We use Google Analytics 4 to see which pages are useful. It loads only if you accept it, and then sets two cookies, _ga and _ga_C372PEK7KT. Nothing is stored until you choose, and nothing it records is used for advertising or sold.
Sizing fails because fit data cannot travel. Not because charts disagree.
TL;DR — Fashion returns are approximately 30% of all online clothing sales, and the dominant cause is fit failure. The standard diagnosis blames inconsistent size charts. The structural diagnosis is different: fit knowledge is fragmented across operators, so every new brand, tailor, or platform starts with zero context about the person. Standardisation helps an anonymous transaction. It does not help the wearer who has already been measured dozens of times.
Fashion keeps diagnosing sizing as a chart problem. A 38 in one system becomes a 40 somewhere else; a medium at one brand fits like a large at another. That diagnosis is familiar and partly true. But it is not the deepest one. The root cause is not that numbers disagree. It is that the knowledge built up during every fitting, alteration, and return is discarded the moment a transaction ends.
Sizing standards have existed since the 1950s. ISO 8559-1 defines global garment construction norms. The EU has pushed harmonised sizing tables since the 1990s. Yet online fashion returns still run at roughly 30% — with fit cited as the primary driver in most major studies. The reason standardisation has made so little difference is that it solves the wrong problem.
A universal size chart improves the starting assumption for an anonymous transaction. It gives a new customer a slightly better prior before she clicks 'add to cart'. What it does not do is carry forward anything that was already learned. It does not remember that a given person needs more room through the shoulder, prefers a specific trouser break, or consistently alters ready-to-wear at the hem. A better chart is still a blank slate.
Gustafsson, Jonsson and Holmström, working from 2,229 return transactions at a Scandinavian footwear retailer, found that a well-calibrated digital fitting tool can cut fit-related return costs by up to 80%. The conditional is the whole finding: the gain depends on calibration against the individual body, and in their trial the tool could not verify fit on every order. The reduction belongs to the data behind the interface, not to the interface.
Standardisation addresses one variable — the relationship between a label and a body measurement range. There are at least three structural gaps it cannot touch. Each one means that fit knowledge resets, and the wearer pays the cost of that reset in the form of a bad purchase or a return.
Definition
Measurement Portability
The right and technical capability of a wearer to carry their fit data — including body measurements, garment outcomes, alteration history, and preference signals — from one operator to another without re-entry, context loss, or vendor lock-in. Analogous to medical record portability under GDPR, but applied to the garment layer.
The economics of fit failure are large enough to restructure supply chains. The IJPDLM study puts the cost of a single return at roughly 17% of the item's prime cost, with handling and transport making up 72% of that. Applied across an online apparel sector returning around a third of what it ships, and with fit the most-cited reason, the figure is structural rather than incidental. It is not born by one bad chart. It accumulates through millions of repeated cold starts — each one a transaction where no prior fit knowledge was available.
Put that on a single brand's P&L. Take 100,000 online orders a year, a 25% return rate, and 60% of those returns fit-related: 15,000 avoidable returns. At the 17% of prime cost the study measured, a €100 item carries about €17 of return cost, so the annual bill is on the order of €255,000 — before any markdown on stock that comes back unsellable. Change the average selling price and the number moves with it; the shape does not. That is not a chart problem. It is an infrastructure problem.
The goal is not to make every brand use the same numbers. The goal is to let what is already known about a person remain usable when the commercial context changes. — Caprice Bespoke, fit infrastructure design principle
A portable fit-data layer must do something fundamentally different from a size chart. It must persist across commercial relationships, be controlled by the wearer rather than any single operator, and degrade gracefully when partial data is all that is available. The architecture has to solve four problems simultaneously.
The GS1 Digital Link standard and the EU's Ecodesign for Sustainable Products Regulation (2024/1781) are both moving in this direction at the product level — creating persistent, machine-readable identifiers for garments. The next step is the same capability at the wearer level: a persistent, portable, wearer-controlled record that travels across every commercial relationship.
The EU Ecodesign for Sustainable Products Regulation, which entered into force in 2024, brings Digital Product Passports to textiles through delegated acts expected from 2027. That requirement creates a machine-readable garment record at scale for the first time. It is primarily a sustainability instrument — tracking material composition, repairability, and end-of-life data. But the infrastructure it creates is directly reusable for fit purposes.
When every garment has a persistent GS1 Digital Link identifier encoding its construction measurements, a portable fit record can link to actual garment data rather than relying on size labels. A wearer who knows that EU garment DPP reference 4902-xxxx fits correctly in the shoulders but not through the waist has created a fit outcome record that any operator — tailor, brand, platform — could read if given permission. The regulatory push toward product-side data portability makes the parallel wearer-side infrastructure significantly easier to build.
Sizing failure is an infrastructure problem with three layers. The first is the chart layer — label inconsistency between brands. Standardisation partially addresses this. The second is the session layer — fit knowledge collected during one transaction that is not retained for the next. No current standard addresses this. The third is the portability layer — the technical and legal mechanism by which wearer-controlled fit data moves between operators. This layer does not yet exist at scale in fashion.
The solution therefore has to reach below sizing language and into data architecture. Fit improves when continuity compounds: when each fitting, alteration, and return enriches a persistent record that the wearer can bring to the next relationship. It does not improve when every operator maintains a slightly better isolated chart. The compounding effect of accumulated fit knowledge — measured in reduced cold-starts, fewer misfits, and lower return rates — is the economic case for building the portability layer.
Size chart variation exists because fashion brands developed their fits independently, often targeting different body proportions for their core demographic. An Italian luxury brand patterns for a narrower shoulder than a Scandinavian outdoor brand. Chart variation is real, but it is only the surface symptom. The deeper issue is that even a perfectly standardised chart cannot carry forward the fit knowledge that has already been built up about a specific wearer.
Virtual try-on reduces returns when it is built on accurate individual body data. The IJPDLM study of a Scandinavian footwear retailer found that a well-calibrated digital fitting tool could cut fit-related return costs by up to 80% — and also that it failed to verify fit on some orders. Those gains depend on the accuracy and recency of the body data behind them. When a customer must re-enter measurements every session, or the tool falls back on a generic avatar, the reduction is much smaller. The technology amplifies good data infrastructure; it does not replace it.
Measurement portability is the wearer's right and technical ability to carry their fit data — measurements, garment outcomes, alteration history — from one operator to another. It matters because most fit knowledge is currently locked inside individual brand or platform databases. When a wearer changes brand, that accumulated context is abandoned, and the prediction quality for the new brand resets to zero. Portability would allow that knowledge to compound rather than reset.
The EU's Ecodesign for Sustainable Products Regulation mandates Digital Product Passports for textiles by 2027. These create machine-readable records at the garment level, encoding construction dimensions in a standardised, portable format. While primarily a sustainability instrument, the same infrastructure can link garment-side construction data to wearer-side fit outcomes — making it possible to build a portable fit record that references actual garment data rather than label approximations.
Online apparel returns run at roughly 30% of sales volume, and fit is the most-cited reason — ahead of colour mismatch or product quality. On cost per return the most specific published figure comes from the IJPDLM study of a Scandinavian footwear retailer: about 17% of the item's prime cost, of which handling and transport are 72%. That is one retailer in one category, so treat it as an order of magnitude rather than an industry constant. What generalises is the structure: the cost is dominated by moving and re-handling the item, both of which are incurred before anyone knows whether the garment fit.
Sources
Related concepts