Client case study · Fashion ecommerce · UK
ClosureLDN: £4.35m in Google Ads attributed revenue over six months.
- Google Ads attributed revenue
- £4.35m
- Google Ads spend
- £415k
- Reported ROAS
- 10.48×
UK · 1 March–31 August 2026 · All Google Ads channels · Conversion-time reporting. Rounded figures from the six-month scorecard.
- Period
- UK, 1 March–31 August 2026 (six months).
- Basis
- Page 5, all Google Ads channels, conversion-time attributed revenue and total spend. Reported, rounded figures; not an incremental-sales or profit measure.
- Source
- Closure London quarterly business review, prepared 16 September 2026; six-month Google Ads scorecard.
Full-account result
£4.35m in attributed revenue across Google Ads.
Across all Google Ads channels, ClosureLDN recorded £4.35m in attributed revenue from £415k of ad spend over six months, at a reported 10.48× ROAS.
Managing that account meant deciding how different products should compete for budget. The Shopping example below shows how returns, available sizes and commercial priorities informed those decisions across 591 SKUs.
Commercial problem
In stock does not mean worth promoting alike.
ClosureLDN's catalogue carries the usual fashion problems at scale. Some products come back at high rates, so their checkout revenue overstates what the business actually keeps. Some styles are technically in stock but key sizes have sold through, changing which clicks are worth buying. Some stock has a cash-recovery job, and a small group of products was assigned a new-customer acquisition role.
Blended reporting alone cannot express "protect margin here, chase cash there, pay for acquisition over there". The Shopping routing work gave these different product situations separate treatments within a wider account management brief.
The question we set out to answer was simple: which products deserve aggressive bids, which deserve restraint, and how do we make the account act on that difference?
Why we did it
A product's availability and job should set its advertising treatment.
In fashion, the commercial value of a click can change with the size run behind it. A style with broad availability may justify a different bid from the same style once key sizes have sold through. High-return lines need separate scrutiny too: the sale is recorded before the eventual return is known.
A single reported return cannot tell the whole commercial story. Return rates, sellable sizes and the cash position behind each product need to inform account decisions alongside conversion data. This is why we separated the Shopping routing groups.
Inside the account
Four different jobs for 591 products.
As one part of the wider account management work, we reviewed 591 SKUs and routed them into four Shopping groups: High Returns, Cash Flow, New Customer and Broken Sizes. The groups reflected different commercial reasons to bid or hold back, rather than assigning every product the same target.
High-return and broken-size groups were restrained to account for returns and limited size availability. Cash-flow and new-customer groups had more room to bid for their assigned jobs.
The four groups
What each group was for.
| Role | SKUs | Commercial job | How it was treated |
|---|---|---|---|
| High Returns | 340 | Protect contribution | Restrained bidding to account for the risk that attributed checkout revenue would not be retained after returns. |
| Cash Flow | 170 | Convert stock into cash | Allowed more room to bid where moving stock into cash was the commercial priority, subject to the agreed economics. |
| New Customer SKUs | 15 | Acquire customers | Allowed more room to bid for products assigned a customer-acquisition role. |
| Broken Sizes | 66 | Limit wasted spend | Heavily restrained when fragmented size availability made a full-price click harder to justify. |
Supporting Shopping example
£78,085 attributed revenue across four Shopping groups.
Within the account-wide total, these four Shopping groups recorded £78,085 in attributed revenue from £13,968 of spend, at a reported 5.59× ROAS.
The review also showed why product-level attention mattered: 17% of active items (547 Grade-A SKUs) accounted for 80.4% of net sales. That concentration helped frame where to focus commercial attention.
- Period
- UK, 1 March–31 August 2026 (six months).
- Basis
- Four Shopping groups only: conversion-time attributed revenue and spend; included in the all-channel scorecard, not additional. Observational, with no incremental or profit inference. Grade-A share is separate catalogue context.
- Source
- Closure London quarterly business review, prepared 16 September 2026; Shopping routing layer and product grading analysis. Reviewed figures only.
Agency-reported case study based on the September 2026 review. Figures are attributed account results, not independently audited results or a controlled measure of incremental growth.
For retail buyers
BOI®: every SKU has a job™.
BOI®, Bid On Intent, is JudeLuxe’s framework for assigning products a commercial job before deciding how to advertise them. Every SKU has a job™: Scale, Profit, Protect, Recovery or Gateway.
ClosureLDN’s four Shopping groups were an account-specific execution of product-level decisions: high returns, cash flow, new customers and broken sizes. They are not a replacement for the five BOI jobs, and this case does not isolate the financial effect of the framework.
When assessing an agency, ask it to show how product economics change eligibility, bids and budget decisions, which account results include brand demand, and which implementation actions remain open. A blended ROAS alone cannot answer those questions.
Related: Explore BOI®: Every SKU has a job™, the Every SKU Has a Job™ principle, Google Shopping management, Google Ads for fashion retailers and Ecommerce PPC management.
What happens next
Routing was not the end of the job.
The September 2026 review identified a next step: check and tighten exclusion enforcement in the main Performance Max campaign for high-return and broken-size products. That enforcement was not recorded as complete. Allocation decisions also need revisiting as stock, sizes and return rates change.
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