Fashion e-commerce 2026: how fashion brands increase their profit

The European fashion market continues to grow, but not fast enough to mask operational weaknesses. Brands and retailers can no longer rely on a growing overall market to pull their sales along. Those who want to grow faster than the market must gain market share, reduce purchase uncertainty and get more out of every customer.

In fashion e-commerce, what counts is not how many orders a campaign triggers, but how many items stay with the customer after the return period and what contribution margin they bring. The most important unit for profitable growth is the kept item. In this article, we show how to align product data, content, campaigns and returns with it together.

We draw on a current Google report and separate findings from recommendations

The basis of this analysis is the Google report “Europe State of Fashion ’26” from July 2026. It is based on a survey of fashion shoppers in the UK, Germany, France, Italy and Spain and supplements it with data from other market researchers and consultancies. Not all results therefore refer to the same countries, target groups or time periods.

The recommendations for action are our own assessment. Sample calculations are labeled as such and are not additional market data. We treat forecasts in the report, for example on the future online share or the share of agentic commerce, as a direction, not as a fixed figure.

A slowly growing market requires growth through market share

The fashion market in Europe will grow only moderately through 2030, with apparel as the largest segment growing more slowly than footwear and accessories. This does not mean that footwear or accessories are more profitable for every retailer. The report contains no data proving a higher margin for these categories.

Google describes the situation as a “zero-sum market.” Literally, that is not true, because the market continues to grow. From the perspective of an individual retailer, however, it still applies: with low market growth, additional revenue comes mainly from better visibility, higher conversion rates, fewer returns, more repeat purchases, new target groups or regions and clearer differentiation.

Therefore, evaluate assortments beyond revenue and growth by at least five measures:

1. realized contribution margin,
2. return rate,
3. days of inventory and availability,
4. repeat purchase and cross-selling potential,
5. competitive and price pressure.

A fast-growing segment can be unattractive if the products have high returns, short life cycles or extreme price competition. A niche growing only moderately can be very profitable if the brand is clearly positioned there.

Online is growing, the purchase decision remains cross-channel

Fashion revenue continues to shift to online retail, but the larger share is likely to still be generated in stores in 2030. The customer journey is a research loop: shoppers search online, look at items in the store, compare prices, read reviews and check what a similar item costs on low-cost platforms. Many use several of these touchpoints.

Those who view individual channels in isolation therefore make wrong decisions. A campaign may have prepared a purchase without receiving the last click. A store may have removed the uncertainty even though the revenue is booked online.

Retailers with stores should present their online and offline offering consistently: local availability, in-store pickup and returns, delivery times, prices and promotions, variants and sizes.

Pure online retailers must replace the missing fitting with better digital advice: clear size and fit information, detail shots of the material, videos in motion, the model's measurements with the size worn, reviews with notes on fit, and clear delivery and return conditions. The product page must remove so much uncertainty that the customer chooses the right size and keeps the item.

Fashion is discovered by people and machines at the same time

The report describes a “two-front journey”: brands must generate attention among people while also making their products understandable for search, shopping and AI systems. A strong video sparks interest. Whether the product is then correctly found, compared and recommended is decided by the data behind it.

Short videos spark interest, longer ones answer purchase questions

Most shoppers who follow fashion content on social networks do so on several platforms, predominantly in short videos. Short formats are suitable for new looks, product launches, trends and styling ideas. Longer content answers the questions shortly before purchase: How does the product fit? How does the material move? Which body type does the cut suit? How can it be combined and cared for? YouTube also prompts purchases across all age groups. Whether it is the channel with the highest profit contribution for your target group is only shown by a test.

A good video is used multiple times

A short creator clip, for example, shows a summer dress in everyday life. From there, the user should get to a longer review or directly to the product page, where cut, material, size selection and availability are described in a comprehensible way. The same material belongs on the product page, in paid campaigns, in shopping environments, in email sequences and as an answer to questions about size and material.

AI becomes a shopping advisor first, a buyer only later

The survey shows a clear limit: many shoppers can imagine using AI for product search, comparisons and recommendations. Only a few would let an AI place orders on its own. In the short term, what counts is therefore not fully automated shopping but AI as an advisor for preselection, comparison, size and style advice and complete outfits. Forecasts on the share of agentic commerce in retail by 2030 come from different sources and target groups and should be read with caution.

