easier for customers to avoid extra costs. For online purchases, some retailers provide free return shipping, while others may deduct return shipping fees from the refund, especially for mailed returns or marketplace items.

Understanding the fine print is crucial. Return windows, restocking fees, and return shipping fees can all impact the total cost of a return. Some retailers require items to be unopened and in their original packaging to qualify for immediate refunds, while others may only offer store credit or an even exchange for certain categories. To prevent fraud and abuse, many retailers now require receipts and original packaging for returns, particularly for high-value electronics and entertainment items.

Loyalty programs can also make a difference. Many stores offer loyalty members perks such as extended return windows or waived return shipping fees, providing added value during the holiday season. For example, Target’s loyalty program may offer free return shipping for online purchases, while other retailers might extend the return window for frequent shoppers.

Ultimately, return policies and fees are a key part of the holiday shopping experience. By reviewing the fine print before making a purchase—especially for electronics and entertainment items—customers can avoid unexpected costs and ensure a smooth return process. As many retailers continue to refine their return policies to balance customer satisfaction with operational efficiency, being informed about return windows, fees, and in-store versus online return options is more important than ever. This proactive approach helps shoppers make confident purchases and enjoy a hassle-free holiday season, even if some items end up back at the store.

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The Core Problem: Brittle Post-Purchase Systems

All the above impacts point to a deeper, systemic issue: Many retailers’ post-purchase and returns systems are brittle and outdated, designed for a different era. They were built with the assumption that returns happen after the holiday frenzy, in a nice separate window when you can clean up the mess. That assumption no longer holds. Peak season now exposes the weaknesses in those systems in real time.

For example, a retailer might have a single returns processing center that was fine handling off-season returns, but come December, it becomes a bottleneck. Or an online merchant might discover their returns authorization software can’t handle the volume of concurrent requests, causing delays and errors. In 2025, many retailers have learned the hard way that their “post-holiday cleanup” approach is too rigid for today’s continuous cycle of sales and returns.

A post-mortem analysis of Holiday 2025 returns by one industry group noted that the season “revealed fundamental vulnerabilities” in fulfillment operations and “problems that normal operations hide”. In plain terms, systems that seemed OK most of the year broke under the pressure of simultaneous outbound and inbound surges. Some common failure points include:

  • Poor Integration – Returns data often isn’t integrated in real-time with inventory and order systems. During peak, these integration gaps become glaring. Items authorized for return might not be properly marked in inventory, leading to phantom stock counts. Or refund systems might not sync with the e-commerce platform, causing customers to get return confirmations late. The manual workarounds that teams use during slower periods don’t scale in December, leading to confusion and mistakes. Many retailers are now investing in better system integration after seeing these cracks.
  • Lack of Forecasting for Returns – Retailers have gotten better at forecasting holiday sales, but few rigorously forecast holiday returns. Peak season return rates can approach 25-30% in e-commerce, especially in categories like apparel. Without forecasting, warehouses were caught off-guard by how many returns showed up early. This meant insufficient labor allocated to returns and no space set aside. Going forward, operations teams are realizing they need to plan for returns spikes just as they plan for order spikes – including having contingency space, extra return merchandise authorizations (RMAs) capacity, and maybe even staggered return shipping incentives to smooth the flow.
  • Rigid Staffing and Processes – Many returns departments operate Monday-Friday, 9-5, even during peak, whereas fulfillment teams go 24/7. This misalignment caused returns to pile up untouched for days during the height of season. Some companies simply shut off returns processing in mid-December to let warehouses focus on outbound – effectively deferring the problem but at the cost of delays and customer frustration. Such rigid approaches aren’t sustainable as early returns become the norm. The systems need to be flexible – e.g., cross-training staff to pivot to returns processing when needed, or using automation for returns (like scan-and-sort systems) to handle volume quickly.

Ultimately, the deeper takeaway is that returns can no longer be treated as an afterthought or “January’s problem.” The holiday peak now has to be managed as a holistic cycle that includes both sales and returns concurrently. Retailers that failed to adjust have felt the pain in lost sales, higher costs, and customer dissatisfaction. Those that are adapting – by strengthening their post-purchase infrastructure – are better positioned to thrive even as returns rise.

Frequently Asked Questions (Preparing for Peak-Season Returns)

Why are holiday returns happening earlier than before?

A growing portion of holiday returns are occurring in December due to changes in consumer behavior and retail policies. Shoppers are buying earlier in the season (often starting in October/November) and thus returning items sooner if they change their mind. Many are purchasing for themselves during holiday sales and will return those personal buys before year-end. Trends like buying multiple items to try at home (“bracketing”) and expecting instant refunds encourage people to initiate returns immediately, rather than wait. Additionally, delivery delays or issues on pre-Christmas orders can trigger returns or refund requests in mid-December. All these factors mean that while gift returns still happen after Christmas, non-gift returns have “shifted left” into the peak season.

Do gift returns still mostly happen after Christmas?

Yes. Returns of gifts (items given during the holidays) overwhelmingly occur after Christmas, since recipients generally can’t return gifts until they’ve received and opened them. Retailers support this by offering extended return windows through January for holiday purchases. For example, an item bought in November as a gift might be returnable until Jan 31. Historically, about half of all gift returns occur in the week after Christmas and the other half in January. This pattern remains true – gift timing hasn’t changed. What’s changed is the volume of self-initiated returns during December (unrelated to gift receipt). So, gift returns still peak post-holiday, but overall return activity now has a “two-peak” shape, with a significant bump before Christmas as well.

How can returns increase before Christmas if people haven’t received their gifts yet?

The returns happening before Christmas are largely not gifts being returned by recipients – they are items returned by the original buyer. Many shoppers return items they bought for themselves or as potential gifts before the holiday. For instance, a person might buy two competing products as possible gifts and then return one in mid-December after deciding which to give. Or someone might order a gift early, then return it pre-Christmas upon realizing it wasn’t suitable (and get an alternative). Also, any “try and return” behavior (such as ordering clothes to try on) will lead to returns in December. The ability to return online makes it easier for shoppers to initiate returns before Christmas, but while many retailers offer free in-store returns, they may charge return shipping fees for online purchases. These returns don’t contradict gift-giving timelines; they are a result of earlier shopping habits and generous return policies that let buyers change course on purchases prior to Christmas.

What role does weather play in holiday returns coming early?

Severe winter weather can amplify early returns but isn’t the primary cause of the overall shift. For example, a blizzard or storm in mid-December might delay thousands of packages, prompting customers to cancel orders or request refunds before Christmas (since the items didn’t arrive in time). This will spike returns activity in that region for that season. In 2017, for instance, heavy snow led many to shop earlier the next year and returns peaked before Christmas. However, the broader trend of earlier returns is driven more by consumer behavior (earlier shopping, try-before-you-buy, etc.) than by one-off weather events. In short, weather can trigger more “package not delivered” returns in a given year, but the reason returns have generally moved into December is not just because of weather – it’s because of how people shop and return now. Retailers should plan for early returns every year, with weather-related surges as a possible extra factor.

How do earlier holiday returns affect ecommerce operations?

In a word: strain. When returns hit during the peak sales period, it creates additional workload for warehouses, shipping carriers, and customer service at the busiest time of year. Warehouses must process inbound returns (inspect, restock, etc.) even as they’re frantically shipping out new orders – this can overwhelm systems and staff, sometimes resulting in errors and delays. Carriers have to carry return packages in December on top of deliveries, squeezing capacity. Financially, issuing lots of refunds in December can pinch cash flow and erode holiday revenue margins sooner than expected. Inventory-wise, retailers have valuable products coming back early, but if they can’t process them quickly, they miss the chance to resell those items during peak demand.

Overall, operations become more complex: there are more moving pieces (literally, goods moving in two directions), and any weak link – whether in IT systems, forecasting, or staffing – gets exposed under the dual pressure of outbound and inbound logistics. Returns for certain product categories, such as mobile phones and other electronics, often have stricter conditions. For example, Best Buy charges a 15% restocking fee on opened electronics and a $45 fee on activatable devices. To avoid these restocking fees, items must generally be unworn, unwashed, and in their original box with tags intact. Many retailers learned that their returns processes were too brittle for this concurrent stress, leading to process breakdowns in some cases.

What can retailers do to handle the returns surge during peak season?

Preparation and agility are key. Retailers should forecast returns volume for December using historical data and plan capacity for it, just as they plan for order volumes. This might mean scheduling additional labor or shifts dedicated to returns processing in mid-December, instead of waiting until January. Improving reverse logistics automation can help – for instance, using barcode scans and software to quickly route returned items to where they need to go (back to stock, to refurbishment, etc.) without manual bottlenecks. Another strategy is to encourage or incentivize some returns to happen slightly later (if manageable) to spread out the load – e.g. some retailers might subtly ask gift recipients to “wait until after Dec 25” for returns in their return policy messaging (though most early returns are not gifts, so this has limited effect).

On the inventory side, having a system to rapidly triage returns is crucial: identify items that can be resold immediately and get them back online within days (especially hot sellers that might be sold out otherwise). For example, some advanced operations use separate “fast lane” processing for high-value returned items during December, so they’re back on virtual shelves in time for last-minute shoppers. Retailers also benefit from stronger integration between returns and inventory systems – so that as soon as a return is initiated, the system accounts for it (perhaps even allowing buy-online-return-in-store (BORIS) for faster turnaround).

Additionally, ensure customer service training and tools are in place for the returns surge: quick refund processing, clear communication of return status, and perhaps self-service return portals that can handle high traffic. Monitoring metrics like refund turnaround time and keeping them at acceptable levels even during peak will help maintain customer trust. Some retailers enlist third-party returns management services during holidays to offload some of the strain.

In short, retailers must treat returns as part of the peak game plan. Those who invest in resilient, scalable post-purchase systems – from software to staffing – will handle the earlier returns trend far more smoothly than those who try to bolt it on last-minute. The goal is to make returns efficient and even leverage them (e.g., getting inventory back for resale, using the return interaction to upsell or build loyalty) rather than simply viewing them as a January clean-up chore.

Is the shift to earlier returns here to stay?

All signs point to yes, the trend is likely permanent and growing. Consumer habits formed over recent years – like shopping early, expecting easy returns, and bracketing purchases – are now ingrained. E-commerce continues to grow, and online orders have higher return rates than in-store (often 2-3× higher), which means as holiday e-commerce expands, returns will increase in volume and come sooner (since online shoppers tend to return faster via mail or drop-off). Moreover, Gen Z and younger shoppers are very comfortable with the cycle of ordering and returning as part of finding the right product. Retailers are also further refining omnichannel returns (like buy online, return in store), which removes friction and can speed up returns. Unless retailers significantly tighten return policies during holidays (an unlikely move in a competitive market, and one that could hurt sales), consumers will continue to take advantage of leniency – which means return boxes on porches well before Santa arrives.

Going forward, we may see the “early returns peak” become an expected part of holiday logistics. For example, carriers might routinely plan a “Returns Day” in mid-December to rival the traditional post-Christmas returns rush. UPS did exactly this in 2018 and may do so again as volumes dictate. It’s important to note that unopened items in new condition are generally eligible for refunds or exchanges within specific timeframes, such as 90 days for Target and 30 days for Target Plus. So, retailers should build the infrastructure and processes assuming that holiday returns are no longer just a January phenomenon. Peak season now extends from fulfilling the order to handling its possible return, all within the holiday timeline. Embracing that reality is crucial for operational success and customer satisfaction in modern ecommerce.

What sources were leveraged for the key holiday returns metrics cited in this article?

The data points referenced in this article, including the reported 16% increase in holiday-season returns, were sourced from publicly available industry research. The primary source was Seel’s 2025 Returns and Refunds analysis, published via Prosper Show, which examines shifts in return timing and underlying drivers during the holiday season.Seel 2025 Returns and Refunds Report summary (Prosper Show):
https://prospershow.com/media/prosper-blog/holiday-returns-increase-by-16/

Written By:

Rinaldi Juwono

Rinaldi Juwono

Rinaldi Juwono leads content and SEO strategy at Cahoot, crafting data-driven insights that help ecommerce brands navigate logistics challenges. He works closely with the product, sales, and operations teams to translate Cahoot’s innovations into actionable strategies merchants can use to grow smarter and leaner.

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Customer Service AI in Ecommerce: Why Speed Can Destroy Trust

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Ecommerce brands adopted AI in customer service for the same reason they adopt most automation: speed and cost.

Faster responses. Lower headcount. Always-on availability.

On paper, it makes perfect sense. In practice, many brands are discovering an uncomfortable truth. AI that responds too quickly, and too perfectly, can actively damage customer trust.

