Fix the product before you fix the bidding

Fashion returns are eating ad budgets alive. The fashionable answer is to get clever with bidding and audiences. The right answer is usually more boring than that.

Fashion e-commerce has a returns problem it would rather not look at directly. In 2020, US shoppers sent back an estimated $428 billion of goods, around 10.6% of everything they bought, with clothing leading the way. For online fashion specifically, return rates hover around 25%. One order in four goes back.

That's not a logistics footnote. Every returned dress was paid for twice: once by the ad budget that won the sale, and again by the reverse logistics that unwound it. McKinsey puts the average cost of processing a single return at about two thirds of the item's price once you count shipping, handling and restocking, and that's before the stock misses its full-price window and gets marked down. Little wonder 83% of retailers describe returns as a serious threat to profitability.

Faced with numbers like that, the tempting move for a marketing team is to optimise around the problem. Tag high-return products in the feed and bid down. Exclude serial returners from prospecting. Feed refund data back into the platforms. All of these are good ideas, and I'll come back to them, because done properly they work. But there's a catch that gets skipped in most of the how-to content: if the returns are being caused by operational problems, such as inconsistent sizing, misleading photography or patchy quality control, then adjusting your marketing is a bandage on a leaky pipe. The water keeps coming. You've just stopped watching that bit of the floor.

When marketing and operations pull in opposite directions

Here's the trap in miniature. A particular dress has a 40% return rate. Marketing spots it, quite reasonably concludes the thing is unprofitable to advertise, and cuts spend. Sales fall, returns fall with them, blended ROAS ticks up, and everyone moves on.

Nobody asked why 40% of buyers were sending it back. Maybe the sizing was mis-marked. Maybe the studio lighting made a dull maroon look bright red. Whatever the cause, it hasn't gone anywhere. Customers still arriving through organic search or email buy the dress, get the same unpleasant surprise and return it, except now there's less revenue around to absorb the cost. Marketing saved some return processing and threw away the legitimate sales that would have stuck if the product page had told the truth. Two departments cancelled each other out.

This happens because, in most businesses, returns don't belong to anyone. McKinsey found that 58% of retailers admit no single team owns the problem, even though preventing returns and recovering value from them cuts across merchandising, e-commerce, marketing and finance. Marketing optimises the metrics it can see, which are gross ones. The product team never learns how much a sizing inconsistency is costing in wasted ad spend, because that cost lands in someone else's report. Most retailers can't even break returns down by root cause at product level, and as McKinsey rather drily notes, causes you can't see are causes you can't fix.

So before touching the bidding, plug the bucket.

Why things actually come back

The reasons customers return clothes are well documented and mostly mundane. In one survey of apparel retailers, size and fit accounted for 53% of returns, colour or appearance not matching expectations for 16%, damage or defects 10%, price disputes 9%, the feel of the material 7%, and slow delivery 5%. Other studies put fit-related returns anywhere between 53% and 70%. Nearly all of it reduces to the same thing: the product that arrived wasn't the product the customer thought they'd ordered.

Sizing is the big one, and it runs deeper than "brands vary". One brand's Medium is another's Small; fine, everyone knows that. Less forgivable is variation within a single brand, where two "Medium" shirts from the same retailer fit differently because they came off different cuts or out of different factories. A BBC investigation found that H&M trousers in black and beige, nominally the same size, fitted very differently, probably because darker fabrics go through extra treatment that changes their stretch. Layer vanity sizing on top (a 38-inch chest is a Small here and a Medium there) and customers respond rationally: they order two sizes and send one back. Bracketing isn't customer misbehaviour. It's a workaround for an industry that can't standardise itself.

Photography is next. If the shade of the dress on the site doesn't match the one in the parcel, whether through studio lighting, screen calibration or a dye batch that drifted, that's a return. A newer version of the same problem is AI-generated model imagery. Dressing a virtual model costs a fraction of a photoshoot, but generators still struggle with fabric texture and drape, so a stiff blouse can look fluid on the page and disappoint in person. Plenty of retailers also still sell garments off a single flat shot on a hanger, which tells the customer almost nothing about length, movement or fit.

