Insights · Apparel & Fashion
We read apparel listings with one question in mind: could an AI shopping assistant confidently tell a buyer whether a piece would fit them?
Fabric composition, weight, cut and silhouette are the attributes apparel listings are built to convey — an assistant reading them can usually explain what a piece is made of and how it's meant to sit.
But the detail that decides "will it fit me?" is the one most easily deferred to a chart — and a chart is not text.
This is rarely a case of the information being missing altogether. Where the fit answer exists only as a size chart image, a fit-finder widget or a size-guide tag, it is present for a human and absent for an assistant. Reading the listing text, the assistant sees the fabric and the silhouette but cannot confirm the fit, so it hedges rather than confidently matching the piece to a buyer's body.
Why this happens
A size chart or fit-finder works well for a human shopper who can click through and compare. But an AI assistant reading a product description doesn't see that chart — it sees the words on the page. When the measurements and fit notes live only in an image or an interactive widget, the assistant is left with the size name (S/M/L) and nothing about how that size actually runs.
It works differently where fit is the whole product: bras and fitted footwear tend to publish full sizing in text, because the question can't be deferred to a chart. And differently again for made-to-measure pieces, where there's no standard size to chart in the first place — the fit question is answered by the process, not a measurement.
What to do about it
State garment measurements in the description text — chest, waist, length per size. Not just in the size chart image.
Add a plain fit note — "true to size," "runs small, size up," "relaxed, oversized fit." It's often cheaper to write than a measurement and answers the buyer's actual question.
State the model's height and the size they're wearing, if you photograph on a model. It's a cheap, partial fit cue that's rarely stated.
For fit-critical pieces — bras, fitted footwear — publish full sizing in the text itself. Where fit is the whole product, a chart alone isn't enough.
Keep stating fabric composition and weight — you already do this well. It's the strong foundation the fit detail builds on.
Keep describing the cut and silhouette — also done well. It tells an AI what the piece looks like; fit tells it who it's actually right for.
Squiggle reads your entire Shopify catalogue and shows you which products state the fabric and cut but leave fit for the buyer to guess — and what to add first.
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Squiggle's category guidance is informed by ongoing analysis of publicly available ecommerce product information. Illustrative examples explain common product-understanding principles and do not represent findings from any one merchant or the entire category. This guidance is scoped to new, standard-sized retail. It works differently for vintage and one-of-one resale (where measurements are often the primary fit signal in text), for made-to-measure apparel (where fit is answered by the process, not a published measurement), and for intimates (where fit is the product). No individual store is named. Public catalogue data only — no account access, sales data, or private information was used.