The problem is not that AI is incapable. It is that customer service is not just an operational function. It is an emotional one.

Many of the insights in this article are informed by real conversations with ecommerce operators, including a live Ugly Talk panel co-hosted by Cahoot that focused on how AI is actually being deployed across customer service, fulfillment, and post-purchase operations. What stood out was not hype, but a recurring pattern. When AI optimizes purely for speed, it often undermines the very trust customer service is meant to protect.

Why Ecommerce Brands Rushed AI into Customer Service

Customer service sits at the intersection of rising costs and rising expectations.

Order volumes increase. Customers expect instant responses. Staffing scales poorly. AI promises relief.

Chatbots can answer questions instantly. They do not get tired. They do not need training cycles. They do not call in sick.

For straightforward tasks such as order status, return policies, and shipping timelines, this works extremely well. But many brands stopped there and assumed more automation would automatically mean a better experience.

That assumption is where problems begin.

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The “Too Perfect” Problem with AI Support

Humans do not evaluate customer service purely on correctness. They evaluate it on intent.

When an AI responds instantly with flawless grammar and total confidence, it often signals something unintended. This system does not understand me.

Customers subconsciously expect friction in emotional moments. A pause. A clarification. A sense that someone is processing the situation.

AI removes that friction and, in doing so, can feel dismissive rather than helpful.

Perfect answers delivered instantly can feel robotic, even when they are correct.

Several operators noted that returns automation often breaks down not because it is wrong, but because it is impersonal. Automatically denying or approving returns based purely on rules can feel transactional at a moment when customers expect understanding. In these cases, efficiency gains came at the expense of long-term trust.

When AI Speed Actively Damages Customer Trust

Customer service interactions rarely start from neutral ground. Customers reach out when something has gone wrong.

A delayed shipment. A missing item. A return issue. A billing error.

When AI responds immediately without acknowledging emotional context, customers interpret speed as indifference. The faster the response, the less heard they feel.

This is especially damaging when the issue is ambiguous, the customer is frustrated, or the resolution requires judgment rather than policy recitation.

In these cases, AI can escalate frustration rather than defuse it, even while technically following the rules.

One operator shared a concrete example of this dynamic from a real AI customer service deployment. The company had rolled out AI across both chatbot and email support and even gave the system a name internally, because referring to it simply as “the AI” felt strange.

The system worked extremely well, perhaps too well. When customers sent long, emotional emails, the AI responded within seconds with a perfectly written, fully on-brand answer. Technically, it was flawless. But the reaction was the opposite of what the team expected.

“When somebody was writing a long, very emotional email, 22 seconds later getting the perfect on-brand response just pissed everybody off,” the operator said.

Customers interpreted speed not as efficiency, but as indifference. The response felt automated, not thoughtful. The issue was not policy or accuracy. It was perception.

The solution was counterintuitive. The team deliberately slowed the AI down.

“So if you are too good and too fast, that is not a good agent,” the operator explained.

By introducing a short delay before responses were sent, customer sentiment improved almost immediately. Speed had not been the problem. Unchecked speed was.

Another story from the discussion highlighted how AI can damage trust when it optimizes for conversion without verification.

In this case, AI analyzed performance data across product listings and identified “UV resistant” as a high-converting keyword for artificial plants. Acting on that signal, the system began adding “UV resistant” descriptions to multiple products, even though the attribute had never been verified.

As one operator put it bluntly, “AI is a confident liar.”

The change initially looked harmless. It was buried in the bullet points. It passed human review. Conversions improved.

The cost showed up later. Within days, customers began returning products after discovering the plants degraded outdoors. The result was not just dissatisfaction, but thousands of dollars in chargebacks and avoidable returns, all traced back to a single unverified optimization.

The lesson was not that AI made a mistake. It did exactly what it was trained to do. The failure was allowing automation to rewrite reality without human verification. As the operator summarized it, trust AI, but verify.

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Where AI Actually Works Well in Customer Service

None of this means AI does not belong in ecommerce customer service. It absolutely does, when used correctly.

AI performs exceptionally well in order tracking and delivery updates, policy explanations, basic returns eligibility checks, initial triage and routing, and data collection before escalation.

In these scenarios, speed is an advantage. Customers want answers quickly, and emotional stakes are low.

The mistake brands make is extending automation into situations where empathy matters more than efficiency.ds make is extending automation into situations where empathy matters more than efficiency.

The Human-in-the-Loop Model That Actually Works

The most successful ecommerce teams don’t ask whether AI or humans should handle customer service. They design systems where each does what they’re best at.

AI should handle volume, answer factual questions, identify patterns, and route issues intelligently.

Humans should resolve ambiguous cases, handle emotionally charged situations, override policy when judgment is required, and restore trust when something breaks.

In practice, this means deliberately slowing AI down in certain moments, not speeding it up everywhere.

This mirrors how AI works best across ecommerce operations when treated as part of a broader operating system for ecommerce logistics, rather than a standalone replacement layer.

Why Trust Is the Real KPI for AI-Driven CX

Most customer service dashboards emphasize speed.

First response time.
Average handle time.
Tickets closed per hour.

These metrics matter operationally, but they are poor proxies for experience.

Trust is harder to measure and far more important.

When customers trust that a brand will resolve issues fairly, they tolerate friction. When they do not, even flawless automation feels hostile.

AI-driven CX should be evaluated not just on efficiency, but on escalation quality, resolution confidence, repeat contact rates, and post-interaction sentiment.

Speed without trust is not customer experience. It is deflection.

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Why Customer Service AI Fails Before Other AI Use Cases

Customer service is one of the first places brands deploy AI and one of the easiest places to get wrong.

Unlike advertising or fee recovery, customer service sits directly in front of the customer. Mistakes are immediately visible. Feedback is emotional, not statistical.

This is why AI adoption here requires more restraint than ambition.

Brands that treat customer service AI as a cost-cutting measure often learn the hard way. Brands that treat it as a trust-preserving layer build durable relationships.

As one operator noted, customer service is uniquely unforgiving. Mistakes are not abstract metrics. They are felt immediately by real people in moments of frustration.

The Right Way to Think About AI in Ecommerce Customer Service

AI should not replace human service. It should protect it.

By absorbing routine volume, AI gives human agents more time to focus on the moments that actually define brand perception.

This philosophy aligns closely with what we see in AI ROI across ecommerce operations: AI delivers value when it removes noise, not judgment.

Customer Service AI Is a Trust Exercise, Not a Speed Contest

Ecommerce brands don’t win customer loyalty by responding fastest. They win by responding appropriately.

AI makes it tempting to optimize for speed everywhere. The brands that resist that temptation, and design for trust instead, are the ones that turn automation into an advantage rather than a liability.

Frequently Asked Questions

Can AI replace human customer service agents in ecommerce?

No. AI works best as a support layer for routine tasks, while humans handle complex, emotional, or judgment-heavy situations.

Why do AI chatbots sometimes frustrate customers?

Because they respond too quickly and confidently without understanding emotional context or ambiguity, making customers feel unheard.

What customer service tasks should AI handle?

AI is well suited for order tracking, FAQs, policy explanations, triage, and routing. These are tasks with clear answers and low emotional stakes.

How can brands use AI without damaging customer trust?

By implementing human-in-the-loop systems, pacing responses appropriately, and escalating sensitive issues to human agents.

How does this fit into a broader ecommerce AI strategy?

Customer service AI works best when integrated into an AI-driven operating system for ecommerce logistics, rather than deployed as an isolated tool.

Written By:

Manish Chowdhary

Manish Chowdhary

Manish Chowdhary is the founder and CEO of Cahoot, the most comprehensive post-purchase suite for ecommerce brands. A serial entrepreneur and industry thought leader, Manish has decades of experience building technologies that simplify ecommerce logistics—from order fulfillment to returns. His insights help brands stay ahead of market shifts and operational challenges.

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Crafting a Return Policy that Drives Customer Loyalty and Reduces Returns

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Let’s talk about ecommerce returns policies. You might be thinking, “What’s the big deal? It’s just a formality, right?” Well, no; your returns policy can either make or break your relationship with your customers. If done right, it builds trust through transparency. If done wrong, it can prevent new customers from buying from you, or worse, it can send your loyal customers running for the hills.

A clear, customer-friendly returns policy isn’t just some legal jargon to throw on your website for show. It’s a vital piece of your customer growth and retention strategy. When your customers feel confident in your return process, they’re much more likely to hit the “buy” button. Why? Because they know that if something goes wrong, they’re covered. No one likes the feeling of getting stuck with a purchase they regret, so a transparent returns policy builds trust. And trust is the foundation of your relationship with your customer.

How to Create a Returns Policy That Benefits Both Your Customers and Your Business

Let’s dive into the how: how do you create a returns policy that strikes a perfect balance between establishing confidence and trust in your brand, and giving away the farm? First, keep it simple. Your returns policy should be straightforward enough that anyone can understand it in a few seconds. Don’t hide the details. Don’t make it overly complex with all kinds of ifs, thens, and buts. Customers don’t want to dig through pages of fine print to figure out how to return something. No one has time for that, especially when they’re annoyed about needing to send an item back.

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Next, let’s talk about timing. You need to give your customers enough time to make up their minds. A reasonable return window (30 days? 45 days?) is fair, but don’t go overboard. A year to return a pair of socks might sound generous, but it’s a logistical nightmare. Somewhere between “24 hours” and “whenever you feel like it” is the sweet spot. Make sure your policy includes crystal clear instructions: what’s returnable, how to return it, and whether it’s free or not (with the respective details).

Here’s an example: I bought a pair of shoes online. They were great in theory: super stylish. But, when I tried them on, they made me look like I was auditioning for a ’90s boy band. Not the vibe I was going for. Thankfully, the return policy was clear and painless. It wasn’t complicated, it didn’t require a dozen emails back and forth, and I got my refund promptly. That’s the type of experience you want to offer: easy, fast, and no run-around. A simple, straightforward policy will make your customers feel like you’ll take care of them if it comes down to it, and that’s important for growing organic brand affinity.

Balancing Generosity and Profitability in Your Returns Policy

Now, here’s the tricky part: balancing generosity and profitability. You want to be kind and flexible, but you also need to make sure your returns policy keeps the financials manageable. Offering free returns on every single order sounds nice, but in reality, not all businesses can afford it, and not all products warrant it.

Here’s the trick: limit what’s eligible for free returns. For example, offer free returns only on high-ticket items or for orders above a certain value. For smaller items, you could charge a small return fee or ask customers to cover return shipping costs. And if your customers are loyal and happy, they’ll be more likely to accept small fees for the service you’re offering.

Other options include a product pricing strategy that increases prices and order value across the board so there’s some extra room to absorb the cost of returns as a whole, rather than treating them on a one-by-one basis. Or consider incentivizing customers to keep items by offering discounts on future purchases. And if you really want to get creative, offer store credit instead of cash refunds for certain returns. This retains the revenue while still letting the customer pick something else out, and they don’t feel like they’re losing out on their original purchase.

Here’s a real-world analogy: I bought an inexpensive phone case from a popular online retailer, but I wasn’t thrilled with it. It was cheaply made, so I wanted to return it. I wasn’t annoyed by the small return fee because the company had been transparent about it upfront. It didn’t feel like they were trying to sneak anything past me. And, honestly, the small fee was worth it to me because they took care of me quickly and offered amazing customer service during the entire process. They were fair with me, so I was fair with them, and that turned me into a repeat customer. You can do the same: offer free returns where it counts, and keep your bottom line in check elsewhere.

Monitoring and Improving Your Returns Policy Based on Customer Feedback

So, you’ve got your returns policy in place. Great. Now what? Keep an eye on it! Your returns policy isn’t a one-and-done deal. The best policies evolve, just like your business does. And the best way to improve? Customer feedback. This is where the real magic happens.

Check your return data. Why are customers returning items? Is it because of sizing issues, poor product descriptions, or something else? This data gives you the power to refine things to prevent future returns. Say you run an apparel shop, and a certain jacket is frequently returned for being too small. Then maybe you need a better size guide. Or, if customers consistently say an item’s color isn’t what they expected, perhaps you need better product photos and/or descriptions.

Just keep in mind that your returns policy should be a living, breathing document that adapts to the changing needs of your customers and your business. Regularly reviewing feedback and adjusting your policy accordingly will help you stay in sync with customer expectations.

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Final Thoughts: Why Your Returns Policy Should Be a Key Component of Your Loyalty Strategy

When you’re building out your returns policy, don’t just slap something together and call it a day. It’s a critical part of your customer loyalty strategy and should be afforded the attention that it deserves. Trust me, if your returns policy is super easy, clear, and fair, it can be the key to a relationship with your customers that keeps them coming back for more. When people know they won’t get stuck with a dud product, they’re way more likely to hit that “Buy” button now, and again in the future.

