AI Intent Scoring vs. Classic Lead Scoring: What Actually Works
Compare traditional point-based scoring with AI-derived intent models built from PerksMate engagement data.
Classic lead scoring assigns arbitrary points. AI intent scoring uses behavioral data from every agent touch.
Classic Model
- +10 for webinar
- +5 for email open
- -5 for no reply in 14 days
AI Model
Feed engagement events (opens, clicks, replies, call outcomes) into a lightweight model hosted in Supabase Functions. It returns a 0-100 intent score.
Comparison Table
| Metric | Classic | AI Intent |
|---|---|---|
| Setup time | Hours | Minutes (auto) |
| Signal freshness | Manual updates | Real-time |
| Personalisation | Low | High (per ICP) |
Action Triggers
When intent ≥ 80, start phone + LinkedIn agents. When intent drops below 40, move lead to nurture and send a value-add resource.
Implementation Tip
Use agent events as the single source of truth so marketing and sales read the same score.
Frequently Asked Questions
Do we need a data scientist?+
No. Start with a regression or even weighted average. Upgrade to ML once you collect a few thousand labeled events.
How often should intent scores refresh?+
Every time a tracked event occurs. Use Supabase's realtime channel to push updates into the dashboard instantly.
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