How to score leads
- Edit the scoring model: list the signals that predict a good customer and give each a number of points. Mark each as fit (who they are) or intent (what they have done). Negative points flag poor leads.
- Set the thresholds for hot and warm leads.
- Tick what you know about a lead to see its score and grade, or paste a CSV of leads with yes/no columns to score a whole list and download the result.
Building a lead scoring model that works
Lead scoring helps sales spend time on the people most likely to buy. Combine two kinds of signal. Fit measures how closely a lead matches your ideal customer: job title, company size, industry, location. Intent measures interest: requesting a demo, visiting the pricing page, attending a webinar, replying to emails. A senior buyer at a perfect-fit company who has done nothing yet is a great target for nurturing, while a student who downloads every guide is high intent but low fit.
Start simple with five to ten criteria and round numbers, then check the model against reality: look at customers you won and lost over the last six months and see whether they would have scored high. Adjust points until the hot group really converts better. Agree the thresholds with sales so that "hot" means the same thing to both teams, and decay intent points over time, because a pricing visit last week matters more than one last year. Lead data you paste stays in your browser.
What is a good lead score threshold?
There is no universal number. Set it so that leads above the threshold convert noticeably better, and revisit it every quarter.
What is the difference between fit and intent?
Fit is whether they match your ideal customer profile. Intent is how actively they are showing interest right now.
Should lead scores include negative points?
Yes. Competitors, students, job seekers, personal emails for B2B and unsubscribes should lower a score.