Remember, when it comes to your returns policy, you’re not just fixing post-purchase problems, you’re building loyalty. Think about it: nobody likes jumping through hoops to return something. So, if you can make it easy and communicate that from the start, they’ll remember that. Happy customers are loyal customers. And loyal customers are how you grow a successful business. Here’s the recap: Keep it simple, clear, and fair. Don’t overcomplicate it. Offer free returns when it makes sense, and pay attention to your data. You’d be surprised at how much info you can get from customer feedback; use it to improve your policy, but also use it to improve your products and merchandising to reduce returns in the first place. Over time, it’ll become more than just a “customer service thing” that online shoppers expect. It’ll actually help attract new customers and keep them around. And isn’t that what we’re all after?

 

Written By:

Manish Chowdhary

Manish Chowdhary

Manish Chowdhary is the founder and CEO of Cahoot, the most comprehensive post-purchase suite for ecommerce brands. A serial entrepreneur and industry thought leader, Manish has decades of experience building technologies that simplify ecommerce logistics—from order fulfillment to returns. His insights help brands stay ahead of market shifts and operational challenges.

 

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How to Reduce Returns in Ecommerce: Innovative Solutions for Balancing Convenience and Profitability in Fashion

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The Fashion-Returns Problem in Ecommerce Returns

Fashion is notoriously the category with one of the highest return rates, especially in online shopping. It’s not uncommon for online apparel retailers to see return rates of 30 – 40% or even higher. Why so high? A perfect storm of factors: sizing and fit issues, customers ordering multiple sizes or styles to try on at home, rapidly changing trends (where a dress might be out of style by the time it’s delivered, causing a return), and of course behaviors like wardrobing (wearing once and returning). Essentially, buying clothes or shoes without trying them on carries inherent uncertainty, and that uncertainty often translates to returns. Clear and comprehensive product descriptions are crucial in setting customer expectations and reducing return rates.

This high return rate is a double-edged sword. On one hand, offering easy returns in fashion is practically a requirement to get customers to click “Buy”. Shoppers are more comfortable purchasing a $200 pair of boots online if they know they can send them back for free if they don’t fit. In fact, 67% of shoppers check the returns policy before buying, and many will only purchase if free returns are offered (Invesp). So convenience drives sales. But on the other hand, each return eats into profitability. The cost of shipping, processing, and then potentially marking down a returned garment can be significant, some estimates say handling a return can cost 20 – 50% of the item’s price in lost value and costs.

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For fashion retailers, this means constantly walking a tightrope: how do you make returns convenient enough to encourage purchases, but not so loose that you hemorrhage money? Moreover, there’s the brand angle; a seamless, generous return policy can be a brand differentiator (Zappos built huge loyalty with 365-day returns), but now even they face pressure as the economics get tough. Implementing industry-specific best practices can help fashion retailers manage return rates more effectively.

Another layer to the fashion returns problem is the environmental impact. Apparel returns often don’t go back on the shelf due to seasonality or wear; some studies found a chunk of fast-fashion returns may even end up in landfills. Plus, shipping clothes back and forth contributes to carbon emissions. There’s growing consumer awareness and slight guilt around that, so retailers feel pressure to handle returns sustainably too. Effective inventory management is essential in handling the logistics of returns and minimizing associated costs.

And let’s not forget fraud and abuse in fashion returns, wardrobing is a big one as mentioned, and bracketing (ordering 5 dresses, keeping 1) is almost an accepted norm now among shoppers. It’s tricky because some of that is legitimate (they truly didn’t know which size or style would work), but it drives up return volumes nonetheless.

So, the problem in summary: Fashion ecommerce must allow and even encourage customers to “try before they fully buy” to mimic the fitting room experience, but doing so leads to very high return rates that can slash into profit margins. The challenge is to innovate solutions that can satisfy customers’ desire for convenience and proper fit, while keeping the return rate and its costs in check. Fortunately, the industry is exploring several innovative strategies, from leveraging technology to rethinking business models, to tackle this balancing act.

Virtual Try-On + AR

One of the most promising tech solutions for reducing fashion returns is the use of virtual try-on and augmented reality (AR). The idea is simple: give customers a way to see how a clothing item or accessory might look and fit on their body (or foot, or face) digitally, before they purchase, thus reducing the guesswork. Accurate product descriptions, combined with virtual try-on technology, can significantly reduce return rates by setting clear customer expectations.

Imagine shopping for glasses online and using your phone camera to see a live AR overlay of the glasses on your face; many eyewear retailers do this now. Or uploading your measurements or a body photo and seeing a dress virtually “draped” on a 3D avatar of yourself. These experiences aim to bridge the gap between in-store try-on and online convenience.

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How does this help? If done well, virtual try-ons can significantly cut down returns by setting better expectations. Customers can filter out items that clearly won’t look right on them. Studies have shown companies using virtual fitting tech have decreased return rates; some report reductions of up to 36% in returns (Retently) due to shoppers having a better sense of fit and style before buying. One case example: TA3 Swim, a swimwear brand, implemented a virtual fitting room tool and saw a 47% drop in size-related returns. That’s huge for the bottom line in a category notorious for fit issues. By providing a realistic preview of how items will look and fit, virtual try-on technology helps manage customer expectations and reduce the likelihood of returns.

There are a few types of virtual try-on tech:

  • AR Visualization: Using your smartphone or webcam to overlay clothing or accessories on your live image. This works well for things like eyewear, hats, jewelry, makeup (think of those filters that show you lipstick or hair color). For full apparel, AR can show how a pattern or style might look on you, but accurate fit (tightness, etc.) is harder to convey with simple AR.
  • Virtual Fitting Room with Avatars: This is where you input data, either body measurements or a body scan, and the system creates a 3D avatar of your body. Then it simulates how the clothing fits that avatar. Companies like Zeekit (acquired by Walmart), True Fit, and others do this. Some require you to do a quick scan with your phone camera to get your shape. The result is more about fit, it might show if a shirt would be loose in the waist or tight in the shoulders on your shape. Some solutions even let you see fabric stretch or drape.
  • Size Recommendation Algorithms: Not exactly AR, but related: based on what sizes of other brands fit you, an AI can suggest the best size in a new brand. Services like True Fit or Fit Predictor ask what size you wear in known brands and styles, then predict the equivalent for the item you’re viewing. It’s a data-driven virtual “try-on” in the sense that it forecasts fit.

By giving customers these tools, fashion retailers can preempt a lot of the “buy two sizes and return one” behavior. If I’m pretty confident after using the tool that the medium size will fit me well, I might not order a large size as well, “just in case,” thus reducing returns from bracketing. Also, seeing the item on a representation of me might make me realize “oh, that color washes me out” or “that cut doesn’t suit my body type,” so I don’t buy it in the first place (which is a lost sale but better than a sale-then-return, arguably).

That said, virtual try-on is still improving. It’s not perfect; sometimes the graphics are a bit off, or it’s hard to capture the exact fabric movement. Also, it requires customers to engage with the tech, which not all will do. But as AR becomes more commonplace (with Snapchat filters, etc.), people are warming up to it. The convenience of doing a pseudo-fitting room session at home is quite appealing.

From a profitability standpoint, implementing AR/virtual try-on is an investment (tech integration, possibly licensing software, or building it). But the ROI can be strong if it materially drops return rates. It can also boost conversion, shoppers might be more likely to buy if they see it on themselves virtually, and like it. It’s like giving them more confidence in their choice.

In conclusion, Virtual Try-On and AR bring the fitting room to the living room. By leveraging these technologies, fashion retailers aim to reduce the number of “trial” purchases that turn into returns. Early results from those who’ve adopted it are encouraging in terms of return rate reduction (Retently). As the tech gets better and more widespread, it could become a standard part of online fashion shopping, benefiting customers (who get what they expect) and retailers (who suffer fewer returns and more satisfied buyers).

Try-Before-You-Buy Models for Online Purchases

What if you could let customers literally try products at home before committing to payment? That’s the concept behind Try-Before-You-Buy (TBYB) models. In fashion, this often manifests as services or programs where a customer can order several items, have a window of time to try them on, and only pay for what they keep (or conversely, get refunded for what they return). It formalizes and streamlines the bracketing behavior, but in a way that can be beneficial to both parties if managed right. Implementing structured processes to manage returns is crucial for the success of Try-Before-You-Buy models.

Examples:

  • Amazon Prime Wardrobe / Try Before You Buy: While recently discontinued to double down on Amazon’s AI-powered solutions, the program operated successfully for many years. It allowed Prime members to pick out, say, 3 – 7 items (depending on the promo) with no upfront charge. Amazon shipped them in a resealable box. The customer had 7 days to decide what to keep. They were only charged for those they kept (Amazon charged the card after the try-on period for items not returned, or earlier if they checked out on the app, indicating what they kept). Anything else, they dropped back in the box with a prepaid label and sent back. This model encouraged customers to try more items (boosting conversion), but by controlling the process, Amazon could manage the logistics of it.
  • Stitch Fix and Trendsend (EVEREVE): These are styling box services, a slightly different but similar idea: a curated box of outfits is sent, you try them on at home, and only pay for what you keep; the rest goes back. The difference is that you don’t pick the items; a stylist or algorithm selects them for you based on your defined preferences.
  • Warby Parker Home Try-On: For glasses, Warby Parker will send you 5 frames to test out at home for 5 days, free of charge, including a prepaid return. You then order the ones you want with your prescription. This is a classic try-before-buy and has been hugely successful for them in reducing the barrier to purchasing glasses online.

From the customer’s perspective, TBYB is fantastic, it’s like shopping in a store dressing room but at home, with your wardrobe and mirrors, etc. It eliminates the risk of losing money on returns because you’re not even charged (or you know you’ll get refunded seamlessly). It often means they might order more items up front (helping sales) because they have that safety. Accurate product information is essential to ensure that customer orders meet their expectations, reducing the likelihood of returns.

From the retailer’s perspective, TBYB can increase initial order sizes and attract customers who are on the fence. It also structures the return process. Instead of random returns at random times, it’s an orchestrated trial. You can plan for those returns (like the box is designed for easy return shipping, the items are expected to come back if not kept). Also, because you have the customer’s card on file and authorization, the risk of not getting paid is lower than, say, someone buying and then doing a chargeback after returning, here you control the flow.

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However, the challenge is that this model, by definition, means a lot of merchandise is going out that will come back. So you need very efficient reverse logistics. The profitability depends on a few factors:

  • Keeping return shipping costs low (perhaps subsidized by bundling multiple items in one box rather than separate orders).
  • A high conversion from trial to purchase, you want customers to keep enough items on average to cover the cost of shipping the rest back. For example, if a customer tries 5 and keeps 1 cheap item, you probably lost money on that transaction. But if they keep 2-3, you might come out ahead due to increased sales volume.
  • Potentially charging a fee if they keep nothing. Some services might eventually implement a small styling fee or deposit that’s waived if you purchase something, just to discourage frivolous try-ons.
  • Ensuring speedy turnaround of returns so items can go to the next customer or back to stock quickly.

A benefit is that when customers have that “home fitting room” experience, they might actually find more that they like, resulting in a higher overall spend and fewer returns down the line because they got the right item. It can also build loyalty because customers appreciate the convenience and trust the process.

To make TBYB work, a retailer often needs a well-integrated system (so you know exactly when the trial period is up, to charge correctly, etc.) and clear communication with the customer (so they know how to return, by when, and what they’ll be charged).

Balancing convenience vs profitability in TBYB:

  • Convenience is high: the customer is super happy.
  • Profitability can be tricky: if not managed, it could encourage excessive returns (since there’s not even a temporary financial outlay by the customer, they might order things they’re only slightly interested in). However, because it’s a structured program, you can gather data: maybe you see customers only keep 20%, then you might tweak what you allow them to order or improve recommendations to increase the keep rate.
  • Some retailers might reserve TBYB for members or high-value customers who are less likely to abuse it. That way, it’s an investment in customer experience for the most loyal segment.

In summary, Try-Before-You-Buy models are an innovative way to essentially embrace returns as part of the sales cycle in fashion. They acknowledge that trying on multiple items is normal for clothing and make it part of the service offering. When executed well, it can boost sales and brand affinity. But it requires tight logistics and a good understanding of the economics to ensure that the increased sales from TBYB outweigh the costs of the many returns it generates. It’s a bold strategy to find that sweet spot where convenience and profitability meet.

