AI decision engine
How I turned dashboards into recommended actions
Combining acquisition and CRM signals into automated diagnostics that tell the team what to do next.
Analysis time reduced
One diagnostic view
The challenge
Dashboards showed what happened, not what to do.
The team had plenty of charts but still spent hours working out the next action.
Signals from campaigns and the CRM lived in different places and told different stories.
What mattered most
The strategy
Structure reporting around decisions.
I built diagnostics that read the signals and suggest the action.
- 01Combined acquisition and CRM information into one model.
- 02Built automated performance diagnostics.
- 03Structured reporting around recommended actions rather than raw metrics.
- 04Kept a human review step on every recommendation.
The evidence
The numbers behind the result.
Graphs use the exact figures from the engagement. Where raw totals were not available, values are indexed to a starting point of 100.
Optimisation analysis
Less time from signal to action
Indexed to 100 because raw hours were not supplied.
The outcome
Quicker, more confident decisions.
Optimisation analysis time fell by 16%, with multiple campaign and CRM signals unified in one place.
The team spends less time diagnosing and more time acting.
Capabilities demonstrated
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A personal point of view
What I’m seeing, testing and learning in PPC right now.
I’m John Iliopoulos, a PPC and growth marketer with over 10 years in the industry. PPC Supreme is where I share my take on what’s changing, experiments I’m running, things that genuinely work and the lessons behind the results.
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