The Hidden Cost of Poor Signals

The account leaned heavily on broad match and branded search. Performance looked acceptable in-platform, but the underlying economics told a different story:

• Branded campaigns captured existing demand while masking true acquisition costs
• Broad match drove volume, but with inconsistent contribution to pipeline
• Tracking gaps meant conversion data failed to reflect actual sales outcomes

Decisions were being made on incomplete information. CAC increased as inefficiencies scaled.

Our Approach

1. Rebuild the Measurement Layer
We started with the foundation:

• Audited tracking across Google Ads and HubSpot
• Mapped HubSpot lifecycle stages, including SQL, to Google Ads conversion events
• Shifted optimisation signals towards revenue-qualified actions

This created a direct line between ad spend and pipeline impact.

2. Refocus on Revenue Signals
With accurate data in place, performance patterns became usable:

• Identified search terms consistently generating sales-qualified leads
• Deprioritised activity that drove volume without pipeline contribution

Targeting moved closer to revenue, not just lead generation.

3. Tighten Spend Allocation
We restructured campaigns to remove inefficiency:

• Reduced exposure to broad match where quality was inconsistent
• Limited reliance on branded terms to reveal true acquisition performance
• Concentrated budget on high-intent queries with proven conversion to SQL

Each decision prioritised output quality over input volume.

Results

• 38% reduction in CAC through improved data accuracy and tighter targeting
• Higher share of spend allocated to SQL-generating search terms
• Clear attribution between media investment and revenue outcomes

Efficiency improved without constraining growth.

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