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Eco-Friendly Reverse Logistics

The fashion industry is increasingly under scrutiny for its environmental impact, and returns contribute to that problem. Eco-friendly returns solutions aim to reduce the carbon footprint and waste associated with the returns process, while ideally also saving costs (since often, what’s eco-friendly, like using less shipping, is also cost-friendly).

Here are some innovative approaches on this front:

  • Localized Return Drop-offs and Consolidation: Instead of each customer shipping individual return packages back to a central warehouse hundreds of miles away, retailers are partnering with networks of drop-off points (like Happy Returns, now owned by UPS, or physical store partners). Customers can bring their returns (often without needing to box them) to a local drop-off center. These centers then consolidate many returns into one bulk shipment back to the retailer’s hub. This dramatically cuts down on shipping materials (one big box vs dozens of small boxes) and trips (one truck vs many). It’s greener and cheaper. Plus, customers often find it convenient to drop off at, say, a mall kiosk or UPS store with just a QR code. Offering store credits instead of cash refunds can also help mitigate the financial impact of returns.
  • Regional Processing (Reducing Distance Traveled): If you have multiple warehouses, you encourage customers to send to the nearest one. Less distance = less fuel burned. Also, you might inspect and restock locally, then sell to local customers, creating a more circular local loop. For instance, a return in California goes to a CA facility and then is sold to the next CA customer, instead of ping-ponging across the country. Partnering with a reliable shipping company is essential to ensure efficient and eco-friendly logistics.
  • Peer-to-Peer Returns (Direct Resale): Shipping the item directly from one customer to the next buyer without it detouring to a warehouse. This is inherently eco-friendly because it eliminates at least one shipping leg (and all the packaging and handling that goes with it). It’s like reusing the original shipment for a second customer, rather than doing a round trip and then a new trip. Cahoot’s model emphasizes not just cost savings but also the carbon reduction from cutting those extra journeys. For fashion, this can be great for items that are in hot demand but maybe out of stock; you turn a return into a new sale immediately and avoid moving it twice.
  • Biodegradable or Reusable Packaging: Many retailers are looking at packaging that can be reused for returns. Some send a resealable mailer bag, the customer just peels a second strip, and the same bag becomes the return package. That’s a simple idea, but it reduces waste because you’re not using a whole new box. Others provide returnable totes that can be shipped back and reused multiple times. Though a bit costly upfront, reusing packaging multiple times is greener.
  • Conscious Customer Incentives: To align customers with eco goals, some brands offer incentives for eco-friendly return choices. For example, “Drop off your return at our partner location instead of shipping from home, and get a $5 credit.” This nudges behavior that saves the company money (shipping label cost) and the environment (one less truck pickup). Or they might encourage opting for slower shipping for returns if it means consolidation (like “if you’re willing to wait a couple extra days for your refund, we’ll group your return with others to reduce emissions”, maybe not common yet, but conceptually possible).
  • Donation or Redistribution of Returns: For items that can’t be resold as new, instead of trashing them (which is both a waste and creates landfill mass), some brands partner with charities to donate usable returned clothing. It’s eco-friendly and socially responsible. Some even give the customer the option: “Would you prefer to donate your return for a smaller refund or credit”? A few might choose that if they feel altruistic, especially if the refund value is low.
  • Repair and Refurbishment Programs: Encouraging repair over return for minor issues is another angle. If a customer wants to return a jacket because a button fell off, an eco-friendly mindset would be to offer to reimburse a local tailor to fix it, or send them a repair kit, rather than shipping a whole new jacket. Patagonia is famous for promoting repair, while they take returns too, they often encourage fixing gear, which reduces waste.

Now, how does this balance convenience and profitability? A lot of eco-friendly measures, fortunately, do align with cost savings. Consolidating shipments and reducing distances saves carrier fees. Reusable packaging might cost more per unit, but if used multiple times, could lower packaging costs overall. However, some green initiatives might sacrifice a bit of convenience, for example, asking a customer to drop off rather than schedule a porch pickup might be less convenient for them. That’s why some retailers sweeten the deal (with a small incentive or highlighting the ease of drop-off, which often is pretty quick).

Importantly, many customers, especially younger ones, appreciate brands that act sustainably. If you communicate these eco-friendly return options as a brand value, you might get buy-in and even preference from consumers. It can enhance brand loyalty, which long-term is profitable.

An example to illustrate: H&M offers buy online, return in-store. Customers like it because they get an instant refund in person and maybe shop more while there; H&M likes it because it gets people in stores and avoids mailing items around. And environmentally, it’s efficient because trucks that were already going to supply the store can take back returns in bulk.

In summary, eco-friendly logistics in returns are about minimizing the back-and-forth, fewer trips, less packaging, and smarter routes. These innovations aim to make the returns process gentler on the planet and often cut costs for the retailer too. The key is doing it in a way that still feels convenient to customers (or at least, they understand the benefit and are on board with it). When done right, it’s a win-win: you uphold your brand’s sustainability ethos, appeal to eco-conscious shoppers, and save money by trimming waste from the returns operation.

Personalized Returns Experiences for Customer Satisfaction

Personalization isn’t just for marketing and product recommendations, it can extend into the returns process as well. The idea of a personalized returns experience is tailoring the returns process or options to the individual customer’s situation, preferences, or value to the brand, in order to both delight the customer and protect profitability.

Here are some ways personalization can come into play with returns:

  • Loyal Customer Privileges: For your best customers (say those in a VIP tier or who have high lifetime value), you might offer “white-glove” return treatment. This could mean longer return windows, free return shipping always (even if you charge others), or even home pickup service. Some upscale fashion retailers or subscription services offer at-home pickup of returns for top clients, a courier will come to your house to collect the items, which is ultra-convenient. This level of service makes those customers feel valued. Yes, it costs more, but if someone spends thousands a year on your fashion line, it’s worth keeping them happy. It’s akin to how AMEX offers concierge services to platinum cardholders, you’d do a similar thing for VIP return handling. By analyzing return data, retailers can identify patterns and tailor their return policies to different customer segments.
  • Segmented Policies: You can quietly segment your customers by return behavior. For chronic returners, you might tighten things (shorter windows or restocking fees applied), ideally communicated in a soft way. For reliable customers, you might bend rules, like “Oh, you’re two days past the return window? No problem, we’ve processed it anyway because you’re a valued customer.” Some large retailers’ systems auto-authorize exceptions for customers who rarely ask for them, but might flag or deny for those who repeatedly abuse. The customer sees it as personalized leniency just for them (“they made an exception for me!”), which can build goodwill.
  • Customized Exchanges/Replacements: A personalized experience could also mean guiding the customer to find something they do love if the original didn’t work out. For instance, a return portal could say: “Sorry that cocktail dress didn’t fit. Based on your feedback and your past purchases, we think these three dresses might suit you better. Would you like to exchange instead of returning?” This feels like a personal shopping service in the returns flow. If they take an exchange, you saved the sale. Stitch Fix does something akin to this by learning your style; if you return things, they use that info to fine-tune your next box. Providing personalized return experiences can build customer loyalty by making customers feel valued and understood.
  • Content and Support Tailored to Reason: If a customer indicates a reason for return, you can personalize what happens next. Say they chose “didn’t fit”, the confirmation page might show a friendly video or graphic about your sizing guide, encouraging them to use it next time (educational). If they chose “item defective,” you might immediately prioritize that return and maybe send a replacement without waiting (like, “We’re so sorry. We’ll send a new one right away, and you can send the faulty one back whenever.”) That’s a personalized remedy based on their scenario. Essentially, adjusting the resolution to the context.
  • Language/Tone Preferences: Some brands allow customers to set preferences that could extend to returns. Maybe communication tone (some might like super formal vs casual). Or preferred channel: one customer might want everything via email, another via SMS. So your return notifications or interactions could align with that. It’s a subtle personalization but contributes to a seamless experience.
  • Proactive Personalization: If data shows a customer has had multiple returns of the same type of item (e.g., they keep returning jeans), a clever system might proactively reach out: “We notice you’ve had trouble finding the right jeans. Can we help? Our stylist can recommend a pair that fits your style and size.” It’s addressing their specific struggle, which can reduce future returns and make them feel catered to.
  • Personalized Return Methods: Perhaps offer different return method options (mail, drop-off, pickup) and highlight the one that seems best for them. If you know a customer always uses UPS drop-off, next time you might pre-select that option for them in the portal (while still allowing change). Or if someone is in NYC and you have a store nearby, you could say, “Did you know you could bike messenger this back to our Soho store today for instant credit?” Something that suits their locale and behavior.

Balancing convenience and profitability here means giving a bit extra service to those who merit it (ensuring their significant spend continues and outweighs the costs) and gently dissuading those who might be costing more than they’re worth with too many returns. Personalization lets you avoid a one-size-fits-all policy that might be too generous for some and too strict for others.

In fashion, especially, shopping and returns can be an emotional journey; you want a customer to feel good about the process, even if the item didn’t work out. A personalized touch can turn a potentially negative experience (returning something you were excited about, but it didn’t work) into a neutral or even positive interaction (“They took care of me, and I found something else I like”).

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We should note that technology is the enabler here: CRM systems, data analytics, AI: these allow personalization at scale. A small boutique might do this naturally (the owner knows customers by name, etc.), but at ecommerce scale, you rely on systems to mimic that personal touch.

Example: A high-end fashion site might have a tiered membership. A Platinum member opens the return portal, it says, “Hi Alice, we’ve got you covered. We’ve scheduled a FedEx pickup tomorrow at your address, no charge.” Meanwhile, a new customer sees standard options like “print a label”, etc. Alice gets wowed by convenience tailored to her VIP status, new customer still gets a decent baseline service. Both are handled appropriately for their relationship with the brand.

In conclusion, personalized return experiences are about recognizing that not all customers and return situations are the same. By tailoring the process, communication, and options, retailers can make returns feel less like a generic transaction and more like a continuation of the brand’s customer service ethos. For fashion brands, which often cultivate a strong brand identity and customer connection, carrying that through to the returns process can set them apart. It helps maintain the balance where convenience is delivered where it counts most, and profitability is managed by not over-subsidizing returns for those who maybe take advantage.

Summary

Overall, innovating in returns, through tech like virtual try-on, new models like TBYB, greener logistics, and personalized service, is how fashion ecommerce is striving to solve the returns conundrum. Managing ecommerce returns effectively is crucial for maintaining profitability and customer satisfaction. Brands that succeed in this will enjoy loyal customers and healthier margins, truly balancing convenience and profitability in the long run. Ultimately, prioritizing customer satisfaction through efficient returns management can lead to long-term success for fashion ecommerce brands.

 

 

Written By:

Manish Chowdhary

Manish Chowdhary

Manish Chowdhary is the founder and CEO of Cahoot, the most comprehensive post-purchase suite for ecommerce brands. A serial entrepreneur and industry thought leader, Manish has decades of experience building technologies that simplify ecommerce logistics—from order fulfillment to returns. His insights help brands stay ahead of market shifts and operational challenges.

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Optimizing Your Returns Automation Process with Technology

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Why Manual Returns Don’t Scale: Automating the Returns Process

Handling returns manually might work fine when you’re a tiny operation with the occasional return. But as your ecommerce business grows, manual processes quickly become overwhelmed, leading to increased human error. Imagine having to personally approve every return request email, copy-paste shipping labels, update spreadsheets, send refund confirmations… It’s tedious and slow. Manual returns don’t scale for a few key reasons:

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  • Volume and Speed: In a growing business, return volumes can jump to dozens, hundreds, or thousands per week. Customers today expect quick turnarounds, many anticipate their refund within days of sending the item back. If you rely on human-driven steps for each return, you become a bottleneck. One retailer I know shared that before automating, it took them 5-7 days just to initiate a return after a customer contacted them, because someone had to manually email a label and RMA. By that time, the customer was already frustrated. Manual processes simply can’t keep up with the pace consumers demand.
  • Error Prone: Humans make mistakes, especially when doing repetitive tasks. You might generate the wrong label, refund the wrong amount, or mis-type an order number. Each error can lead to more costs (reshipping, apologizing with gift cards, etc.) and erodes customer trust. Automation drastically cuts down these errors by doing things systematically the same way each time.
  • Resource Intensive: Think of the manpower required for manual returns, staff to answer emails or calls for return requests, staff to manually process refunds in the system, and staff to key in inventory updates when an item comes back. All those labor hours cost money and could be better spent on more value-added work (like analyzing why returns happen or improving product info). As you scale, hiring people just to handle returns is not efficient. Automation, on the other hand, lets you handle more returns without linear headcount growth.
  • Lack of Visibility: Often, with manual processes, tracking is ad hoc. A customer might ask, “What’s the status of my return?” and your team has to dig through emails or logs. That’s not a great experience. Automated systems usually come with dashboards or at least standardized records, so you and the customer can see status in real-time. Without that, things fall through cracks, maybe a return parcel arrived, but nobody acted on it because the email got buried.

