How Fashion Brands Can Reduce Fit-Related Returns

"Doesn't fit" is the most common message in return notes, yet it can mean a number of things for a customer. The phrase might describe issues such as the fit being wrong, proportions being off on their body, the garment's fabric restricting movement, the material being unflattering or skin-unfriendly, etc., and the customer might see it simply as a visual difference on themselves.

Fashion companies can make a real loss by treating all these as one and the same problem, although the remedy for each may be distinct, from changing the size chart to completely overhauling one of the patterns. Therefore, the solution to the problem lies in accurate and clean data about the main returning reasons, classified in such a way that you can take actionable measures. This article walks you through setting up such a system, getting the gist and the reason pattern behind your figures.

Build a fit-return taxonomy.

A single "doesn't fit" checkbox gives you just about no information. You should dissect what is happening and divide the cause among various causes that your team could address.

Categorize fit-related complaints into specific reasons:

• One size doesn't fit all situations
• Short vs. Long
• Fatigue in a particular region of your body like your collar, shoulders, hips, or breasts
• Fabric features, which include properties like flexibility, shrink back, etc., and the way material shifts
• Lack of opacity / Transparency
• Level of support, especially from clothes meant for a structured/fitted look
• Picking a fit that doesn't reflect the customer's personal taste or style preferences.

Distinguish fit reasons from those 3 categories that frequently blend in with the mix: defect returns that refer to a genuine problem with the product; preference returns that indicate customer changed his/her mind; fulfillment errors that involve dispatch of the wrong product or item. Confusing and lumping up all 4 groups into a single figure gives an illusion of an average rate, whereas the real issue for each return will still be concealed.

Find the pattern at SKU and variant level.

After analyzing the returns and exchange percentages across various factors, you will be able to figure out where problems are really coming from. For example;

• Size: one special size always being the case may indicate it is going off-size.
• A change of look may occur depending on color.
• Quality control issues are more likely detected through supplier comparisons.
• Different types of stretch and recovery of various fabric types also lead to fit-related complaints.
• Facing a different set of customers may lead to a different return rate, as your online customers may have different expectations from a marketplace buyer.

A well-set return reason system only reveals limited aspects. So combine the information with the review comments of real customers about the actual products and their complaints via customer service channels. A particular size which has been labeled as "too small" will have the description as "tight on one sleeve at the shoulder," so it may be pointed out as a pattern fix as well as ​‍​‌‍​‍‌a chart.

Improve the product truth layer.

The main fitting issues occur due to a contrast between the description on the web page and what actually arrives in the box. Often, such discrepancies make up a single major lever for changing a brand's direction, whereas such changes are usually far heavier than minor changes, such as changing a pattern or even changing your supplier.

Give customers real measurements, not just a size label

You should provide real, garment measurements (e.g., the chest, waist & seat measurements for men) alongside the basic sizes (S, M, L). Add details about the height & weight of the model and the clothes the model wears in the photographs so that the customer gets an idea of how he can look if he is a similar body shape.

Make sure that the product description is rich in details, like how much the fabric will stretch, if the fabric is thick or thin, the height of the crotch, the inseam or hem lengths.

State fit intent directly

Inform the customer directly about the fit (whether the item will go straight to size, run oversized, etc.) on the product page. An educated buyer, who has been told that the piece runs larger, will most probably refrain from making a return due to it looking too big to him.

Keep claims consistent everywhere.

Make sure that the fit descriptions in your product listings, ads, and marketplace posts are the same. Describing a product as a "loosely-fitted design" in an ad but "true-to-size" on a product page is misleading the customer at the least, as there's a disconnect between the two pieces of information one will receive. So one of them will eventually get a customer's dissatisfaction.

Use styling context to reduce expectation gaps.

In some cases, customers may express dissatisfaction with fit not because the product was wrong but simply due to the expectation not being met.

Closing that mismatch is what styling content does best

• Come in sizes and shapes different from your style
• Match of shoes, heels affect the length of how pants or dresses hang on the body.
• Layer options showing either the garment alone or with a second layer
• Use context of an event to let the client visualize the garment against their scenario

In reality, the difference between one fit and another can only be closed with the right kind of content. But if the product is really small on the shoulders or the fabric pilling is a real issue, then such an approach is not even the first thing on the fix list. Internally, it is important to keep the difference clear between a physical problem and a visual fit problem.

