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Confidential — UK Ecommerce Brand

[demo] GA4 was reporting 40% fewer conversions than Shopify. The team was optimising paid campaigns on bad data, inflating perceived CPA by nearly

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CONVERSION INCREASE

37%

CPA REDUCTION

41%

EVENT MATCH SCORE

8.7

TRACKING GAP

<3%

§ · What happened

Challenge

The problem

GA4 was reporting 40% fewer conversions than Shopify. The team was optimising paid campaigns on bad data, inflating perceived CPA by nearly double.

Solution

What we built

Deployed a GTM server container on a first-party subdomain. Migrated all GA4 and Meta CAPI events to route server-side. Implemented a data layer with enriched order data including margin and LTV signals.

Results

The outcome

Conversion tracking gap closed from 40% to under 3%. Meta CAPI event match quality score improved from 4.2 to 8.7. ROAS reporting stabilised within 8% of actual revenue.

Confidential — aggregated from three UK ecommerce deployments on the Compound Sprint tier (£5,000 / 30 hours per month). Client names anonymised; numbers combined and rounded. The tracking infrastructure and outcome pattern are real.

The problem

GA4 was reporting 40 percent fewer conversions than Shopify. The team was optimising paid campaigns on that data, which inflated perceived CPA by nearly double and made profitable channels look broken. The root cause was entirely browser-side: ad blockers, Safari ITP, and consent friction were stripping signals before they reached Google.

What we did

Deployed a GTM server container on a first-party subdomain. Migrated all GA4 and Meta CAPI events to route server-side. Built an enriched data layer that passed margin and LTV signals alongside the standard event payload — so the data that arrived was worth acting on, not just present.

The numbers

Conversion tracking gap closed from 40 percent to under three percent. Meta CAPI event match quality score improved from 4.2 to 8.7. ROAS reporting stabilised within eight percent of actual revenue. The paid channels that looked underperforming were performing — the data was the problem.

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