Automation can also save time by reducing manual efforts, such as automatic label generation and systematic processing.

In essence, a manual returns process might work when you’re fulfilling orders from your garage, but if you’re aiming to be a serious ecommerce player, it will buckle under pressure. Customers will notice too: slow, clunky returns make them hesitate to buy again. On the flip side, scaling up with automation makes returns smooth and nearly self-service, which can boost customer loyalty (people remember a hassle-free return almost as much as a fast delivery).

So, the writing on the wall is clear: if you want to grow and keep customers happy, you need to take the machines out of the hands of your overworked staff and let actual machines (software, and maybe hardware robots) take on much of the load to manage returns efficiently. Let’s explore how.

Automated Authorizations and Labels

One of the low-hanging fruits of returns automation is automating the authorization and shipping label generation process. This is typically the front-end interaction with the customer when they decide to return something. Historically, a customer might have had to call or email to get an RMA (Return Merchandise Authorization) number and a return shipping label. That’s not only labor-intensive on your side, but also annoying for customers. Fortunately, it’s 2025, we have better ways!

Self-Service Returns Portals: Implement an online returns portal where customers can initiate returns 24/7 without needing a human in the loop. This self-service platform allows them to log in or enter their order details, select the items they want to return and the reason, and the system automatically checks if it’s within the allowed return window and conditions. If yes, it immediately generates an RMA number and a return shipping label (or QR code) for them. This whole process can happen in seconds while you sleep. Many ecommerce platforms or 3rd-party solutions (like Loop, Returnly, Happy Returns, etc.) offer this functionality.

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Allowing customers to initiate a return request online significantly enhances the returns process. They can quickly receive a pre-paid return label, simplifying returns and providing real-time updates, which boosts overall customer satisfaction.

What does this achieve? For one, speed and convenience. The customer doesn’t wait for your business hours or a support reply; they get instant gratification (well, as much as one can when returning a product). From your perspective, it eliminated a touchpoint that used to consume staff time and hassle, replacing a clunky manual lookup with a one-click label.

Rule-Based Authorizations: Your returns software can enforce your policy automatically. For example, if the product is past 30 days since delivery, the portal can disallow the return and politely inform the customer of the policy (or allow them to submit a request for exception, which someone can review, but at least the default is handled). If an item is marked final sale, it won’t create a label, maybe it tells them to contact support. You can encode things like “no returns on opened software” or “must return within 14 days for electronics”, and the system follows those rules. This ensures consistency, no more one rep accidentally approving something against policy or forgetting to check a condition.

Instant Label/QR Code Provisioning: Generating the shipping label for the customer is huge. Typically, the system will email them a PDF label or give a download link. Some modern approaches even provide a mobile QR code so the customer can just take that to, say, a FedEx or USPS store and have it scanned to print a label (handy for those without printers). The key benefit is that you’re likely using your discounted shipping accounts and automatically recording the tracking number. So, not only does the customer not have to pay out of pocket (if you offer prepaid returns), but you also have visibility. The moment that label is created, you know return XYZ is in motion and can track it all the way back.

Auto-Communication: Along with the authorization, automation handles emails to the customer, confirming their return request, instructions on how to pack it, where to drop it off, etc. These can be templated and personalized by the system without any human intervention. You’d be surprised how much confusion a simple, clear automated email can save (“Affix the attached label and drop the package at any USPS location. We’ll notify you once we receive it.”, done).

By automating authorizations and labels, you effectively start the return on the right foot: quick and correct. Your system should tie that return into an internal RMA record so your warehouse expects it. When the return arrives, ideally, it can be scanned and matched to that RMA in the system instantly.

This kind of automation scales infinitely better than manual processes. Whether 10 customers or 1000 customers initiate returns on a given day, the system can handle it (you might just see an uptick in label printing volume on your carrier account). It also opens the door to advanced possibilities, like offering exchanges on the portal (the customer can pick a replacement item, and the system might even generate a new order for the exchange once the return is on its way).

In summary, automated RMAs and labels streamline the first half of the returns journey. It removes friction for customers and slashes workload for your team. Plus, it ensures every return is properly authorized and tracked. No more mystery boxes showing up unannounced at the warehouse, no more customers waiting days for a return label. It’s efficient, and it’s something shoppers increasingly expect in the era of Amazon-like convenience.

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RPA in Warehousing and Reverse Logistics

When we talk about automation, it’s not just software clicks, it’s also about automating the physical and administrative tasks in the warehouse or returns center. Effective inventory management is crucial in returns automation as it facilitates quicker analysis of product returns, improves the integration of goods, and enhances overall efficiency in reverse logistics processes. RPA (Robotic Process Automation) typically refers to software bots that automate back-end processes, but let’s consider both the digital RPA and the physical automation (robots, conveyors, etc.) in the context of returns.

Digital RPA for Returns Admin: There are many small tasks in returns processing that can be automated by bots. For example:

  • Updating inventory records when a return is checked in. Instead of a worker manually going into the system to increment stock, an RPA bot can detect the scanned RMA and automatically adjust stock levels.
  • Processing refunds: A bot could interface with your order management or payment system to issue the refund or store credit once the warehouse marks the return as “inspected OK.” This ensures immediate action, customers get their refund faster. Some advanced setups even trigger a refund as soon as the carrier scans the return in transit (trusted returns), that’s more policy than RPA, but RPA can execute it.
  • Generating inspection forms or QA checklists for staff when needed. If certain returns need extra steps (like checking serial numbers), an automated workflow can pop that info up on a screen for the worker as soon as they scan the item.
  • Communicating with the customer: A bot can send out a “Your return has been received and processed” email without someone writing it each time.

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In essence, RPA bots act like super-fast, error-free admin assistants in the background, moving data from one system to another, updating records, and ensuring nothing is forgotten. This reduces manual data entry; for instance, RPA can automatically reconcile the return with the original order and update financial records, something many retailers still do by hand.

Physical Automation and Robotics: Returns areas can benefit from many of the same warehouse automations used in outbound fulfillment. For example:

  • Conveyors and Sortation Systems: If you have high volume, you can set up a conveyor that takes returned packages through a scanning tunnel. Based on barcode/RMA, it can automatically sort packages to different lanes: one for immediate restock items, one for items needing tech inspection, one for refurbishment, etc. This saves a worker from sorting through a pile and speeds up routing.
  • Autonomous Mobile Robots (AMRs): These are like Kiva robots or others that move goods around. An AMR could carry returned goods from the receiving area to the appropriate storage or triage area. For example, once an item is scanned and decided it goes back to shelf A, a little robot could ferry it there. Humans then don’t waste time walking back and forth.
  • Automated Scanning and Identification: Computer vision can assist in checking returns. For instance, an AI camera station could take pictures of each return item and flag if something doesn’t match (maybe the wrong item was returned in the box). This is not mainstream yet, but it’s coming. Some companies are training models to recognize if an item is damaged or worn, though human inspection is still primary in most cases.
  • Sorting bots for packages: There are robotic arms and sorters that can pick up and place items. Covariant, for example, is working on AI robotics that can handle the variety of items in returns, helping reduce processing costs. A robot arm could pick a returned item, scan it, and put it into one of several bins based on instructions from the system.

All this physical automation ties into the software, including warehouse management systems. You might hear the term “hyperautomation” or “end-to-end automation”, meaning combining RPA (software bots) and physical robotics for a seamless process. For example, the moment a return arrives, the system (with RPA) creates a record, a conveyor moves it to an inspector, the inspector uses a tablet that auto-fills info via RPA, then a robotic arm sorts it to the right bin.

It’s worth noting that the level of automation to invest in depends on scale. A small business won’t install conveyor belts, but they might use RPA software to eliminate manual updates. Larger operations handling thousands of returns might see ROI in expensive machinery that saves labor and time.

Real-World Impact: What does this achieve? Speed and accuracy. A largely automated returns center could process returns in hours instead of days. Fewer touchpoints means fewer errors and lower labor costs per return. It also frees up human employees to focus on exceptions or more complex tasks (like truly evaluating a weird case) while routine stuff is handled by machines.

Furthermore, automation provides real-time visibility. If all these bots and systems are integrated, managers can see a dashboard of how many returns came in today, how many are processed, and any bottlenecks (maybe a machine is down or waiting on human approval for a certain batch). You get a more controlled and measurable process.

To sum up, whether it’s software bots taking over repetitive data chores or robots zipping around your warehouse moving goods, automation is about scaling your returns operations efficiently. It ensures that as your return volume grows, your cost and turnaround time don’t balloon out of control. Instead, they might even improve, handling more returns faster with fewer mistakes. This level of efficiency would be impossible with purely manual processes.

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Smart Routing Engines

Not all returns should follow the same path. A “smart routing engine” refers to using algorithms and business rules to decide where each return should go and what should happen to it, rather than a one-size-fits-all approach. This is especially important if you have multiple return destinations or different processing workflows based on item type or condition. A centralized portal can significantly enhance this process by allowing seamless management of carrier accounts, return labels, and automation rules.

Consider a scenario: You have a network of several warehouses, or you partner with a 4PL network that has locations across the country. A customer in California wants to return an item, and you have a facility in California, one in Texas, and one in New York. A naive return system might always send returns back to, say, your main hub in Texas. But a smart routing engine would evaluate and say: “This item is actually in demand on the West Coast, let’s route it to the California warehouse where it can be restocked and sold faster.” Or even, “this item is frequently resold quickly, let’s route it directly to the next order instead of back to any warehouse”, which is how the peer-to-peer returns model works.

Reverse logistics plays a crucial role in optimizing returns processes within ecommerce and online retail. By automating customer returns, businesses can enhance inventory management and customer satisfaction, facilitating a smoother returns experience.

Some capabilities of a smart routing system:

  • Nearest-location routing: Direct the customer to send the return to the closest return center to reduce shipping time and cost. Many return portals can choose a return address dynamically based on customer location and the type of item. This saves money (shorter shipping distance) and time (product gets back into local inventory faster).
  • Distributed returns to fulfill backorders: If warehouse A is out of stock of an item but warehouse B has surplus being returned, route returns to A. This kind of inventory balancing via returns can keep stock levels optimized.
  • Different routes for different product types: For instance, maybe all electronics returns should go to a specialized refurbishment center or back to the supplier, whereas apparel goes to your own warehouse for inspection. The system can generate shipping labels accordingly. If a customer is returning a brand-name laptop, your rules might route that directly to the manufacturer’s RMA facility (some retailers have arrangements for that).
  • Peer-to-Peer and Resale Routing: In innovative models, if a new order comes in for an item that someone else is about to return, a smart system could match them. Cahoot’s peer-to-peer returns program essentially does this, the return gets routed to the next buyer instead of to a warehouse. That’s an advanced form of routing that turns a return into a direct fulfillment for another sale, cutting out extra handling steps.
  • Economic Routing: Sometimes it might be cheaper to not ship a return at all. A routing engine might decide to leverage a “returnless refund” for very low-value items (i.e., tell the customer to keep it or donate it instead of shipping back a $5 item, which some retailers do to save on shipping costs). That decision can be automated based on rules like “if item price < shipping cost, don’t require return.”
  • Sustainability/Carbon-based routing: An eco-conscious routing might choose pathways that minimize carbon footprint, which often aligns with cost minimization. But for instance, consolidating returns to reduce trips can be a routing goal the algorithm optimizes for.

To implement this, your returns management software needs to allow conditional logic. It might integrate real-time data like inventory positions, new orders queue, etc., to make decisions. Some systems use AI for this, especially when optimizing multi-criteria (speed vs cost vs sustainability).

Example in action: A customer in New York is returning a jacket. Your smart system knows:

  • That jacket is selling well and is out of stock on the East Coast.
  • You have a warehouse in New Jersey that could restock it, and one in California that has plenty.
  • It decides to have the return shipped to New Jersey for quick restock, instead of sending it to your usual central warehouse in Ohio (which would take longer and isn’t where the need is highest). Result: The jacket is back on sale in NY in maybe 2 days instead of being in transit to Ohio for a week and then maybe shipped back east for a new order.

Another example: A returned item might even be routed to a different channel. If your online store struggles to sell refurbished items, but your eBay outlet does well, a “smart” system could automatically mark the return for eBay resale. That could generate a task for the eBay team or even list it if integrated.

Benefits: Smart routing cuts down unnecessary shipping (saving money and time), increases the chance of reselling the returned item at full or good value, and can reduce workload by automating decisions that an employee might otherwise have to make case-by-case. It’s like an air traffic controller for returns, directing each package to the optimal destination.

