Returns in Omnichannel Retail

I walked into ZARA Oxford Street at the weekend to return a few things I'd ordered on the app. And I walked out 2 minutes later, return done, push notification confirmation already on my phone. Here's what happened: Zara now has a self-service returns station. A big touchscreen kiosk above a counter. You scan your QR code from the app, place your items on the counter, and the screen picks them up instantly via RFID. Every item appears with its photo, size, and price. You confirm, pack the items into a paper parcel they provide, the kiosk prints a label, you stick it on, drop it in a built-in post box, and you're done. No queue. No staff interaction. No "we'll email you in 5-7 business days." I got a push notification confirmation before I'd even left the store. This is what good omnichannel looks like in practice. Four systems working together in one 2-minute experience: the app identifies you, the in-store kiosk handles the touchpoint, RFID does the item recognition, and push closes the loop. The customer barely has to think. Returns are still the most painful part of online shopping for most brands. Zara just made theirs faster than buying a coffee. That matters because easy returns drive repurchase. If I know returning something is painless, I'm more likely to buy. Especially in fashion, where sizing is always a gamble. Have you seen anything like this at other retailers? I'm always collecting examples of omnichannel done well (or badly). Drop yours in the comments.

Last year I had a call with the VP of ecommerce of a $300M+ retail company who was convinced their 32% return rate was "just the cost of doing business" When I dug into their data I discovered that almost half of post-purchase revenue loss is preventable. This happens all the time, retailers are pouring their heart and budget into hitting sales targets, only to watch a third of that revenue disappear due to inefficiencies and refunds. It's demoralizing to be a retailer these days. It doesn't have to be this way! Here's the playbook we used to help that company recover over $6.8M in just 4 months: Most retailers focus on the wrong metrics, for example they celebrate $10M in sales while silently losing $3.2M to returns, and another $1M to operational inefficiency, plus $800K to return fraud and abuse. Quick observations: Your "best customers" are killing you! 37% of "VIP shoppers" are serial returners, they look great in your CRM but they're negative margin customers. We found one customer returning over $14K → this is totally preventable! This is our framework that we developed after working with hundreds of enterprise retailers in the past 5 years: Prevent returns Enable size/style swaps and allow for uneven exchanges (more expensive or cheaper options) Store credit options instead of refund Relevant product recommendations for exchange and upsell Analyze the return reasons by product - this can save you a lot of products from being returned! Results: Over 60% reduction in refunds b) Prevent fraud and abuse Fraud rules to prevent return abuse Automate policy enforcement and verification of product quality before the product is sent back Product inspection workflows at the warehouse level Results: the highest we seen last year for a customer was over 90% c) Streamline Operations Setup rules for returns routing to the closest warehouse or outlet stores Minimize clicks and enable a scan, scan, refund workflow Centralize all returns data and actions into one system, to prevent system switching Results: 42% faster processing Returns are not a cost of doing business. They're a goldmine of hidden opportunities. But here's the truth: Most retailers will read this and do nothing. They'll keep losing millions because "that's just ecommerce." The smart ones will see this as the competitive advantage it is. What side do you want to be on? P.S. If you're a retail executive seeing 20%+ return rates, DM me. I'll share our full framework as it’s way more detailed.

Spend time in stores - observe, ask questions, listen — and the story becomes clear. Returns aren’t just numbers on a dashboard. There are racks full of unsold product. Backrooms are filling up. Product returned, then re-ticketed, re-routed, or placed back out — often into assortments that don’t need them. And when you talk with store managers and district leaders, you’ll hear the same themes again and again: -Returns slow down the front end, especially during peak traffic, impacting CX -Product comes back that never belonged in the store to begin with -There’s little visibility into why items are being returned -And the product coming back isn’t moving out — it’s often marked down quickly or lingers until it’s cleared out. Stores are challenged as to where to merchandise it in the store. This is bigger than operational friction — it’s a profitability issue with roots far upstream. And the broader data confirms what the field already knows: -Returns are projected to hit $890B in 2024, or 17% of total U.S. retail sales (NRF + Happy Returns) -Returns cost $25–$30 per item, on average, in labor, freight, and inventory value loss (Narvar, Optoro) -Returned product inflates inventory and distorts category turns, making planning decisions harder -Markdowns accelerate, and stores become clearance zones instead of brand storytellers -Fit issues, unclear expectations, and poor spec execution are rarely detailed in return documentation, but show up everywhere on the floor. So what’s going on? Returns are often a symptom of upstream missteps: -Loose size specs and inconsistent fit -Product imagery that doesn’t reflect the actual item -Poor routing logic that pushes product to the wrong store -Weak capture of return reasons — “didn’t fit” isn’t enough We talk about “bracketing” as if it’s a customer quirk — but often it’s a direct result of inconsistent sizing or unclear fit guidance. That’s not behavior. That’s feedback. Here’s where brands can take control: 1. Tighten vendor tolerances for better consistency across styles 2. Upgrade product info and visuals — specs, lifestyle imagery, model sizing, real fit notes 3. Route inventory smarter, using returns data to avoid problem-SKU/store combos 4. Introduce store credit or exchanges as frictionless alternatives to straight refunds 5. Capture and act on return data beyond checkboxes — what came back, and why. The return may be the end of the customer’s experience, but the root cause usually started months earlier in specs, planning, and product decisions. The answers won’t come from a KPI report— they’ll come from listening closely to the floor. If you're close to this — in planning, stores, ops, or CX — what return signals are you seeing? And what’s working to reduce them? #retail #ecommerce #returns #storeops #customerexperience #retailstrategy #upstreamfixes #reverseSupplyChain

