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Industry

Fashion & Apparel

Fashion commerce is defined by two numbers most other categories never think about: how many variants a style generates, and what share of orders come back. Both are design decisions before they are logistics ones.

Two people in linen summerwear, one seated and one standing, against a sunlit plaster wall

Diagnosis first

What makes apparel harder than it looks

Four characteristics that break storefronts built for simpler categories.

  1. One style becomes ninety SKUs

    Colour times size times fit. Merchandising, imagery and stock logic all have to work at variant level without drowning the customer in choice.

  2. Returns are a third of orders, or more

    Mostly fit. Every percentage point is margin, which makes sizing guidance and accurate imagery a commercial lever rather than a nicety.

  3. The catalogue turns over every season

    New ranges, retired lines and thousands of URLs that must not become dead ends. Collection and redirect strategy matter more than in evergreen categories.

  4. Drops create traffic you cannot model

    A launch is a load test with revenue attached. Platforms behave differently under a spike than under steady traffic.

Variants are the architecture decision

Most apparel projects that go wrong do so because the variant model was decided casually. Colour as a separate product or a variant option, size runs that differ by colourway, fits that are really separate products — each choice determines what merchandising, filtering and stock logic can do for years afterwards.

It is worth spending real time on before the build, because changing it later means re-cutting the catalogue and every URL attached to it.

Returns are a storefront problem

The instinct is to treat returns as logistics. In apparel they are mostly a product page problem: the customer could not tell whether it would fit, so they ordered two sizes.

That reframes the work. Measurements, model information, consistent photography conventions and clear fit notes are conversion work with a direct margin effect, and they depend on product data being structured enough to display consistently across a changing range.

Focus areas

Where we focus in fashion

Each sub-sector carries its own variant, fit and returns profile.

  1. Apparel Deep size and colour runs, seasonal turnover and the returns rate that defines margin. Fit guidance is the commercial lever.
  2. Footwear Half sizes, width fittings and brand-specific sizing. Cross-brand size mapping reduces the double-order-and-return habit.
  3. Accessories and bags Lower returns, higher attach rate. Merchandising for add-on and gifting matters more than fit tooling.
  4. Activewear and sportswear Technical attributes, fabric performance and activity-led navigation rather than occasion-led.
  5. Luxury and premium Considered purchase, high order value, expectations of service and authenticity. Checkout and delivery experience carry the brand.
  6. Occasion and rental Date-bound availability, deposits and return logistics that a standard checkout does not model.

Platforms

Where we build.

Fit information is a margin decision

If a third of apparel orders return and most of that is fit, then size guidance, model measurements and honest imagery are not merchandising polish. They are the difference between a profitable order and one that costs you twice in shipping and once in handling. We would rather spend a budget there than on a homepage animation.

Talk through your range

Scope

What a build includes.

  • Variant architecture that scales

    Colour, size and fit modelled so that stock, imagery and pricing resolve correctly without one product page per colourway.

  • Sizing and fit guidance

    Size guides, model measurements and fit notes placed where the decision is made. The cheapest returns reduction available to most brands.

  • Imagery that answers fit questions

    On-model and detail shots with consistent conventions across the range, which is a workflow problem as much as a photography one.

  • Seasonal collection and URL strategy

    Collections that survive a range change, with redirects for retired lines so seasons of accumulated ranking are not discarded.

  • Drop readiness

    Caching, queueing and inventory behaviour tested against a simulated spike rather than discovered during one.

  • Returns and exchange flows

    Self-serve returns with exchange encouraged over refund, because an exchange keeps the revenue and a refund does not.

Frequently asked questions

How do you reduce returns in fashion?

Mostly by answering fit before purchase rather than processing it afterwards: size guides specific to each range, model height and worn-size, measurements rather than letter sizes alone, and customer fit feedback where volume supports it. None of it is exotic. The reason it is often missing is that it is a content and workflow commitment, not a feature to switch on.

Can Shopify Plus handle a large apparel catalogue?

Yes for most. The constraint worth checking early is variant limits per product and how your range maps onto them — brands with deep size runs across many colourways sometimes need a deliberate modelling decision rather than a naive import. Adobe Commerce handles very deep configurability more naturally at higher cost.

How do we prepare for a drop?

Test the spike before it happens. Caching behaviour, inventory decrementing under concurrency, queueing and any third-party script in the critical path. A drop surfaces every weakness at once, and the failure mode is usually a dependency rather than the platform itself.

What happens to last season's URLs?

They get redirected to the closest live equivalent, not to the homepage. Retired product and collection URLs often carry years of accumulated links and rankings, and sending them all to a generic page discards that. It is handled as part of the seasonal process rather than as an annual clean-up.

Want to talk through Fashion & Apparel for your store?

Tell us where it hurts. We will tell you honestly whether we are the right people for it.

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