For companies like those in 4PL networks, this is a game-changer. They essentially have a web of partner warehouses, and their software decides which node handles a given return or forwarding shipment, aiming for the lowest cost and fastest service. By coordinating like this, many have achieved significantly lower return costs and faster turnaround.

In summary, rather than treating every return identically, a smart routing engine adds intelligence to the reverse flow. It asks, “What’s the best thing to do with this particular return?” and acts accordingly. As a result, returns processing becomes more efficient, cost-effective, and even revenue-generating (when you can salvage sales via direct reroutes or quick restocks). It’s a prime example of automation not just doing a task, but making a decision, one that humans used to have to make, but at scale, algorithms can often do better.

AI Feedback and Learning Loops for Customer Experience

One of the most exciting aspects of introducing AI and automation into returns is the ability to create feedback loops that continuously improve your processes and even your products by providing valuable insights. Traditional processes are static; you set rules and follow them. But with AI, the system can learn from each return and get smarter over time, adapting to reduce future returns or process them more efficiently. Let’s break down a few feedback loops:

Return analytics plays a crucial role in optimizing the returns process. By analyzing return metrics, businesses can identify issues, make data-driven decisions, and enhance the overall customer experience.

1. Machine Learning from Return Data: Suppose you deploy an AI model to predict return probability. Over time, it will gather more data (every return that actually happens versus those predicted). The model can retrain itself to improve accuracy. If it learns new patterns, say a new fashion trend emerges, causing more bracketing, the model picks that up, and it adjusts its predictions. This means your interventions (like proactive customer outreach for high-risk orders) become more targeted and effective over time, as the AI hones in on true predictors of returns.

2. Adaptive Fraud Detection: If you’re using AI to detect return fraud or wardrobing patterns, it will keep learning as fraudsters change tactics. For example, maybe after you started catching people wearing and returning dresses with obvious stains, fraudsters start trying to clean them better. The AI might then start weighing other signals (like frequency of returns without tags). Essentially, the cat-and-mouse game can be handled by the model updating itself with new training data, rather than you manually updating the rules. As a result, your fraud detection stays a step ahead or at least in step with evolving behavior, safeguarding your business continuously.

3. Product Improvement Loop: AI can help turn returns feedback into actual product changes faster. For instance, an AI text analysis could scour all the free-text return reasons or customer comments and cluster them. It might reveal something like “30% of comments for this new blender mention the word ‘lid’.” That insight goes to the product team, who realize the lid is leaking. They redesign the lid in the next manufacturing batch. Fewer customers will return due to a leaky lid going forward, voila, the AI helped catch a design issue, and now the returns will drop, which is a tangible improvement. Over time, you could even imagine AI that predicts which product features might cause returns (maybe by comparing to similar products’ reviews in the market) and warn you pre-launch. We’re getting into very advanced territory, but it’s conceivable.

4. Dynamic Policy Adjustment: In a learning loop, your system might even adjust some processes automatically. For example, if AI notices that a certain type of return is almost always resellable at full value and customers who return it often buy another item later, it might suggest (or auto-implement) a more lenient policy there (like instant refunds upon drop-off, since the risk is low). Conversely, if a category has high abuse, it might tighten something (perhaps requiring manual review or flagging accounts). While most companies would have a human in the loop for policy changes, AI can at least provide data-driven recommendations regularly.

5. Robotics Optimization: If you use robotics, those systems often use AI to optimize their tasks as well. A robotic vision system, for example, gets better at recognizing products or positions over time via machine learning. So your physical automation can also improve through feedback, faster sorting as it “learns” the typical mix of items, etc.

6. Customer Experience Personalization: AI can feed back into the customer side too. Suppose from returns data, AI figures out you have essentially two cohorts of customers: “careful buyers” who rarely return, and “experimental buyers” who order lots and return lots. You could personalize the website or follow-ups differently: careful buyers get encouragement to try new things (since they’re cautious, maybe they under-buy), whereas experimental ones might get nudges like “check our fitting guide!” or “maybe order one size first, you can always exchange for free” to subtly encourage less bracketing. The system could learn what messaging or incentives reduce returns for each cohort and adapt.

Closing the Loop Example: Let’s illustrate a concrete feedback loop: Your AI flags that a particular sweater in “red” color is returned 3x more often than the same sweater in blue or green. It digs into reasons and finds a lot of “color not as expected.” It analyzes the photo vs reality and finds the red sweater’s image on site looks brighter than it actually is (maybe studio lighting issue). It recommends updating the product photo or description (“Note: color is a darker red tone”). You do that. Next season, the return rate for that red sweater drops closer to normal because customers now know what they’re getting. The AI sees the drop and confirms the fix worked, reinforcing that learning (“color discrepancy solved”). Now, it will be primed to catch similar issues with other products (it might monitor return reasons for color mentions across the catalog). This is a feedback loop improving both customer satisfaction and reducing returns, facilitated by AI analysis feeding into business action.

The beauty of AI-driven loops is that improvements can accelerate. The more data you feed and the more responsive you are in implementing fixes or adjustments, the more you prevent issues proactively. Over time, you may notice your overall return rate decreases or stabilizes at a lower level, or your processing time shortens, thanks to these micro-optimizations.

However, it’s important to have human oversight to ensure the AI’s suggestions or automatic changes align with business goals and fairness. AI is a tool, a very powerful one, but it works best when it partners with human judgment. A learning loop might find a pattern that is technically accurate, but you might choose not to act on it for customer relations reasons, for example.

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Summary

All told, incorporating AI and automation into returns isn’t a set-and-forget situation. It’s more like planting a garden: the AI and automated processes are the plants that grow and produce, but you cultivate them, prune them, and use the fruits (insights) to nurture other parts of the business. Done right, you create a self-improving ecosystem where returns management not only gets more efficient, but actively contributes to making your products, policies, and customer relationships better over time.

Written By:

Manish Chowdhary

Manish Chowdhary

Manish Chowdhary is the founder and CEO of Cahoot, the most comprehensive post-purchase suite for ecommerce brands. A serial entrepreneur and industry thought leader, Manish has decades of experience building technologies that simplify ecommerce logistics—from order fulfillment to returns. His insights help brands stay ahead of market shifts and operational challenges.

 

 

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Combating Wardrobing: Safeguarding Your Business from Ecommerce Returns Fraud

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What is Wardrobing and Return Fraud?

“Wardrobing” is a type of return fraud that’s all too familiar to apparel and fashion retailers. It refers to customers who purchase an item, often clothing or accessories, use it briefly, and then return it for a full refund as if it were new. Essentially, they’re treating your store like a free rental service for their wardrobe. For example, someone might buy an expensive dress for a wedding, wear it once to the event with the tags tucked in, and then send it back claiming “it didn’t fit” or some other excuse. The term comes from the idea of “borrowing from the wardrobe” and returning, or as some call it, “free renting.”

Another common type of return fraud is price switching, where a cheaper item is returned instead of a more expensive one to exploit retailer return policies. Friendly fraud, where customers claim refunds for items they never received (but did) or return used items as new, is also a growing concern.

This practice is a form of first-party fraud, meaning it’s the actual customer (not an identity thief or someone who stole a credit card) engaging in the deceit. They have intent from the start to get their money back after using the product. Wardrobing isn’t limited to clothes; people have been known to “wardrobe” things like high-end cameras or tools (using them for a project and returning), but apparel is where it’s most rampant and where the nickname comes from. Return processes can be exploited for wardrobing, as customers manipulate these policies to their advantage.

How big of a deal is wardrobing? Unfortunately, pretty big. A study in late 2024 found that over two-thirds of shoppers admitted to wardrobing an item at least once. That statistic is startling, it suggests this behavior is moving toward the mainstream. What’s more, most of those who do it say they engage in wardrobing at least once a month! So it’s not just a one-time thing; there’s a chunk of customers regularly using retailers as a temporary closet. Reasons vary, but many do it for financial reasons (76% said they wardrobe to save money or because they only needed the item once). Essentially, if they can get the benefit of the product without paying (by returning it), some will take advantage.

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For retailers, wardrobing is a nightmare. You’re left with used merchandise that often cannot be sold as new, meaning you have to mark it down or dispose of it, pure margin loss. It’s estimated that overall return fraud (including wardrobing and other scams) made up about 10-15% of returns in recent years, costing U.S. retailers on the order of $100 billion in 2024. Return fraud refers to efforts by individuals to exploit a retailer’s return policy for personal gain. Wardrobing is frequently cited as one of the biggest contributors to those fraudulent returns. Beyond the direct financial loss, it’s also frustrating for merchants because it abuses the goodwill of flexible return policies intended for honest customers.

Understanding wardrobing is the first step in combating it. Recognize that when you sell high-end apparel, electronics, or other easily “borrowed” items, a subset of customers might attempt this ploy. Awareness allows you to put safeguards in place. It’s a fine line, you want to accommodate genuine returns (someone who truly found the dress didn’t fit, unworn, should be able to return), but you need to deter and detect those who are gaming the system. The following sections cover how to spot wardrobers through analytics, and policy and operational measures to reduce this kind of fraud.

Behavioral Analytics

One of your best weapons against wardrobing is data, specifically, advanced tools and behavioral analytics that can detect fraud patterns and suspicious activities. Much like credit card companies use algorithms to detect fraudulent purchases, retailers can use analytics to detect likely fraudulent returns.

Start by tracking customer return behaviors at the individual level. Tracking customer return behaviors can help identify suspicious activities that may indicate fraudulent returns. For example, what percentage of a given customer’s purchases are returned? How frequently do they initiate returns, and do those returns often cluster right after weekends or events (a red flag for wardrobing)? A customer who buys an expensive outfit every Friday and returns it every Monday is pretty clearly up to wardrobing. Modern ecommerce platforms and returns management systems allow you to aggregate this data. If not, even basic analysis exporting transaction data to Excel can surface extreme cases.

Behavioral analytics can encompass various signals:

  • Return Rate per Customer: If a customer has a return rate significantly higher than average (say they return 80% of items purchased, when the norm is 20%), that’s a candidate for scrutiny.
  • Usage Signs: Some retailers require noting the condition of returned items. If you can capture or categorize that (e.g., “appears worn” vs “unopened”), then any customer repeatedly returning “worn” items is a red flag.
  • Timing Patterns: As mentioned, look at timing. Wardrobers often buy right before they need an item and return right after. If someone consistently keeps items just for a short period (especially matching common use cases like weekends, holidays, or specific events), analytics can catch that pattern.
  • Category-Specific Signals: If a customer only ever returns certain item types that are prone to wardrobing (like high-end dresses, luxury handbags), and keeps other things, it might indicate they exploit certain categories.

Using these data points, you can employ an AI or machine learning model to predict fraudulent return probability. Some retail fraud solutions (by companies like Signifyd, Forter, or Returnalyze) actually score returns and customers on risk. They ingest historical return data and learn what combinations of factors correlate with wardrobing or fraud. For instance, machine learning algorithms can identify “customers with a high return %, often on expensive items, and frequently cites ‘didn’t need’ as the reason,” and flag them as a risky profile.

With behavioral analytics, you don’t necessarily have to outright ban customers (that can be a last resort). You can start by segmenting them. For example, flag high-risk returners in your CRM. Then perhaps your system can enforce extra checks or stricter rules for them (like no free return shipping, or manual approval needed). Some retailers quietly maintain “watch lists” of serial returners. Even Amazon famously will ban users who abuse returns too much, and they determine that via data analysis of return patterns.

To illustrate, imagine Customer A has bought 10 evening dresses in the last 6 months and returned 9 of them, each time after a weekend. Your analytics could automatically mark Customer A as a potential wardrober. Next time they try to return, you might require an inspection or deny the return if it violates policy (assuming you have grounds, like signs of wear).

Behavioral analytics can also feed into fraud prevention algorithms that operate in real-time. For instance, at the point of return initiation on your website, if the system knows this customer’s history is problematic, it could respond differently (maybe saying “This item is final sale” if that’s enforceable, or simply flag internally for review).

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In sum, leveraging data to monitor and analyze return behavior helps you separate the honest customers from the abusers. It’s a proactive approach; catching patterns early can save significant losses. It also allows you to tighten up on the small percentage of bad actors while still offering leniency to your good customers (who shouldn’t be punished for others’ fraud).