Feel and quality round it out. A customer can't touch the fabric, so they infer weight, softness and stretch from photos and adjectives, and when a jumper that looked thick and cosy turns up thin and scratchy, back it goes. Multi-factory production quietly makes this worse: the "same" shirt cut from different fabric lots can fit and feel like two different products, and the customer experiences it as a quality lottery. Charging them a return fee when the product was at fault is a particularly efficient way to lose them for good.

None of this is exotic, which is rather the point. The majority of fashion returns trace back to fixable gaps in sizing consistency, content accuracy and quality control, not to fickle customers.

What fixing it at the source looks like

The fixes are unglamorous. Standardise your size specifications and hold factories to them. Publish actual garment measurements, not just S/M/L, so people can compare against something they own. Photograph products on real bodies, check colour against the physical item under neutral light, and if you use AI imagery, review it for honesty and keep at least one real photograph in the set. Write fabric composition, weight and stretch into the description. If a style runs short or a colour runs snug, say so on the page; better to lose the sale than win the sale and the return. Video helps more than almost anything, because it shows true colour in moving light and how the fabric actually behaves. One study found shoppers 64% more likely to buy after watching a product video, so this isn't even a trade-off between conversion and returns. It improves both.

The more interesting development is retailers pointing machine learning at the problem, and the results are worth a pause.

H&M, whose online returns were dominated by fit issues, built AI-powered virtual fitting rooms. Customers enter their measurements or scan themselves, and a 3D model shows how a garment will sit on their body, accounting for cut, fit type and fabric stretch. Early rollouts showed meaningful reductions in returns, with lower reverse-logistics costs and a sustainability story thrown in. Better still, the try-on data flows backwards into the business: if a particular cut fits badly across thousands of avatars, the design team hears about it before the returns arrive.

Playful Promises, a UK lingerie brand, went a similar route with Prime AI's fit prediction after ordinary size charts proved useless for bras, which is about the least forgiving fit category there is. The system learns from real purchase and return data, factors in each garment's cut, material and even colour (dye processes can make a black lace bra fit more snugly than the same style in nude), and recommends a size for that specific item rather than assuming your usual size travels. Returns fell 27%, conversion rose 18%, and sizing queries to customer service dropped. The model keeps improving as more outcomes feed in, which is exactly the shape you want: a system that learns from past returns to prevent future ones.

Zalando runs a lighter-touch version, mining customer feedback at scale to put "size flags" on product pages ("Runs small, consider sizing up"), reportedly cutting size-related returns by around 10%.

Notice the shape of these wins. Nobody reduced returns by suppressing demand. They reduced returns by helping people buy the right thing, and sales rose as a side effect. Industry surveys suggest 85% of apparel retailers are now using or planning virtual fitting tools, and 80% of those running a size recommender report higher conversion. Compare that with the alternative timeline where H&M just quietly bids down on customers who return bras. Returns fall because sales fall. The sizing stays inconsistent, the customers stay annoyed, and the "optimisation" is a slow leak dressed up as efficiency.

Get the data before you get clever

You can't fix what you can't diagnose, and most return data is under-used at best. Reason codes at the returns portal are the start: too small, too large, looks different from the photo, faulty, changed my mind, arrived too late. On their own they're ambiguous. "Too small" might mean the garment runs small, or that the customer guessed wrong. The trick is triangulation: if 80% of a dress's returns say "too small" and a decent chunk of those customers reordered the next size up and kept it, that's a sizing problem, not a customer problem, and the size guide needs changing.

Free-text comments are worth collecting too ("returning because the colour is much greener than on the site"), and worth mining, along with reviews and social mentions. Language models have made it cheap to comb unstructured feedback for themes at a scale no merchandising team could read manually.

Then slice return rates by attribute. By factory, by supplier, by fabric, by collection, by photography style. You'll find patterns you didn't expect, like every product shot on a mannequin rather than a model returning above average, and each pattern is an instruction.