Killer graph. Out of the £130 billion online non-food purchases we make in the UK, £27 billion of them get sent back to retailers. Our research with ZigZag Global shines a spotlight on the significant challenge online returns cause in the industry, focusing on those consumers who consistently and intentionally over-order - the "serial returners". Key stats ➡️ Around 11% of online shoppers are serial returners (frequently over-ordering with the intention of returning many items) ➡️They account for 24% of all online returns ➡️Serial returners send back, on average, £1,400 worth of online orders per year, compared with an average of £650. ➡️ This amounts to £6.6 billion of returns. ➡️ Almost three-quarters of serial returners are under the age of 45, and they return more than 42% of all their orders. A 1/4 of serial returners admit to over-ordering just to reach a minimum order value (often to trigger free delivery) only to return goods they had no intention of keeping. The same proportion also said they had returned items after finding them cheaper elsewhere or on promotions. While 18% admitted to returning items having already used them for a short period. There is no silver bullet here that is going to fix this issue for retailers. A nuanced understanding of specific triggers and barriers is essential to effectively target returners through pricing and returns options. 💥 For many boardrooms debating whether they should charge for returns, my thoughts are: 💥 The returns equation transcends simple binary choices between free or paid. Retailers must architect differentiated returns propositions that align commercial realities with customer lifetime value. Smart retailers will segment their returns strategy by customer profitability metrics, leveraging AI to identify purchase patterns that predict long-term value. This enables dynamic returns pricing that protects margins while fostering relationships with truly valuable customers. The goal isn't to punish returns – it's to price them according to their true cost to serve, while rewarding profitable shopping behaviours. There's also a paradox at play where customer acquisition costs are optimised but customer profitability is compromised. Many retailers are essentially subsidising unsustainable shopping behaviours at the expense of margin, unknowingly targeting customers they could do without. The real opportunity lies in leveraging returns data as a predictive indicator of customer profitability. By applying advanced analytics to returns patterns, seasonal purchasing behaviours, and cross-category browsing and mining deep behaviour insights, retailers can enable proactive intervention before profitability erodes. This shifts the conversation from universal policies to personalised solutions that can turn returns from a pure cost centre into a strategic lever for customer engagement and loyalty. Full research is available to download here ⬇️ https://lnkd.in/e5paRNWC

Returns are still treated like an afterthought at many fast growing apparel brands. That is getting expensive. In 2026, retail returns are projected to approach $900B, with roughly 17–20% of online orders coming back, compared to 8–10% in-store. At the same time, consumers are becoming more value-conscious, and online continues to grow. That makes one thing clear: Returns can no longer sit outside the inventory strategy. The faster a brand turns returned units back into sellable inventory, the more it protects working capital, margin and availability. But in many apparel businesses, returned units still: - sit in a separate queue - get processed too late - stay invisible in weekly demand planning - miss the full-price resale window A few practical shifts worth implementing: 1. Restock high-demand products fast If a core size comes back, every extra day in processing is a missed sell-through opportunity. 2. Create a 3-way routing rule Every unit should quickly be classified as: restock, resale or unsellable. 3. Track time-to-resell Return rate tells you volume. Time-to-resell tells you if you are protecting margin. 4. Include returns in weekly inventory decisions Availability, allocation, and markdown reviews should include returns, not just warehouse and store stock.