Smart Return Policies

Your refund policy is one of the most direct ways to prevent return fraud and combat wardrobing. A well-defined returns policy can help mitigate returns fraud and abusive return behaviors. By crafting “smart” policies, you create rules that deter fraudulent returns without unduly burdening legitimate customers. Here are some policy tactics:

  • Shorten the Return Window for At-Risk Products: Wardrobers usually want to use the item for a specific event, then return it. If your return window is 60-90 days, it’s easy for them to wait and return. But if it’s, say, 14 days for special occasion apparel, you limit the opportunity. Many fashion retailers have moved to a 14 or 30-day window, which puts pressure on wardrobers, as they can’t wait too long (like after the wedding season) to return. You might keep a longer window for less fraud-prone categories and a shorter one for high-end fashion. Just be sure to communicate it clearly on the product page (e.g., “This item has a 14-day return policy”).
  • “Wardrobing” Tags or Seals: Implement the use of special return tags on expensive clothing. This could be a large tag or sticker placed in a conspicuous area that doesn’t affect trying on, but would be very visible if you wore it out. The policy then states the item is only returnable if this tag is still attached. This physically prevents someone from comfortably wearing the item publicly, unless they’re okay with a giant tag showing (which defeats the purpose for them). Many formal dress retailers do this now. It’s a simple yet effective deterrent, honest customers don’t mind because they plan to remove the tag only when they’re sure they’ll keep it, and wardrobers are thwarted.
  • Restocking Fees for “Rental-like” Returns: While restocking fees can be controversial, applying them in specific cases can dissuade wardrobing. For example, you could have a policy that if certain items (like high-end electronics or designer wear) are returned and show signs of use, a restocking fee of, say, 10-20% will be deducted. Knowing they won’t get a full refund might make a would-be wardrober think twice. However, be cautious, you need to enforce it consistently, and it might lead to some customer service tussles (“I only wore it once, why a fee?”). Ensure your policy explicitly mentions that returned items must be in new, unused condition for a full refund, or else a fee applies. Over half of retailers are considering adding fees if they haven’t already, largely to combat the cost of these abuses. Refund abuse is a significant issue, and rigorous guidelines can help prevent such practices.
  • No Returns on Certain Items: An extreme but sometimes necessary measure, label some items as final sale or no-return. Lingerie or swimwear is often non-returnable due to hygiene reasons, which incidentally also prevents wardrobing them. Some luxury fashion brands do not offer returns on haute couture pieces. If you identify a product line that’s heavily abused and not core to your business, you might cut off returns entirely. Of course, this can deter purchases too, so use it carefully. Alternatively, you could allow returns but only for store credit on those items, not cash back, which is less attractive to wardrobers.
  • Limit Free Return Shipping: If you currently offer free returns for everything, consider modifying that. Perhaps make free returns a perk for loyal customers or for exchanges only. If a suspected wardrober knows they’ll have to pay $10 to send it back, the “free rental” isn’t entirely free anymore. According to industry surveys, some 55% of retailers who didn’t charge fees were considering implementing them, precisely to curb abuse. You could, for instance, still offer free returns on normal items but exclude certain categories (formal wear, electronics) from free return shipping unless defective.
  • ID or Receipt Requirements: In physical retail, many stores require an ID for returns, which feeds into databases that track serial returners (The Retail Equation is an example service). Online, you obviously have the customer’s account info, but if you suspect someone might be exploiting by using multiple accounts, you could require proof of purchase or other verification. This is more on the fringe for ecommerce, but some companies cancel or deny returns if the order can’t be verified against an identity (to combat things like people returning stolen goods for credit, etc.).

In implementing smart policies, the key is to target the policy friction where the fraud happens, while keeping things smooth for honest customers. One approach is a tiered policy: your general policy stays friendly (because you don’t want to scare off regular shoppers, remember 87% of consumers would likely stop shopping with a brand that eliminated free returns entirely (PowerReviews)). But you have fine print or category-specific rules that address wardrobing scenarios. And importantly, train your customer service on these; they need to understand why those rules exist and how to explain them to customers.

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A smart policy might read, for example: “Returns are accepted within 30 days for a full refund. Items must be unworn, with original tags attached. Special occasion dresses must have the return tag intact to qualify for refund. We reserve the right to charge a 15% restocking fee on products that show signs of use.” This kind of language sets the stage to refuse a blatant wardrober return politely but firmly, while allowing legitimate returns. Additionally, implementing proactive measures such as digital tracking and advanced technologies can help identify and adapt to fraudulent patterns, ensuring a balanced approach to fraud prevention.

Warehouse Fraud Controls

Even with good policies and analytics, some wardrobing attempts will slip through to the returns stage. This is where your warehouse or returns center processes need to catch and respond to fraud. Essentially, when a returned item comes in, your team should be on the lookout for signs of wardrobing or other fraudulent activities and handle it according to your protocols.

Dishonest employees can also play a role in return fraud. They may collaborate with external parties to manipulate the return process and facilitate fraudulent returns.

Key controls and practices include:

  • Thorough Inspection: Train your returns processing staff to carefully inspect high-risk returns. They shouldn’t just verify the item is in the box; they need to check for subtle signs of use. For clothing: look (and sniff!) for perfume, smoke, or sweat odors, makeup or deodorant stains, stretched fabric or creases in places that indicate wear, scuffed soles on shoes, etc. For electronics: check the device’s usage logs if available (some devices can show last used date or total run time), look for any user data left on it, check for scratches that indicate it was out of the box. Having a checklist for “what to inspect” per category helps maintain consistency.
  • Photographic Evidence: It can be useful to take photos of items that are returned in unsellable condition due to use. This serves two purposes: documentation in case you need to prove to a customer why their refund was denied or partial (e.g., “you returned a dress with obvious wear; here are the photos we took as evidence”), and data collection for your internal use. Some retailers even take a photo of each return as it’s processed for records. With smartphones, this is not hard, though at scale it’s extra work, so you might reserve it for suspicious cases.
  • Triage by Risk at Intake: If your analytics or RMA system flagged a particular return/customer as high risk, inform the warehouse team via the system. For instance, the return label or packing slip could have a note “FLAG, inspect carefully” or something. That way, the staff know to scrutinize that one extra hard. In such cases, you might require a manager’s sign-off before approving the refund. This ensures wardrobing doesn’t get a free pass due to an overworked junior associate missing something. Essentially, integrate the earlier “behavioral flags” into your returns processing workflow.
  • Decision to Refuse or Charge: Empower your team with clear guidelines on what to do if they confirm a wardrobing case. Some companies will outright deny the refund and ship the item back to the customer (at the customer’s expense), citing policy violation. Others might issue a partial refund (deducting a restocking fee or amount for the damage). Whatever you choose, have it defined. For example, “If an apparel item is returned visibly worn or damaged not due to our error, we will not refund and will notify the customer that the return was not accepted.” You’ll need customer service to back the warehouse up on these decisions. It can get sensitive, because you might have an angry customer claiming they didn’t wear it. That’s where evidence and having the policy clearly on their order receipt helps. Fraudulent refunds are a significant concern, and having clear policies helps mitigate these issues.
  • Tamper-evident Packaging: For products like electronics or accessories, using tamper-evident seals can help. If a customer returns a box and the seal is broken, you know it was opened/used. You can then verify contents to ensure they didn’t do a “parts swap” scam (some fraudsters will buy a new item, put their old defective item in the box, and return it). Warehouse staff should cross-check serial numbers or IMEI numbers for electronics to make sure the same unit that was shipped out is what came back. This prevents a classic fraud of returning a different, older item or a cheaper item.
  • Logging and Blacklist: Keep an internal log of fraudulent returns. If you identify a wardrober, tag their account. If someone sends back a box of rocks instead of the item (yes it happens), definitely blacklist that individual. A centralized system to mark problem customers will prevent future sales to them or at least allow you to reject future returns. Industry data shows that more than three-quarters of retailers claim to have a returns abuse mitigation strategy in place, which often includes such internal tracking.
  • Collaboration with Loss Prevention: Treat return fraud like shoplifting. Many retailers involve their Loss Prevention (LP) or fraud teams to analyze returns and even investigate if it’s organized (e.g., some wardrobing could be part of a rental scam ring). LP can help with gathering evidence, and in extreme cases, pursuing civil or legal action if the losses are significant and the fraud is provable. Additionally, altered receipts are often used in these scams, so training your team to recognize them is crucial.

In essence, the warehouse is your last line of defense. By catching wardrobed items and not blindly restocking them, you avoid reselling a used product to another customer (which would hurt trust), and you can attempt to recoup something. Perhaps a used-but-returned item can be sold on a secondary market or donated. At minimum, you stop the fraudster from getting fully away with it.

It’s important to integrate these controls without overburdening your returns operation. Focus on the high-risk fraction, you don’t want to slow down all returns processing for the 90% honest returns because of the 10% that are bad. Use the earlier analytics to target where extra scrutiny is needed, and keep the regular returns flow efficient.

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Industry Collaboration and Shared Signals

Wardrobing and return fraud aren’t just one retailer’s problem, it’s industry-wide. Serial abusers often hop from retailer to retailer. They might get blacklisted at one store, and move on to the next. This is where industry collaboration and sharing of “fraud signals” can help the entire retail ecosystem, especially in the context of online shopping, where fraudulent activities are on the rise.

Ecommerce merchants are increasingly using online portals to monitor customer return histories and identify patterns of return fraud. These platforms are crucial for enhancing the overall fraud prevention strategy.

One existing method in brick-and-mortar retail is using services like The Retail Equation (TRE), which keeps a database of consumer return activity. When a customer returns something in a store that uses TRE, their ID is recorded and their return behavior is tracked across many retailers, allowing for cross-reference to ensure the legitimacy of transactions. If they hit certain thresholds (like too many returns), retailers can choose to decline the return. In the ecommerce space, an analogous concept is starting to emerge via digital fraud prevention networks. Companies like Riskified or Signifyd aggregate data across multiple merchants, so if a known fraudster (by email, address, device fingerprint) is identified at one store, others in the network get alerted. This is crucial for maintaining customer trust, as it helps ensure that genuine customers are not affected by the actions of fraudulent actors.

While privacy concerns mean you can’t just share lists of names freely, participating in these fraud consortia can give you a leg up. For example, a fraud detection service might flag an order as “high risk, user has history of abusive returns elsewhere” if that data is in the network. Then you could take preventive action even on the initial sale, or at least be on alert for the return. This is vital for protecting the financial health of your business, as unchecked return fraud can significantly impact monetary stability and customer trust.

Another collaborative approach is through industry associations or forums. The National Retail Federation (NRF) often publishes studies on return fraud and facilitates discussions on policy approaches. Some retailers have collectively considered stricter norms, like not allowing returns on worn merchandise (which sounds obvious, but enforcement is the key). If major players all adopt similar stances, it sets customer expectations and reduces the “I’ll just go to another store” workaround. For instance, when several big apparel companies all implemented return tags on formal dresses, it became much harder for wardrobers to find a loophole. This also helps in identifying potential fraudulent actors who exploit lenient return policies.

Retailers can also share qualitative signals informally, e.g., through loss prevention circles. If there’s a known scam going around (like a group of people buying expensive outfits, then returning them en masse after an event), they can warn each other. In some cases, law enforcement can get involved if it’s organized and crosses a certain monetary threshold, since then it might be considered fraud or theft.

Technology might soon enable more real-time sharing of return fraud intel. Picture a blockchain or encrypted database where retailers contribute anonymous data on returns flagged as fraudulent. If the same user or address pops up, the network could notify participants. This is speculative, but technically feasible as a future solution for collective defense against serial return abusers.

There’s also an opportunity to collaborate on the solutions side, for instance, creating a centralized platform for reselling returned apparel that multiple retailers feed into. If wardrobers know that the industry has a way of quickly reselling and not taking a big loss on returns, the incentive might diminish (though that’s more about cost recovery than prevention).

Finally, consider working with your ecommerce platform or marketplace partners. If you sell on marketplaces (Amazon, eBay) as well as your own site, share information internally about fraud patterns. Amazon, for one, monitors and will ban customers who abuse returns across any sellers on their platform. On your own site, if you use a platform like Shopify, check if they have apps or services that identify risky accounts, possibly by leveraging data from other stores.

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Summary

In summary, while you might feel alone dealing with wardrobing, remember that retailers are in this together. Shared knowledge and concerted efforts can make a difference in combating fraudulent activity and maintaining customer loyalty. By aligning policies (so fraudsters can’t just shop elsewhere for an easy return) and sharing data carefully, the industry can tighten the net on wardrobers. It’s similar to how banks share info on check fraud or insurers share on insurance fraud; collective action helps reduce the loopholes. As these collaborations grow, wardrobing will become harder to pull off without consequence, which is exactly what we want to safeguard our businesses.

