Send Faslet the products worth showing.

Faslet is a retail intelligence platform used by European fashion and apparel brands to reduce size-related returns and recover missed sales. Its core product is a digital size and fit advisor embedded on the product page: shoppers answer a short, anonymous questionnaire about height, weight, age and body shape, and Faslet's algorithm matches that profile against each brand's own size chart — drawing on what the company calls its proprietary "Body Profile Database" — to recommend the size most likely to fit. Faslet also offers a virtual try-on feature and "missed sales" tracking that flags when a shopper likely abandoned a purchase because their size was unavailable.

Faslet markets itself under the tagline "every body wins" and states, in its own materials, that its technology can reduce size-related returns by up to 10% and lift conversion by up to 15%. These are Faslet's own reported figures, not independently audited numbers.

Faslet is not a shopping channel like Google Merchant Center or Meta — it does not advertise your products anywhere. It is a fit-recommendation layer that sits on top of your existing product data, which is why it still needs a structured feed.

Faslet's size recommendations only work if it knows what it's recommending sizes for. The feed is how Faslet learns which products exist in your catalog, what sizes and genders each product is available in, and which listings are variants of the same underlying style (so a T-shirt in sizes S–XL and three colors is treated as one product with multiple options, not nine unrelated items).

Without a correctly structured feed, Faslet can't reliably decide which products should show its size-assistant button, can't group size/color variants together, and — per Faslet's own documentation — will simply ignore any item it can't classify (rather than blocking your whole catalog). Getting the feed right is what determines whether the fit assistant appears on the right products at all.

It's worth being precise about what the product feed actually powers, because Faslet's fit engine relies on two separate data sources that are easy to conflate. According to Faslet's API documentation, the product feed supplies catalog-level attributes — identifiers, grouping, size labels, gender, availability, price and imagery. The actual body-to-garment matching, however, is driven by a separate endpoint (v1/size-specs) where a brand uploads real garment measurements per size label — for example waist, hips, chest, sleeve and shoulder, each with minimum and maximum "stretched" values.

In short: the feed tells Faslet what a product is and which sizes exist; the size-specs data tells Faslet how that garment actually measures in each size. Both need to be accurate and kept in sync for recommendations to be reliable — a feed with perfect size labels but no corresponding measurement data won't produce a meaningful fit recommendation.

Per Faslet's own product-feeds documentation, Faslet does not use a proprietary feed format — it ingests a feed in the Google Shopping format, the same specification used for Google Merchant Center. In practice this means most brands can point Faslet at the same feed they already maintain for Google Shopping, rather than building a second, Faslet-specific export.

The feed is connected by pasting its URL into a field in the Faslet Partner Portal (portal.faslet.net), under your store's settings. From there, a validation button checks the feed and shows a pass/fail result: a green checkmark if it's valid, or a popup listing specific errors and warnings if it isn't.

Faslet's documentation lists the following attributes as required for a feed to process correctly. These map directly onto standard Google Shopping feed fields.

Faslet will also read a GTIN (EAN/UPC/JAN) if one is present, but its documentation cautions merchants to make sure it's a genuine GTIN and not an internal store product ID — if you don't have a valid GTIN, Faslet recommends leaving the field out and setting identifier_exists to false instead of submitting a fabricated or incorrect value.

One privacy-related note from Faslet's documentation: for any item tagged age_group: kids, Faslet does not remember or reuse a shopper's sizing profile across sessions.

1. In Feedance, use (or adapt) the Google Shopping feed template you already maintain for Merchant Center — since Faslet ingests the same format, you generally don't need a separate template.
2. Confirm the twelve required fields above are mapped and populated for every product you want Faslet's size assistant to appear on: id, item_group_id, title, brand, gender, availability, size, link, image_link, description, price, and age_group.
3. Pay particular attention to item_group_id: it must be shared consistently across every size/color variant of the same product, or Faslet cannot group them and its own validator will flag the item.
4. If your GTIN data is unreliable or reused as an internal SKU, either exclude the gtin field or set identifier_exists to false, rather than passing an invalid value through.
5. Publish the feed from Feedance to a stable, publicly reachable URL.
6. In the Faslet Partner Portal, under your store's settings, paste that feed URL into the Google Shopping Feed field.
7. Run Faslet's built-in validation. Resolve any items flagged with errors (a missing item_group_id is a documented example) before assuming the feed is fully live.