One click return. Thirty percent chance it never sees another customer. That is the quiet punchline of post holiday ecommerce. A parcel goes out. Another parcel comes back. Margins disappear somewhere between the warehouse scan and the landfill gate. Here is the part nobody likes to say out loud. Returns are no longer a customer service topic. They are a structural cost problem with an environmental side effect that is getting expensive to ignore. A quick reality check before scrolling on. 📦 Around one in six online orders now comes back after the holidays 🗑️ Roughly a third of returned items never get resold because processing costs beat resale value 💸 A single return can eat up to sixty percent of an item’s cost once labor and shipping are counted Pause for a second. Free returns were sold as a growth lever. They quietly became a margin tax. A sideways observation that keeps popping up across marketplaces. The same sellers who obsess over CPCs often have no idea what their average return actually costs per SKU. Short story from the floor. An apparel item gets returned in January. The size is fine. The product is fine. The season is not. By the time it clears inspection, the discount hammer is already out. That is not bad luck. That is system design. Here is where the operator lens kicks in. 🧠 Fix product pages like revenue depends on it because it does 📏 Kill size guesswork with better charts and real photos 🔁 Push exchanges and instant swaps instead of refunds 🏬 Local drop offs beat long haul shipping every single time 🧾 Price returns into the model instead of pretending they are free Single sentence truth bomb. If the return is free, the margin is not. Zooming out. Marketplaces are watching closely. Expect tighter return rules, smarter fraud filters, and more nudges toward paid or conditional returns. Not because platforms got mean. Because the math stopped working. Final signal. The next advantage will not come from faster shipping. It will come from fewer boxes coming back. #ecommerce #marketplaces #returns #logistics #dtc

Over 60% of returned fashion inventory in India still never gets reused efficiently and that inefficiency is now turning into the biggest AI opportunity in retail. And it is already starting to reshape how fashion brands operate. Currently India’s circular fashion space is growing at 18-22% CAGR but the real constraint is not demand. It is decision making around inventory that is sitting in return loops, unsold stock and fragmented resale channels. Because today, most of that inventory is still handled like this..manually inspected, manually priced and manually relisted or liquidated Which means value is lost long before it re-enters the market. Now look at how some brands are starting to shift the system: 1) Snitch has started moving toward resale integration through partnerships like Relove, bringing used inventory back into its ecosystem instead of pushing it out of the value chain 2) Virgio is building a circular first fashion model where resale and buy back are part of the product lifecycle itself 3) Neeman's is pushing sustainable production with focus on material lifecycle thinking rather than one time consumption 4) Platforms like MyThriftKart, Palwand and Parichay are building early resale and thrift aggregation layers that extend product lifespan But here is the gap that still exists. None of this is fully scalable yet without intelligence layered on top. Because circular fashion breaks on three problems: 📌Matching problem: Which returned product goes to which buyer, at what time and through which channel 📌Pricing problem: How do you price used inventory dynamically instead of discounting it blindly 📌Condition problem: How do you evaluate usability of returns without manual inspection at scale This is where AI quietly changes the system design. >Computer vision models can assess garment condition instantly from images >Demand prediction models can estimate resale velocity based on category and trend cycles >Dynamic pricing models can adjust resale value instead of relying on flat discounting And my view is simple. Most brands are still treating resale as a separate channel. But the real opportunity is to treat it as an intelligence problem inside the core supply chain Because once AI sits in the loop: 1. returns stop being waste 2. unsold stock stops being loss 3. resale becomes continuous revenue 4. inventory becomes self correcting And that changes the definition of retail itself. So let’s talk if you want to know how you can implement these for your Retail brand.

Perhaps no problem is more migraine-inducing for the retail industry than returns. An estimated 15.8% of retailers’ sales valued at $849.9 billion were expected to be returned in 2025, according to a report from the National Retail Federation and Happy Returns, a UPS Company. Online sales boomerang more, with 19.3% returned. Chances are those returns are not fetching full price the second go-round. Only 47% of returned items are then sold for full price, according to a 2023 survey of US and UK apparel retail executives by SML-RFID. Another 42% are sold at a reduced price, with an average markdown of about 38%. As for returns that are damaged or with damaged packaging, or whose seasonal window has closed, they’re often thrown on a pallet and sold at drastic discounts. A recent examination by Retail Brew of B-Stock, an online marketplace where retailers and brands sell returned and excess merchandise in bulk, found many returns are drawing bids for a fraction of their original prices. - An auction by Walmart for five pallets of primarily apparel customer returns with a total MSRP of $15,094 had a high bid of $625 (4.1% of MSRP) with about five hours left. - An auction by Target for two pallets of customer returns of women’s apparel with a total MSRP of $27,588 had a high bid of $575 (2.1% of MSRP) with about four and a half hours left. But these days, some retailers are finding a way to recover more value from their less-than-pristine returns. With the rapid growth of the resale industry, some brands are beginning to route returned merchandise into their own resale channels. At Archive, a resale as a service (RaaS) company that partners with retailers including lululemon and The North Face on their recommerce programs, about half of its retail partners are selling damaged returns on their resale sites, according to Ryan Rowe, Archive’s co-founder and chief technology officer. Rowe told Retail Brew that his company’s partners typically fetch 50%–60% of MSRP on their recommerce sites, compared to 10%–20% in the liquidation channel. “The amount that you’re going to get when you literally just throw it on a pallet and you sell it for pennies on the dollar, versus if you actually do the work to sell it to your customer—it’s night and day,” Rowe said. At Treet, another RaaS company for brands including Tecovas and Ministry of Supply, Co-founder and CEO Jake Disraeli estimated brands fetch from 45% to 65% of MSRP on average on returns they sell through their resale sites. About 25% of Treet’s brand partners are including returns on their resale sites. “Historically, the goal of brands with discounted inventory and B-grade inventory is to break even,” Disraeli said. But through their resale channels, “the core thing is they can actually earn revenue.” >Click for full story, including how returns are a hook for customer acquisition, plus Trove CEO Terry Boyle. #resale #returns #reverselogistics #retail