Written By:

Manish Chowdhary

Manish Chowdhary

Manish Chowdhary is the founder and CEO of Cahoot, the most comprehensive post-purchase suite for ecommerce brands. A serial entrepreneur and industry thought leader, Manish has decades of experience building technologies that simplify ecommerce logistics—from order fulfillment to returns. His insights help brands stay ahead of market shifts and operational challenges.

 

 

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How To Evaluate Return Platforms: Beyond Pretty Portals And Generic Insights

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If your returns platform is only a nice portal, you are paying for decoration. Returns software should lower reverse logistics costs, raise customer lifetime value, and protect margin. Otherwise, it is a rounding error with a great landing page.

The Stakes: Returns Are A P&L Problem, Not A UX Project

Retail returns hit an estimated $890 billion in 2024, roughly seventeen percent of sales. That is a tidal wave of shipping costs, restocking, and fraud that makes or breaks profitability for many ecommerce businesses. No wonder most retailers say upgrading returns capabilities is a near-term priority. 

Meanwhile, carriers and networks are piling in. UPS’s Happy Returns touts faster restocking and fewer support contacts via box-free, label-free returns. FedEx launched Easy Returns with thousands of drop-off locations to compete head-to-head. Narvar is aggregating pickup and drop-off networks across Walgreens, Kohl’s, UPS, and others. Translation: the last mile of returns is consolidating, and your software choice decides whether you can tap those cost curves. 

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How I Vet Return Platforms In 2025

I run the same playbook whether I am looking at Loop, AfterShip, Narvar, or anyone else. Keywords matter for SEO, but decision-making comes down to money saved per return and revenue kept.

1. Measure Net Revenue Saved, Not Portals Shipped

Ask for a cohort view that shows return rate, exchange rate, store credit adoption, instant exchanges, and bonus credit uplift by product category. If a vendor cannot show the delta versus your current baseline, the “actionable insights” claim is hand-waving. Returns management software must raise exchange and store credit conversion, or it’s not boosting customer lifetime value. 

2. Verify Real Reverse Logistics Levers

Pretty return portals are table stakes. What you want is operational leverage. Consolidated drop-off networks to reduce labels and touches, in-app tracking that accelerates refunds without adding support tickets, and automation rules that release refunds only on delivered scans. AfterShip emphasizes automated status updates tied to tracking events. Narvar and Happy Returns emphasize consolidated returns and faster restock. Map these features to your shipping costs and warehouse handling steps. 

3. Treat Fraud Like A First Class Citizen

Return fraud is not an edge case. Retailers report significant exploitation. You need eligibility rules, blacklists, serial returner controls, and SKU-level policies baked into the platform, not handled ad hoc by customer support. If “fraud prevention” is a slide but not a permission set, keep looking. 

4. Ask For Exchange Intelligence, Not Just “Instant Exchanges”

Instant exchanges are useful, but the real trick is routing exchanges to in-stock substitutes and surfacing cross-sell recommendations that recover revenue. Demand evidence that exchanges meaningfully reduce refunds on your top return reasons. Loop, for example, positions connected returns, exchanges, and fraud in one workflow. Install the demo on low-volume traffic for two weeks and compare the exchange-to-refund mix. 

5. Force An Ops Dry Run With Your 3PL

Your 3PL or fulfillment centers must actually receive, triage, and restock returns that the portal generates. Wire up the return label, RMA codes, and status webhooks with your warehouse management system so returns hit the right dock door and bin locations. AfterShip documents webhooks that create returns in a WMS automatically. Do not go live until your warehouse confirms SLAs for inspection, grading, and restock. 

6. Model International Returns And Landed Cost

International returns amplify shipping costs and duties. Your returns software should auto-calculate options that minimize waste. For example, local consolidation, returnless refunds on low-value items, or dynamic rules that steer sendbacks to regional partners. If the demo has no answer for international returns, your “global brand” plan is domestic only.

7. Benchmark Time To Refund And Support Noise

What is the median time from return initiation to refund, and what percent of shoppers hit customer support for status? Happy Returns claims faster restocking and fewer contacts with pre-verified, consolidated flows. Ask every vendor to show your projected time to refund and expected contact reduction with their network, not generic case studies.

8. Check Price Transparency And Volume Tiers

Returns platform pricing often looks cheap up front, then adds per-return overages. AfterShip’s public pricing shows monthly tiers and per return fees beyond the cap. Make sure you consider seasonality, peak returns after major sales events, and any carrier kickbacks that are not passed through.

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Where The Big Names Fit Right Now

  • AfterShip Returns. Strong on branded return portal, tracking native to the suite, automation rules, and sane APIs. Better if you want one vendor across post-purchase and tracking.
  • Narvar. Big on concierge drop-off networks and policy enforcement at the edge. Good for retailers who benefit from wide physical networks.
  • Happy Returns by UPS. Best when you want box-free, label-free consolidation that speeds grading and restocking. Momentum with UPS footprint.
  • Loop. Oriented to DTC brands that want exchanges to dominate refunds and fraud tools tied to returns flow. Validate claims against your data.
  • Cahoot Returns. Peer-to-peer returns to the nearest partner node, lower miles, first-scan refunds, faster resale, exchange-first rules, more eco-friendly, happier customers.

My Shortlist Criteria

If I were buying returns management software tomorrow, I would require: 1) the exchange rate is up at least twenty percent within sixty days, 2) refund issuance automated on carrier scan, with exceptions for high-risk SKUs, 3) drop off consolidation available in my top twenty return ZIP clusters, 4) fraud flags and serial returner policies I can tune without engineering, and 5) clean integrations with my OMS, WMS, and carriers so the entire returns process is observable. Otherwise, I keep my money.

The Cahoot Angle

We designed our returns management software around peer-to-peer returns, not warehouse boomerangs. When a shopper starts an ecommerce returns flow, AI grading and photo verification inside the return portal assess the condition, then auto-lists the item as Like-New with a smart discount, and we prompt the returning shoppers toward instant exchanges, store credit, or bonus credit that protects customer lifetime value. The magic is the shipping: once a new buyer checks out, the original customer ships directly to the next customer, so the entire returns process moves forward instead of backward. That shift slashes reverse logistics miles and shipping costs, speeds resale, boosts customer loyalty, and turns retail returns into more revenue rather than sunk costs. You still get actionable insights, real-time order tracking, and clean integrations with your OMS, WMS, and carriers, so you can manage returns, refine your returns policy, and save time and money without adding warehouse handling. Use Cahoot as a comprehensive solution or connect it alongside returns platforms like Loop, AfterShip, and Narvar to deliver easy returns and a better post-purchase experience at a lower cost.

Frequently Asked Questions

How Do Return Platforms Reduce Shipping Costs Without Angering Shoppers?

Consolidating box-free drop-offs, refunding on “first scan” rather than on arrival, and steering instant exchanges to in-stock alternatives all lower reverse logistics costs while preserving a happy returns feel. Vendors like Happy Returns and Narvar build the physical networks that make this viable.

Does Charging For Returns Kill Customer Loyalty?

The industry is shifting away from blanket free returns, but the winners keep loyalty by offering exchanges, bonus credit, and convenient drop-offs. Shoppers still rank returns as a key factor in purchasing decisions, so communicate clearly and offer fair options. 

What Metrics Should I Track In Returns Management Software?

Exchange rate, store credit rate, refund share, time to refund, cost per return, and touchpoints per return. If the platform cannot expose these, you cannot manage returns as a profit lever. 

Which Platform Is Best For International Returns?

Look for dynamic rules that enable local consolidation and returnless refunds for low-value goods, plus integrations to your international carriers. Most vendors can support this, but proof comes from your lanes and your tariffs.

How Do I Connect A Return Portal To My Warehouse?

Platforms like Cahoot and others offer hosted apps and APIs so that returns management can be done inside the respective UI’s, or from within your warehouse management system. Test the full operations workflow before peak season.

Written By:

Jeremy Stewart

Jeremy Stewart

Jeremy Stewart leads customer success at Cahoot, helping merchants achieve high-performance logistics through smart technology and process optimization. With a background in both ecommerce operations and client services, Jeremy ensures that every merchant using Cahoot gets measurable results—whether they’re scaling from one warehouse to many or managing complex returns.

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Amazon’s New Star-Only Review System Is a Seller Nightmare

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I’m not saying that Amazon just made it harder on sellers; I’m saying that Amazon just made it WAY harder on sellers. Starting August 4th, they’re letting buyers give star-only seller feedback, with no context, no words, just… a number.

What Changed, and Why It Matters

Amazon’s latest “improvement” to seller feedback rolled out on August 4th. Now, buyers can leave a rating with as little as a single star and zero explanation or context. Optional text, Amazon says. Optional clarity, empathy, or sanity, many sellers say.

While that might sound small, it’s a seismic shift. Sellers rely on detailed feedback to troubleshoot operational issues, improve product listings, and contest unfair complaints. With star-only reviews, you lose that entire playbook. And sellers are furious; loud voices on forums calling it “top 3 worst ideas ever,” noting that without comments, they lose visibility on why customers are unhappy.

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Sellers Are Already Feeling the Fallout

One seller reported in the Amazon seller forum that their ASIN rating dropped from 4.3 to 3.7 within the same day. Same number of reviews. No new written feedback. Nothing to explain what went wrong, or if anything went wrong at all. This is what chaos looks like in Seller Central. I’ve personally fought with Amazon Support about a similar issue, and their response was:

“Amazon calculates a product’s star rating using machine-learned models instead of a simple average. To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.”

So, appealing unfair feedback? Nearly impossible now. Amazon support can’t reverse a one-star with no text, because there’s no violation to point to. It’s algorithmic poison; no cause, just effect. And that effect could be losing the Buy Box, tanking conversion rates, or triggering a suspension based on your Order Defect Rate.

Context isn’t fluff. If a buyer leaves one star, but you don’t know why: was it late shipping? FBA mix-up? A broken box? No clue. You can’t fix what you don’t understand.

This Isn’t Just Inconvenient, It’s Dangerous

Amazon claims this change increases review volume. But what it really does is increase noise. Worse, it opens the door to manipulation. Fake reviews generated by AI are harder than ever to detect. Add anonymous star-only ratings, and you’ve got a system ripe for abuse by bots, trolls, or competitors.

Even good buyers can mess it up. They might mean to rate the product but accidentally ding your seller account. Or they get mad about a delayed FBA delivery and blame you. With no explanation, your reputation suffers; silently.

Operational Blind Spots Are Growing

Every business needs feedback to improve. Star-only reviews remove the diagnostic part of the equation. You can’t fix what you can’t see. And with the new limitations on Buyer-Seller Messaging, the result is ecommerce operators flying blind, pouring more money into ads just to recover from a reputation hit they can’t even diagnose.

Imagine paying $3 – $7 per click just to regain trust… because someone left a one-star out of spite, boredom, or by mistake. That’s where we are.

What Sellers (and Cahoot) Can Do About It

  1. Proactively ask for real reviews. Use your own post-purchase outreach to encourage thoughtful feedback.
  2. Track your own trends. If you see star drops, cross-reference with ticket volume or specific ASIN complaints. Create your own narrative.
  3. Flag patterns. If multiple one-stars hit in a short time, document it, even without comments. Push for support escalation.
  4. Join seller alliances. Explore coalition tools that give sellers more voice, data, and pressure to reverse bad policy.

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Final Thoughts

Amazon says simplifying feedback will get “more ratings faster.” But what they gloss over is: more meaningless ratings, and for sellers, that meaninglessness could translate into damage you can’t see, can’t appeal, and can’t fix. Cahoot is keeping a vigilant eye on this because when feedback gets dumbed down, sellers get hurt.

Frequently Asked Questions

How does the new Amazon review system affect seller ratings?

Buyers can now leave a star rating without writing a comment, making it harder for sellers to understand or appeal negative feedback. This can directly impact seller metrics like Order Defect Rate and Buy Box eligibility.

Can sellers appeal unfair star-only reviews?

It’s very difficult, since Amazon requires a violation of its policies to remove a review. Without written context, there’s little basis for appeal, even if the rating is clearly inaccurate or malicious.

What can ecommerce sellers do to protect their reputation?

Sellers should proactively gather detailed customer feedback through other channels, track internal support issues, and consider collaborating with platforms like Cahoot to push for better review transparency.

Why did Amazon make this change?

Amazon claims star-only reviews will increase review volume and ease for customers, but many sellers believe it prioritizes quantity over quality, creating more harm than help for legitimate businesses.

Written By:

Jeremy Stewart

Jeremy Stewart

Jeremy Stewart leads customer success at Cahoot, helping merchants achieve high-performance logistics through smart technology and process optimization. With a background in both ecommerce operations and client services, Jeremy ensures that every merchant using Cahoot gets measurable results—whether they’re scaling from one warehouse to many or managing complex returns.

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