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Fabrik Analytics™
For fashion brands

Analytics for fashion brands that trade in sizes and seasons.

Size curves, pace against plan, profit after returns and the email designs that earn, joined on one timeline with every change to your store, ads and email.

Products by size: bought against sold for each size, and the selling lines with gaps in their size run, for Fellwick, an example brand.
What fashion fights with

A range bought months ago, sold day by day.

  • The curve you bought is not the curve you sell

    M and L go first, XS and XL sit, and the next buy repeats the mistake unless someone looks.

  • Best sellers that come back

    A dress that sells well and returns 40% of the time is a different product once the refunds land.

  • Months that do not trade in straight lines

    A slow first week is normal and a slow last week is not; a flat target cannot tell them apart.

Pace against plan

Know by the 9th whether the month will land.

A target that follows how the month trades, the gap split into orders, discounts and returns, and a list of lines to push and hold.

See targets and the P&L
Targets: the year by month, actual against target, with the projection, for Fellwick, an example brand.
Profit after returns

The best seller on revenue is not always the best on profit.

Returns, costs and the ads that sold it come off, product by product, and the tiers written to Shopify follow.

See the P&L
The P&L: lines down, months across, contribution margin from CM1 to CM3 and EBITDA, for Fellwick, an example brand.
Email design

See which email designs actually earn.

Every campaign kept as sent and ranked by revenue per recipient, beside your sales.

See email
The email gallery: eight email designs side by side, each with its revenue per recipient, for Fellwick, an example brand.
The numbers fashion runs on

Built on sizes, stock and seasons.

  • Size coverage

    The share of sizes in stock for every selling line, and the lines with holes in their run.

  • Sell-through

    Units sold against units received, per product and range.

  • Discount rate and markdown candidates

    How deep the month's discounts ran, and the lines the markdown rule flags.

  • Return rate against the category

    Products returning far more than their category, a fit signal worth checking.

  • Theme publishes

    Every new season's theme on the line with the conversion that followed.

  • Entry products

    Which first purchase brings a customer back for a second.

Does it work with our size set-up?

Size views read the Shopify option named Size and normalise the common formats. If yours differ, we check them with you before you rely on them.

Can it separate wholesale and store sales from online?

Yes. The Shopify data filter includes or excludes sales channels and order tags, so the numbers can be web only.

See your own size curves and pace.

A 30-minute walkthrough on your store's data, with a straight answer on fit.