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AI Lead Scoring

Focus on the leads most likely to convert with AI-powered scoring.

How Lead Scoring Works

Data Inputs

AI analyzes:

  • Firmographics - Company size, industry, location
  • Demographics - Title, seniority, department
  • Behavior - Email opens, clicks, website visits
  • Intent - Research activity, competitor visits
  • Fit - Match to ideal customer profile

Score Calculation

Each lead receives a score from 0-100:

  • 80-100 - Hot lead, prioritize immediately
  • 60-79 - Warm lead, good potential
  • 40-59 - Neutral, needs nurturing
  • 20-39 - Cool lead, low priority
  • 0-19 - Poor fit, likely not a prospect

Score Components

Fit Score (0-50 points)

How well they match your ICP:

  • Company size match: +10
  • Industry match: +10
  • Title/seniority match: +15
  • Location match: +5
  • Technology match: +10

Engagement Score (0-50 points)

How engaged they are:

  • Email opens: +5 each
  • Link clicks: +10 each
  • Website visits: +5 each
  • Content downloads: +15 each
  • Meeting booked: +20

Using Lead Scores

Prioritize Outreach

Sort prospects by score:

  1. Call hot leads (80+) immediately
  2. Email warm leads (60-79) today
  3. Nurture neutral leads (40-59)
  4. Deprioritize low scores

Route to Reps

Auto-assign based on score:

  • Hot leads → Senior reps
  • Warm leads → All reps
  • Cool leads → SDR team

Trigger Automation

Based on score changes:

  • Score reaches 80 → Alert sales
  • Score drops below 40 → Add to nurture
  • Score increases 20+ → Create task

Customizing Scoring

Adjust Weights

Change importance of factors:

Company Size: 2x weight (very important)
Industry: 1.5x weight (important)
Location: 0.5x weight (less important)

Add Custom Signals

Include your own criteria:

  • Specific technologies used
  • Certain job titles
  • Custom field values
  • List membership

Define ICP

Tell AI your ideal customer:

  • Target company size
  • Target industries
  • Target titles
  • Target locations

Score Analytics

Score Distribution

See how leads are distributed:

  • Hot (80+): 5%
  • Warm (60-79): 15%
  • Neutral (40-59): 30%
  • Cool (20-39): 35%
  • Poor (0-19): 15%

Score Accuracy

Track prediction quality:

  • Do high-score leads convert more?
  • Conversion rate by score band
  • Adjust model based on results

Monitor changes over time:

  • Rising scores (increasing engagement)
  • Falling scores (cooling off)
  • Sudden changes (new signals)

Frequently Asked Questions

How often are lead scores updated?

Scores update in real-time when new engagement data arrives (email opens, clicks). Fit scores recalculate daily based on profile data changes.

Can I override AI lead scores manually?

You can’t directly edit scores, but you can prioritize or deprioritize leads regardless of score. Use tags or custom fields to mark leads for special handling.

Why did a lead’s score drop suddenly?

Score drops typically indicate negative signals: email bounced, unsubscribed, marked as spam, or became a poor ICP fit. Check the prospect timeline for recent events.

How do I know if my scoring model is accurate?

Review conversion rates by score band. If high-scoring leads don’t convert better than low-scoring ones, adjust your scoring weights or ICP definition.

Can I create multiple scoring models for different segments?

Currently, one scoring model applies globally. Use custom fields and filters to apply different prioritization logic to different segments manually.

Lead scoring is a guide, not gospel. Use it to prioritize, but trust your instincts too.