AI Lead Scoring
How the score is built from fit and intent, how to read the distribution, and how to tell whether it is working.
Every lead and every response carries an AI lead score, so routing, alerting and
follow-up can be automatic. It is a number you can filter on: a segment
definition like Score is 75-100 is the everyday use of it.
What Goes into It
| Half | What it asks | Inputs |
|---|---|---|
| Fit | Does this account match your ICP? | Industry, company size, revenue, geography, job title, seniority, technologies, each with the weight you gave it |
| Intent | Did they behave like someone with a problem? | Website behaviour, a weighted ICP dimension of its own: sessions, pages, forms viewed, started and completed, and the answers themselves |
A high-fit account with no behaviour and a low-fit one with intense behaviour can score the same for opposite reasons, so read the score next to the record rather than instead of it.
Reading the Distribution
Surface gives you the number and the statistics behind it. Reading the shape of the distribution is judgement rather than a product feature, so treat the rest of this section as how teams do it.
get_ai_score_stats returns average, min, max and distribution buckets in one
call, and get_top_ai_scores with order: "asc" returns the bottom, usually
the more informative end when you are debugging.
What people look for in that distribution is spread:
| Shape | What it usually means |
|---|---|
| Broad spread, thin high tail | Healthy. The score is discriminating. |
| Everything clustered in the middle | The ICP is too vague, or its weights are too even, to separate anyone. |
| A large spike at the very bottom | Often free-provider emails or bot traffic. Check spam protection. |
| A large spike at the top | The ICP may be too narrow, or too much of your traffic is one account. |
Setting a Threshold
Wherever you draw the line, it decides who gets routed and who triggers an alert, so pick it from your own data rather than from a round number. The method most teams use:
Score your closed-won accounts
Most should land above the line you are considering.
Score your closed-lost-as-unqualified accounts
Most of those should fall below it.
Take the crossover, then check the volume it implies
A threshold that produces four leads a week for eight reps is set too high.
Eligibility to book is that threshold plus a territory check. See Routing for how the two fit together, and talk to sales before you move the line.
Checking That It Works
The real test is outcome: over a quarter, do high-scoring leads book and close more often than low-scoring ones?
- The Meetings screen lists the score beside every booked meeting, which is the quickest look at what scores actually book.
get_leads_with_ai_scoresgives you the ranking to compare that against, andget_leads_countwithbyEventType: truegives the meeting-booked totals.compare_formsshows whether one form produces systematically worse-scoring leads than the rest. That is a targeting problem upstream, not a scoring one.
No correlation usually means an ICP that describes an aspiration rather than your book of business. Fix the ICP first.
SAM can rank on outcome rather than volume from Slack: "rank our blog posts by meetings booked, not pageviews". See the prompt library.