Lead Scoring
A methodology for ranking leads based on their perceived value to the organization, using a combination of demographic, firmographic, and behavioral data points to prioritize sales outreach.
Most Lead Scoring Models Are Broken
Here is the dirty truth: most B2B companies have a lead scoring model that nobody trusts. Marketing set it up two years ago based on assumptions. Sales ignores the scores. Nobody has validated whether high-scoring leads actually convert better than low-scoring ones. If that sounds like your company, you are in the majority.
Building a Score That Works
Start with your closed-won deals from the last 12 months. What did those buyers look like? What did they do before buying? Build your scoring model backward from conversion, not forward from assumptions. If pricing page visits correlate with 3x higher conversion, weight them heavily. If whitepaper downloads do not correlate, stop counting them.
The Two-Axis Model
Score leads on two dimensions: fit (how well they match your ICP) and intent (how engaged they are). A lead with high fit and high intent is your top priority. High fit, low intent gets nurtured. Low fit, high intent gets monitored. Low fit, low intent gets deprioritized. This 2x2 framework is simple enough for sales to actually use.
Predictive Lead Scoring
AI-powered lead scoring uses machine learning to identify patterns humans miss. Instead of manually assigning point values, the model learns from historical conversion data which combinations of attributes predict purchase. Predictive models outperform manual scoring by 20-30% in most studies. Tools like MadKudu, Clearbit, and 6sense offer this capability.
Frequently Asked Questions
What are the best lead scoring criteria?
Combine fit scores (company size, industry, title, revenue) with engagement scores (website visits, content downloads, email opens, pricing page views). Fit tells you if they could buy. Engagement tells you if they want to buy. You need both — high fit with low engagement is a target account, not a lead.
How often should you update your lead scoring model?
Review quarterly at minimum. Analyze which scored leads actually converted to customers and which did not. If high-scoring leads are not converting, your model is wrong. Most companies set lead scoring once and forget it — that is why most lead scoring models underperform.
