Predictive lead prioritisation
How I found a lead segment converting at almost double the baseline
Analysing thousands of historical leads and contracts to build cohorts around conversion probability.
Historical lead dataset
Outcomes used for modelling
vs 10.6% baseline
The challenge
History was full of answers nobody had asked for.
The CRM held years of lead and contract outcomes, but they were only ever used for reporting the past.
I wanted to use that history to predict which new leads deserved the most attention.
What mattered most
The strategy
Turn historical data into conversion probability.
Using AI assisted analysis, I built cohorts around the signals that most reliably predicted a contract.
- 01Analysed 2,926 historical leads and 311 resulting contracts.
- 02Built customer cohorts around multiple conversion signals.
- 03Identified the segments that deserved greater sales priority.
- 04Used three AI platforms to process and interpret the data at speed.
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.
Conversion rate
The priority segment nearly doubles the baseline
The outcome
A clear priority list for sales, built from evidence.
The priority segment converted at around 19.2% against a 10.6% baseline, giving sales a data backed reason to act on those leads first.
The same cohorts informed acquisition, helping paid media chase more of the people most likely to sign.
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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