Case Study — ARNE
More efficient. More revenue.
At £300k+ monthly spend.
ARNE is a fast-growing UK fashion brand with a strong product range and an audience that was responding well to paid social. With momentum building in the UK, the brand set its sights on two things simultaneously: improving the efficiency and returns of existing paid channels, and establishing a commercial foothold in the US market. The brief required both rigorous in-market optimisation and the strategic thinking to open a new territory from scratch.
Scale revenue across Meta and Snapchat while improving ROAS and CPA year-on-year — at a UK spend level exceeding £300k per month where inefficiency compounds quickly. Alongside this, build and launch a US paid social presence from the ground up, deploying budget intelligently into a market where audience knowledge of the brand was limited and targeting decisions had to be data-led from day one.
The account was managed hands-on daily — in platform for optimisation every day, on every client call discussing budgets and strategy, and across both Meta and Snapchat simultaneously. The approach was built on four foundations: intelligent campaign architecture, data-led optimisation beyond in-platform metrics, strategic US market entry, and multi-platform discipline.
Campaign Architecture
With multiple clothing ranges each requiring distinct audience strategies, a dedicated full-funnel structure was built per range rather than consolidating everything into a single campaign framework. This was pre-ASC — before Meta's Advantage+ Shopping Campaigns became mainstream — which meant building and managing traditional sales campaign structures with granular control over targeting, creative, and budget allocation across every funnel stage. The architecture gave each range the space to perform on its own terms rather than competing for budget within a blended structure.
Attribution-Led Optimisation
Optimisation decisions were not based on in-platform data alone. Triple Whale's Total Impact attribution model — which uses AI to incorporate broader data touchpoints and more accurately assign revenue to channels like Meta — was used as the primary basis for budget and creative decisions. This gave a clearer picture of true channel performance than Meta's own reported ROAS, and meant optimisation decisions were grounded in commercial reality rather than platform-reported metrics that tend to overstate returns.
US Market Entry
Rather than replicating the UK targeting approach in the US, first-party data was used to identify the specific locations and audience profiles most likely to convert — prioritising areas where brand awareness already existed or where demographic and behavioural signals indicated commercial intent. This ensured that the US budget, while significantly smaller than the UK, was deployed with precision rather than spread thinly across a market where the brand was still establishing itself.
Snapchat
Snapchat was managed in parallel with Meta throughout — maintaining consistent campaign oversight, creative testing, and optimisation across both platforms simultaneously. The cross-platform view ensured budget was working efficiently across the full paid social mix rather than each channel being managed in isolation.
The Results
+ Snap
Improving ROAS by 124% while cutting CPA by 55% is one thing. Doing it while scaling spend — not reducing it — is what makes the numbers meaningful. At £300k+ monthly, every percentage point of efficiency is material.
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