Not all leads are equal. AI lead scoring helps you focus on prospects most likely to buy, so your sales team spends time on the right opportunities.
What is Lead Scoring?
Lead scoring assigns points to prospects based on:
- Fit - How well they match your ideal customer profile
- Interest - Engagement with your content and outreach
- Behavior - Actions that indicate buying intent
Higher scores = more likely to buy = higher priority for sales.
Traditional vs AI Scoring
Traditional (rule-based):
- Manual rules: +10 for job title, +5 for company size
- Simple but often inaccurate
- Does not adapt to changing patterns
AI-powered:
- Analyzes thousands of data points
- Learns from actual conversion data
- Identifies patterns humans miss
- Continuously improves accuracy
Data Points AI Considers
- Firmographic - Industry, size, revenue, growth rate
- Technographic - Technology stack, tools used
- Behavioral - Website visits, content downloads, email opens
- Intent signals - Searches, competitor visits, review site activity
- Timing - Budget cycles, recent funding, hiring patterns
Implementing Lead Scoring
- Define ideal customer - What do best customers look like?
- Collect data - Integrate sources into your CRM
- Start simple - Basic scoring before AI
- Train on outcomes - Feed conversion data to improve
- Act on scores - Route and prioritize accordingly
- Iterate - Refine based on results
Lead scoring helps sales teams work smarter, not harder. AI takes it further by finding patterns that drive conversion. Focus on quality, not just quantity.