Feed insights back to buying and design.

Only when such information is actually leading to changes in the decisions upstream will the whole process have a positive effect, even if the return hasn't happened yet.

Taking into account verified fit issues in your work:

• Change the size curves where certain garments consistently have a different size trend
• Brief your suppliers with updated measurements and ranges of acceptable deviations
• Rework the cutting patterns on those body parts that are mentioned many times in reviews
• Change your fabric selection if the material is repeatedly causing issues over stretch, elasticity, recovery, etc.
• Adjust the stock line, removing the styles that are leading to excessive returns and can not be handled by margins

While trend data tells you what the coming direction will be, through which you design, fit data makes sure that whatever is being made is of the right quality. This way, a style coming from a trend might not become a high-return liability because of a mismatch between design and fit when the product ​‍​‌‍​‍‌arrives.

Where Garmio Contributes

Garmio is a tool especially designed to support your product development and design team with insights and analysis from language found in customer feedback, which is a key element of fit problems.

Through our platform, you will be able to identify recurring words and language used in customer complaints. Once you detect, for instance, that people are complaining about the shoulder or sleeve of the garment, you are aware of the problem and can act quickly instead of spending hours manually reading customer comments. On the other hand, competitor & visual research show how other brands present measurements, model context, and fit intent, giving your team reference points for clearer product pages.

Besides, AI image edit tools can generate style and silhouette variations fast opening up variations worth testing, still fit of the garment requires confirmation by a qualified person on pattern, grading, and construction before anything is sent to the production stage. Research can help to raise the first issues quickly and a fit expert finalizes them.

Analyze customer language around your next assortment in Garmio, and catch fit complaints before they turn into a costly return pattern.

Conclusion

Prioritize revenue over orders. A high number of orders but with a high percentage of returns indicates different issues than a total sales number alone. Design a clear fitting-return system, analyze the SKU-level to better understand your data, close the gaps in your product description, create fitting content only at the places where it will be the most helpful, and send all the knowledge back into the buying and design process. Every time you implement a process, you protect your revenue in a little amount.

Generally, the work of the fitting-return cycle rarely ends with one round. New providers, new kinds of textile, and new silhouettes all bring with them a new potential risk of a fit problem. Develop the tradition of inspecting return language every time a new style of clothing is introduced. Not only is it worth monitoring return rates on a report, but also the return language.

Start identifying customer feedback on your next collection in Garmio today. Go to garmio.ai and transform customer complaints about fit into fewer returns and more orders that will actually be ​‍​‌‍​‍kept.

FAQ

Why are clothing return rates high?

Return rates for clothes are high due to various reasons. Customers may encounter variations in sizing for different styles, issues with fit or comfort, differences between expectations and actual products, quality problems, changes in personal preference, and errors in order fulfillment.

What is a fit-related return?

A fit-related return refers to clothing that does not conform to the customer's required or expected dimensions, proportions, ease of movement, or comfort, except for defects, preference changes, or fulfillment errors.

How can product pages reduce fit returns?

Product pages can minimize fit returns by providing accurate measurements, clearly explaining the fit intent or behavior of fabric, contextualizing with models, presenting consistent images, and including helpful reviews.

Can styling recommendations reduce returns?

Styling recommendations can help set expectations and build confidence in outfits. However, they cannot fix sizing inaccuracies or design flaws.

What data should fashion brands analyze?

Fashion brands should analyze return and exchange data by SKU and variant, broken down by reasons, review language, service notes, supplier, fabric, channel, and customer cohorts.

How does Garmio help with fit-return research?

Garmio identifies patterns in reviews and shows how competitors present products. This enables faster answers in product content, buying decisions, and redesign questions.

Can trend analytics predict return rates?

Trend analytics alone cannot predict return rates. Return outcomes depend on first-party product data, customer feedback, and operational details. External trend context adds useful information, but not directly answer.

Related Reading