The other half of the diagnostic job is joining returns to marketing data, and this is where most companies quietly fail: the e-commerce team tracks return reasons, marketing tracks conversions, and nobody has merged the tables. Until they're merged, your ROAS is a work of fiction. A campaign that spent £1,000 to drive £5,000 looks like a 5x return; if 30% of that revenue came back, it actually drove £3,500 of kept revenue, and once you subtract the cost of processing those returns it may not have broken even. Two thirds of retailers say they have a strategy for improving the economics of returns, but without unit-level data joined across systems they can't see what anything actually costs. The order ID is the key that links a click to a sale to a return to a reason; if your data model can't make that join, that's project number one.

Track it over time as well. If the fixes are working, the reason codes should move: fit returns fall after you launch a size recommender, "not as described" falls after you add video. That feedback loop tells you you're fixing the right things, and it tells you when the preventable returns are mostly gone, which is the moment the remaining returns data becomes safe to hand to your ad platforms.

Now you can bring in the ad platforms

Once the operational floor is solid, returns data stops being a symptom you're papering over and becomes a genuinely useful signal. A few ways to use it.

Custom labels in Google Shopping. Product feeds allow five custom labels, and one of the better uses going is a returns tier: label products ReturnRate_High, Medium or Low from trailing return data, refreshed automatically by a feed rule or script. Then structure campaigns so the tiers carry different targets. High-return products are less profitable per conversion, so they need to earn more per sale: put them behind a higher ROAS target, or in a tightly budgeted campaign of their own, and let low-return products run freer. Concretely: dress A returns at 5%, dress B at 40%. Left alone, Smart Bidding spends happily on both, because it can't see that B's conversions keep un-converting. Tier them, demand say 30% more ROAS from the high-return group, and budget migrates towards revenue that stays. Feed specialists have been arguing for years that custom labels should carry operational data like margin and returns rather than just "summer sale", and one UK agency reports that labelling high-return items and bidding accordingly directly improved marketing ROI. Two cautions. This is a compensator for categories with inherently high returns (occasionwear that gets worn once and sent back), not life support for products you should be fixing. And don't leave a product in the naughty tier after the fix has landed.

Value-based bidding on net revenue. More powerful, and more work: tell the platforms what a sale was actually worth after returns. Google supports conversion adjustments, so when an order is refunded you retract the conversion or restate its value, keyed on the order ID, and Smart Bidding gradually learns which contexts produce revenue that sticks. Meta's value optimisation works on the same principle: feed accurate values through the pixel and Conversions API and delivery chases predicted value rather than raw purchase counts. You never tell the algorithm "people who search 'red dress' bracket like mad"; you feed it net values, and it works out on its own that specific-product searches keep their orders while broad-query browsers don't. The catch is plumbing. Closed-loop refund feeding needs your returns system talking reliably to your ad accounts, and few brands have built it, which is exactly why it's an edge for the ones that do.

Audiences. If you can identify serial returners in your customer data, and I mean the buy-everything-return-everything pattern rather than your best customers, who return plenty because they buy plenty, exclude them from prospecting via Customer Match or a custom audience. Point lookalikes at the opposite group: multi-order customers with low return rates, so the platform hunts for people who resemble keepers. And treat a return as a marketing trigger rather than a dead end. Someone who just sent back ill-fitting shoes doesn't want a generic "come back soon" ad, but a similar style with a sizing nudge and free exchange delivery might turn the return into an exchange instead of a lost customer.

The common thread is that you're teaching the machines what a good sale looks like. Do that on top of a broken product experience and you've automated the doom loop from earlier. Do it after the fixes and every pound of spend starts flowing towards revenue that survives the returns window.

Where this leaves you

The order of operations is the whole argument. Fix sizing, photography, product content and quality control, using machine learning where it genuinely helps, because that removes returns while growing sales. Build the data spine that joins ad spend to orders to returns to reasons, because that makes the problem visible. Then, and only then, get clever with labels, adjusted values and audiences, because at that point you're fine-tuning a machine that works rather than compensating for one that doesn't.

H&M's chief executive put the goal simply: whatever customers buy, they should want to keep. That's a sentence that aligns operations and marketing better than any org chart. When it's true, the next marketing pound goes towards kept revenue rather than an initial sale, and that, more than any bidding trick, is what makes the ad spend count.