Customer pattern detection
How I spotted the most valuable leads before sales even picked up the phone
Combining keyword, property and behavioural data to identify high converting lead patterns and flag them for faster sales follow up.
3.9% vs 3.2% overall
vs 1.36 hours overall
CRM data fed into AI analysis
The challenge
Every lead was treated the same, even though some were far more likely to sell.
Sales worked leads in arrival order. Valuable sellers could sit waiting while weaker enquiries were called first.
The data to tell them apart already existed across keywords, property details and reasons for sale. Nobody had joined it up.
What mattered most
The strategy
Teach the business what a great lead looks like.
I combined multiple data sources to find repeatable winning combinations, then put them in front of sales.
- 01Combined keyword, property, reason for sale and behavioural data into one view.
- 02Identified recurring combinations that converted at a higher rate.
- 03Flagged pattern leads inside the CRM so sales could prioritise them.
- 04Fed CRM data into AI analysis tools to keep discovering new patterns.
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
Pattern leads convert better
Response time
Pattern leads reached faster
October response times. Shorter is better.
The outcome
Sales now knew who to call first.
Pattern leads converted at 3.9% against 3.2% overall, a 22% advantage.
In October, pattern leads were reached in 48 minutes on average compared with 1.36 hours overall, putting the best opportunities in front of sales sooner.
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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