More important: even advisory AI reduces large assortments to a few suitable options. This makes product data part of the sales infrastructure. For important products, the following should be available in structured form: unambiguous product name, material composition, cut and fit, size measurements, care instructions, color and pattern, occasion, season, variants, delivery time, availability, certifications, return conditions and verified reviews.

A weak product title:

Damen Kleid Sommer rot

A more meaningful variant:

Damen-Midikleid aus Leinenmix – Wickelschnitt – Kurzarm – Korallenrot – Größen 34 bis 44

Search systems understand the second variant better, and the customer recognizes more quickly whether the product fits. Statements in the report that structured data increases the probability of being selected many times over are based on a single vendor interview. Clean data increases the chance that a system understands an item correctly. It does not guarantee a recommendation or a particular position.

Trust increasingly arises outside the brand message

Only a minority of internet users has a preferred fashion brand. Brand content remains important but competes with reviews, creator content, product tests, comparisons and community recommendations. Shoppers most often name reviews from other users as the trigger for a purchase.

Good product communication therefore fulfills four tasks:

1. Spark interest: What does the product look like in everyday life, what occasion does it suit, how can it be combined?
2. Provide evidence: verified reviews, product tests, creator experiences, real customer photos.
3. Make alternatives comparable: tight or loose, light or heavy, formal or everyday, entry-level or premium.
4. Reduce uncertainty: measurements, fit, size recommendation, feel of the material, transparency, elasticity, care, delivery and returns.

With age, the weighting shifts: younger shoppers respond more strongly to creator styling, try-ons and trends; older shoppers pay more attention to quality, comfort, durability and credible advice from the brand. Reviews work across all age groups.

Therefore, don't choose creators by reach, but by fit with the target group, quality of product presentation, clear statements about size and fit, usage rights for website and advertising, and measurable influence on sales and new customers. Likes and views show attention, not additional contribution margin. Only experiments show that.

Customers stay because of reliability, not points

Loyalty has not disappeared, but it is re-examined with every order. Many shoppers concentrate a large part of their spending on a few brands. As reasons for returning, they mainly name consistent quality and fit, easy delivery and returns and good after-sales service. Loyalty points play a much smaller role.

This results in a clear order of priorities for customer loyalty:

1. Secure product quality and size consistency.
2. Make delivery information reliable.
3. Design returns to be understandable and efficient.
4. Speed up customer service.
5. Personalize repeat purchases based on previous purchases.
6. Only then use additional rewards.

A discount can move a customer to a second order. But it does not permanently make up for sizes that vary or returns that are complicated.

The report also shows a strong correlation between a retailer's perceived differentiation and its pricing power. A correlation is not yet a cause, but the direction is clear: interchangeable retailers are more likely to end up in price competition. Differentiation comes, for example, from reliable fits, a clearly defined target group, exclusive collections, curated assortments, traceable quality, better advice, repair and take-back offers or particularly good product information.

For many buyers, secondhand is part of the normal fashion budget

Buying used fashion has increased across all categories in recent years. For some shoppers, secondhand is a fixed part of the budget; for others, it is not an option. Those active on resale platforms often look there first before buying new, and consider resale value when buying new.

A product therefore no longer competes only with new goods from other retailers, but also with itself in used condition, with older collections and with alternatives that hold their value better.

A take-back or resale program is suitable for products with a long lifespan, recognizable brand value, stable quality, higher merchandise value and good resaleability, such as high-quality bags, shoes, outdoor apparel, children's clothing or limited collections. A pilot can look like this:

1. A clearly defined product group is taken back.
2. The condition is checked according to fixed criteria.
3. The customer receives store credit instead of cash.
4. Sellable products are refurbished.
5. Unsellable items are repaired, reused or recycled.
6. Repeat purchases, resale rate and total costs are measured.

The most important economic lever often lies not in the margin on the used goods, but in the credit triggering a new purchase. Whether the model pays off is only shown by your own costs for inspection, refurbishment and logistics.

Returns determine profitability in fashion e-commerce

Size and fit are the most important reason for returns, even more so for luxury brands than for general apparel. Size uncertainty also leads to abandoned purchases, and after a fit-related return many customers buy less often on the same platform again. Weak size advice therefore causes four problems: lower conversion rates, higher return costs, weaker customer loyalty and distorted marketing metrics.

A good platform ROAS can be deceptive

Advertising platforms initially measure the order value. If a large share of the goods is returned, the campaign is worth less than the original ROAS suggests.

