The ads were working. We aimed them better.
The Silk Co came to us for Meta ads that were already returning four to five times. The bigger opportunity was upstream: spend was spread across the catalogue while the genuine hero kept selling out. We put ad spend, restocks and pricing onto one shared set of numbers.
across twelve months Channel result
on ad spend, February Channel result
validated per SKU Verified
sold in sixty days Verified
Return-on-ad-spend figures are the client’s own bookkeeping records across the twelve months to April 2026 and span a period largely preceding this engagement — they describe a channel that was already performing, which is what made the targeting work worth doing. Margin and sell-through are validated against supplier invoices and Shopify data. Absolute revenue and cash figures are withheld.
Good instincts, running ahead of the data.
A founder-led boutique moving fast on taste and judgement, with the numbers underneath not yet catching up to the decisions being made on top of them.
A brand with real customer love and a paid channel already returning four to five times. What it did not have was a shared view of which products deserved that spend.
Landed costs had never been validated per style, so true margin varied more than anyone realised. Restocks were sized on feel rather than sell-through. And email — usually a fashion brand’s most profitable channel — was sitting almost entirely untouched.
One evidence base that ad spend, restock sizing, pricing and discounting could all be argued from — so the next decision was defensible rather than instinctive.
Then point the working channel at the products the data says people actually want, and open the channels that weren’t running yet.
Six workstreams, one source of truth.
What each style really earns
Validated per-unit landed costs against twelve historical supplier invoices and separated accounting entries from real cash movement, so margin could be trusted style by style.
Buying to demand
Built a six-month sell-through time series across every style, so restock sizing follows what is actually selling rather than what sold last season.
Spend that follows the data
Wrote ad copy at volume across the catalogue and repointed budget onto in-demand, in-stock product instead of spreading it evenly across the range.
Pricing and promotion strategy
Sized the restock and the next collection against real sell-through, and built a four-tier archive sale with a gift-with-purchase layer to protect margin while clearing slower lines.
An ambassador programme, built
Designed the programme and built the page into the live Shopify theme — tier structure, creator content and signup capture — opening an affiliate channel the brand did not previously have.
Keeping the numbers current
Built a structured brand knowledge base and an automated update pipeline, so the team keeps making decisions from live data instead of a spreadsheet someone exported last quarter.
The data already knew what the hero was.
97 units in sixty days, then sold out.
One blouse was doing far more work than the rest of the range: 97 units in sixty days, straight into a stockout, with customers still asking for it. It was the clearest demand signal in the business.
Meanwhile ad spend was spread evenly across a catalogue where five styles drive 85% of product revenue. The recommendation was simple and immediate: back the winners with both budget and stock, and stop funding the long tail out of the same pot.
- Hero style, 60 days 97 units
- Ended the period Sold out
- Revenue from top five styles 85%
- Spend before Spread across the range
- Spend after Follows demand and stock
A boutique that now argues from evidence.
The channel was already good. Now it is aimed.
Ad spend, restock sizing and pricing all now run off the same validated numbers, with budget concentrated on the styles that carry the revenue and the stock to support them. April 2026 was the strongest trading month of the twelve.
Two channels that were not previously contributing are now open: an ambassador programme built into the live store, and a lifecycle email build scoped against the segments the data identified. Both are aimed at the same thing — more revenue per customer the brand has already paid to acquire.
now the focus of spend
sized against real demand
built into the store
Absolute revenue, profit, cash and inventory values are withheld; figures here are ratios, multiples and unit counts only. Return-on-ad-spend figures are the client’s bookkeeping records and span a period largely preceding this engagement, presented as the channel we inherited rather than an attributed result. Margin and sell-through figures are validated against supplier invoices and Shopify data. Restock sizing is a recommendation based on measured sell-through.