Faslet's documentation does not publish an exact polling interval or refresh schedule for the feed, so this section is general guidance rather than a sourced number. Faslet's FAQ does confirm that new products are detected automatically and that "most new products will be live very soon after adding them," with no manual resubmission needed on the merchant's side — which implies the feed is refreshed on a recurring basis rather than only read once at setup.

Because Faslet reads the same feed you likely already maintain for Google Shopping, the practical guidance is to keep it on whatever refresh cadence you already use for Merchant Center (commonly at least daily for stock and price accuracy) rather than treating it as a separate, lower-priority feed.

Faslet's own documentation describes its validation behavior directly, rather than publishing an exhaustive error-code list:

• Missing item_group_id — documented directly as a named validation error ("the item_group_id is missing"), which prevents Faslet from correctly grouping that product's variants.
• Items missing size, gender, or item_group_id — Faslet's docs note these are typically products that shouldn't show the Faslet button in the first place (e.g. non-apparel items); if they trigger validation warnings, they are simply ignored rather than blocking the rest of the feed.
• Invalid or reused GTIN values — Faslet warns against passing a store's internal product ID in the GTIN field; an invalid GTIN should be omitted with identifier_exists set to false instead.

Per Faslet's documentation, an item-level error does not take down the whole feed — only that item's Faslet integration: "Faslet cannot process any items that have errors," but everything else continues to validate normally.

General best practices — not sourced from a specific Faslet document, but consistent with how the required fields function:

• Keep item_group_id assignment logic in Feedance identical to whatever you use for Google Shopping, so you're not maintaining two different grouping rules for the same catalog.
• Since Faslet's fit accuracy depends on both the feed and the separate garment-measurement data, treat feed cleanup and measurement-data upkeep (via Faslet's size-specs process) as two halves of the same project rather than sequencing one after the other.
• Audit gender and age_group values regularly — these directly control whether and how the size assistant behaves, and drift here (e.g. unisex items miscategorized) will surface as visibly wrong recommendations, not just a feed warning.
• Re-run Faslet's Partner Portal validation after any bulk catalog change (new season, size-range expansion, brand migration), rather than assuming a feed that validated once will stay clean.

Is Faslet a shopping/ads channel like Google or Meta?
No. Faslet doesn't advertise your products; it's a size and fit recommendation widget for your own product pages. It still consumes a product feed to understand your catalog and variants.

Can I reuse my existing Google Shopping feed for Faslet?
Generally yes — Faslet's documentation states it requires the feed in Google Shopping format, so most merchants can point Faslet at the same feed URL used for Merchant Center.

Does the feed alone power size recommendations?
No. The feed establishes your catalog structure (products, variants, sizes, gender). The actual garment measurements used to compute fit are submitted separately through Faslet's size-specs API.

What happens if some items in my feed have errors?
Per Faslet's documentation, items with errors are not processed, but this doesn't block the rest of the feed — everything else continues to validate and go live normally.

Do kids' products work differently?
Yes. For items tagged age_group: kids, Faslet's documentation states it does not remember or reuse a shopper's sizing profile, for privacy reasons.

Which platforms does Faslet support?
Faslet publishes a dedicated Shopify app ("Faslet Size & Fit") and a Custom Integration Guide for other platforms, covering adding the Faslet snippet to the product page, enabling variants, and order tracking.

Faslet is a size and fit advisor used by European fashion and apparel brands. It adds a size-recommendation button to the product page: shoppers answer a few short questions about their body, and Faslet matches that profile against the brand's own size chart to suggest the size most likely to fit. The goal is fewer size-related returns and fewer abandoned purchases.

Faslet is not a sales or advertising channel. It does not list your products anywhere else. It is a layer on top of your own store, and it needs your product data to know which products exist, which sizes and genders they come in, and which items are variants of the same style.

Faslet reads a product feed in the Google Shopping format, so most brands can use the same feed they already maintain for Google Merchant Center. The feed URL is added in the Faslet Partner Portal, where a built-in validator flags errors such as a missing item_group_id. Garment measurements per size are supplied separately to Faslet.

Feed quality decides where the fit advisor appears and how well it works. Consistent item_group_id values across all size and color variants, correct size labels, gender and age group, and accurate availability let Faslet group products properly and show recommendations on the right product pages.