Simplified sample calculation (illustrative, not market data): a fashion store achieves €100,000 in reported order revenue with a campaign at €20,000 in advertising costs. The displayed ROAS is 5.0.

The platform ROAS is the same in both scenarios. Nevertheless, the contribution margin in the second scenario is €6,500 higher. Fewer returns can therefore be worth more than additional traffic.

These metrics belong in reporting

In addition to orders and reported revenue, evaluate campaigns by:

• revenue after returns,
• returns-adjusted ROAS,
• contribution margin per order and per kept item,
• cost per kept item,
• return rate by product, size and acquisition channel,
• most common reason for return,
• repeat purchase rate after a return.

Where technically possible, we feed cancellations and returns back into the marketing analysis after the return period has expired. Until then, you can work with average return rates per category. Marketing and returns management can thus be connected through shared data, up to assessing the return risk of an order before shipping.

We steer products by margin and returns

A high return rate is no reason to switch off a product immediately. First it must be clear why it comes back: incorrect size information, misleading images, deviating color, unclear cut, insufficient material description, overly broad targeting or quality problems. If these causes cannot be fixed economically, the product gets less reach or leaves the assortment.

Visual search and virtual try-on must be measured by contribution margin

Some shoppers already use visual search, for example uploading an image to find similar products. Product images thus become a search signal. What matters is a clear main view, consistent cut-outs, multiple angles, detail shots of the material, realistic colors, visible fit, images in motion and on different body shapes.

Virtual try-on and visual size advice can reduce purchase uncertainty. The report does not prove a fixed reduction in returns. Test such features where the lever is greatest: on products with high size uncertainty, many fit-related returns, high revenue and long availability. Measure success by conversion rate, return rate, choice of the right size, repeat purchase rate and contribution margin after returns, not by use of the feature.

With a 90-day plan, you align your growth with profit

Days 1 to 30: create economic transparency

The first month is about a solid baseline, not new campaigns. Analyze revenue per SKU, realized margin, return rate and reasons, inventory, conversion rate, review coverage, quality of size information, image and video material and the repeat purchase rate. The result is a shared priority list for marketing, e-commerce, purchasing and logistics.

Goal after 30 days: For the most important 100 to 200 products, it is known what contribution margin they generate after returns, why they come back and whether they are scalable.

Days 31 to 60: reduce purchase uncertainty for the most important products

Focus on the items with the greatest economic potential: sharpen product titles, add material and fit information, improve size advice, evaluate reviews, create detail images and try-on videos. In parallel, campaign management differentiates by margin and return risk for the first time.

Goal after 60 days: For the most important 50 to 100 products, there are more complete product data, clear fit information and returns-adjusted performance measurement.

Days 61 to 90: launch controlled growth tests

Only now do you scale. Useful tests are revised versus previous product pages, new size advice versus the standard display, creator video versus classic product video, steering by gross revenue versus steering by revenue after returns, a take-back pilot in one product group and visual search or virtual try-on for items with high returns. Define success criteria for each test in advance, at least revenue after returns and contribution margin.

For Google Shopping and comparison shopping services, the focus shifts to profitability

For comparison shopping services (CSS) and those responsible for Shopping, the task is changing. Serving products and lowering click costs is no longer enough. The greatest profit contribution arises at the interface of product data, campaigns and profitability:

1. Check feed quality commercially: A feed can be technically error-free and still weak. A good audit also checks for missing selling points, imprecise titles and unclear fits.
2. Steer campaigns by economic product value: Products are grouped by contribution margin, return risk, days of inventory and fit reliability.
3. Make AI readiness concrete: unambiguous facts, structured attributes and traceable reviews so that systems can explain who an item is suitable for.
4. Close the data loop: The analysis connects ad contact, order, return, contribution margin and repeat purchase.

You can read how we implement this in Google Shopping and with our own comparison shopping service on the respective pages.

Conclusion: Profitable growth begins after the purchase

In 2026, the European fashion market is neither a crisis market nor a sure-fire success. It is growing slowly, products are becoming easier to compare, the customer journey is becoming more fragmented and the demands on product data are rising. Three systems must work together: attention through search, shopping, social media, creators and video, trust through reviews, tests, clear information and reliable service, and profitability, which only arises when products are kept and bought again.

The biggest change therefore lies in steering demand by its actual economic value. The most successful fashion retailers are especially reliable at ensuring that the right customer finds, keeps and buys the right product again.