For a decade, retail’s holy grail was identity. Know your customer. Build the profile. Personalize 1:1. Collect the email, the history, the loyalty data — and the predictions will follow. A Danish fashion group just showed that the most powerful signal isn’t the customer at all. It’s the basket. Read that again. Not who is buying. What is being bought. Here’s the story. BESTSELLER — behind Jack & Jones, Vero Moda, Only and Vila — was fighting fashion’s most stubborn margin-killer: returns. Every returned item is shipped, processed and restocked at a loss, and Europe returns a lot. Most brands fight this at the checkout — try-on, size guides, fit tools. Bestseller asked a different question: what if some “sales” aren’t really sales at all? Picture it. Peter buys three identical pairs of jeans — sizes 30, 31, 32 — on a Monday night. To the tracking pixel, a €150 win. To anyone in fashion, a red flag: two of those pairs are already heading back. So they built a model on Vertex AI that predicts the return at the moment of purchase. It doesn’t change the price Peter pays. It corrects the value reported to the ad engine in real time — telling Google this is really a €50 order, not €150, so the ad budget stops chasing shoppers who buy big and send most of it back. ROAS up 50%. Cost-per-click down 24%. They didn’t sell more. They stopped paying to acquire returns. But here is the part that should stop every retail leader cold. When they searched for what actually predicted a return, it wasn’t the email address. It wasn’t the loyalty profile. It wasn’t identity at all. It was the DNA of the basket itself — its composition, the payment method, the order value. Those signals predicted returns with 93% accuracy, on every order, including first-time anonymous shoppers the brand knew nothing about. Sit with that. The industry spent ten years and untold budgets insisting the answer was knowing the customer more deeply. This says the opposite: in a post-cookie world, what someone is doing right now can matter more than everything you’ve ever stored about who they are. Behavior over identity. Context over profile. And as a bonus — more privacy, not less, because you no longer need to know the person to read the pattern. The brands still racing to collect more personal data may be optimizing the wrong thing entirely. What if the signal you need was never about who your customer is — but about what they’re doing this very second? Source: Google Cloud / Bestseller case study (Vertex AI, BigQuery) — April 2026 #RetailTech #FashionRetail #ArtificialIntelligence #AI #DataStrategy #Personalization #Privacy #Ecommerce #Returns #RetailStrategy #Bestseller #FutureOfRetail #CompetitiveAdvantage #RetailLeadership

Survey Shows In-Store Returns Are Exposing Major Retail Blind Spots Retailers are discovering that the biggest return risks are not always fraud. Much of the loss comes from operational gaps across channels and inconsistent return policies. A new benchmarking report from Appriss Retail highlights how omnichannel returns are creating pressure on both margins and store operations. 🔸 What Changed → Retailers processed $706B in customer returns in 2025. → About 14.2% of those returns — roughly $100B — are considered preventable loss, tied to fraud and abusive return behavior. → In-store returns account for 81% of all product returns across retail. → Omnichannel behavior is adding complexity. 29% of returns are items purchased online and returned in store, often referred to as BORIS returns. → Returns are also expensive to process. Retailers lose roughly 30% of an item’s value once it goes through the return process. 🔸 Why It Matters Returns are quietly becoming one of the largest margin drains in retail. The issue extends beyond fraud. Operational inefficiencies, inconsistent policies, and omnichannel behavior create significant cost exposure. In-store returns place the largest burden on retailers. Stores often process items that do not belong to that location—or even that sales channel—creating additional labor, logistics costs, and inventory distortion. The growth of BORIS returns adds further strain because many retailers still operate with disconnected systems. When online and in-store return data is not unified, fraud and abusive behavior can move across channels undetected. Retailers that unify return data across ecommerce, store, and customer service systems are better positioned to identify patterns, prevent abuse, and protect margin while maintaining a strong customer experience. 🔸 What Is Not Changing Convenient return policies remain a major driver of purchase decisions. Many shoppers choose retailers based on how easy returns are. Retailers must continue balancing customer experience with margin protection, which requires smarter return policies rather than blanket restrictions.