Lead scoring assigns a numerical value to each lead based on how well they match your ideal customer profile (fit score) and how engaged they are with your brand (behavior score). A VP of Sales at a 200-person SaaS company who visited your pricing page three times scores higher than an intern at a 5-person startup who downloaded one whitepaper. Scoring lets sales teams prioritize their outreach based on data instead of intuition.
Why It Matters
Without scoring, every lead looks the same in the queue. Reps either work them first-in-first-out (which ignores quality) or cherry-pick based on company name recognition (which misses hidden gems). Lead scoring solves this by ranking leads objectively. But here's the catch: scoring is only as good as the data feeding it. If company size, industry, and title fields are missing or wrong, the score is meaningless. Most broken lead scoring models have a data quality problem, not a model problem.
How Lead Scoring Works
- Fit scoring: Award points based on firmographic match: company size, industry, tech stack, geography, growth stage
- Behavior scoring: Award points for engagement: pricing page visits, demo requests, email clicks, webinar attendance
- Negative scoring: Deduct points for disqualifying signals: competitor employee, student email domain, unsubscribed from emails
- Score thresholds: Define what score triggers action: 80+ = fast-track to sales, 50-79 = nurture, below 50 = low priority
- Model refinement: Compare scores against actual conversion data quarterly. Adjust weights for the attributes that actually predict deals
Example
A company builds a scoring model: 20 points for 100-500 employees, 15 for SaaS industry, 10 for VP+ title, 25 for demo request, 10 for pricing page visit. A lead scores 80 and gets called within an hour. Another lead with the same behavior but at a 5-person company scores 45 and goes into the nurture track. Close rates for 80+ leads are 4x higher than unsorted leads.
Related Terms
Related Resources
Lead scoring not working?
The problem is usually missing data, not bad models. We'll enrich your leads so your scoring has the inputs it needs.
See What We'll Find