Industries

Built for apparel brands

See profit after returns, improve fit decisions and make discounts earn their margin.

What each team gets

Illustrative scenarios and figures. We model your actual costs, customer behaviour and business rules.

Founder and finance

A healthy total hides customers whose costs exceed their sales.

TodayContribution

$16.4m

128,400 customers

With customer-level profitContribution

+$20.5m

87,700 customers40,700 customers −$4.1m

Action: See contribution by customer and cohort over an agreed period, then decide where to invest.

Growth

The launch looked profitable. Then the returns arrived.

TodayCampaign revenue

Orders placed Count the sale before the return window closes

With customer-level profitCustomer contribution

Keep what customers keep Join acquisition, discounts, kept items and return costs

Action: Compare launch cohorts after returns settle, then recommend budget changes.

Your expertise, embedded: Finance sets the return window and payback target; marketing approves changes.

Operations and CX

A valuable customer keeps returning the same cut.

TodayReturn rate

Customer looks expensive High returns alone do not show relationship profit

With customer-level profitFit + customer profit

Fix the fit problem Check retained margin alongside product, size and return reasons

Action: Flag repeated fit issues for buying and CX; recommend a better-fit alternative.

Your expertise, embedded: Your fit team defines product relationships; CX preserves the returns promise.

CRM

New-season buyers get the same code as sale-only shoppers.

TodayOne sale email

20% off for everyone The offer ignores full-price purchase history

With customer-level profitOffer + holdout

Test extra contribution Compare profit after the discount, fulfilment and returns

Action: Create separate audiences and test whether a code earns extra profit.

Your expertise, embedded: Your team sets eligible ranges, contact limits and minimum margin.

What it was worth for one apparel retailer

Shirts on wooden hangers in a clothing store

Case study, apparel retail

A $80m apparel retailer, 25 stores and online

Customer data sat in POS, Shopify and Klaviyo with no way to act on it. We joined it into one record per customer, ranked by gross margin.

Read the case study →

5%of customers make 38% of gross margin

66%of new customers didn't come back within a year

~$3mestimated extra revenue from targeted actions