Key takeaways
- A score combines fit (who the lead is) and interest (what the lead does).
- The score is only useful if sales trusts it and acts on it.
- Start with a few simple rules and adjust them against deals you actually won.
How does lead scoring work
You list the traits and actions that tend to come before a sale and give each a number of points. Each lead's points add up to a score, and a threshold decides when it passes to sales.
Negative points matter too. A student email address, a competitor's domain or a job title with no buying role can lower the score.
What is an example of lead scoring
Say a team sells to software companies with 50 to 500 staff. A head of sales at a 120-person software firm earns fit points for industry, size and role. Opening two emails and visiting the pricing page adds interest points. Together they cross the threshold, and the lead goes to a seller that day.
What is the difference between rule-based and predictive lead scoring
Rule-based scoring uses points a person sets by hand. Predictive scoring uses a model trained on past deals to estimate how likely a lead is to buy. Predictive scoring needs a good amount of clean history; rule-based scoring works from day one and is easier to explain.
Where Pebbles Ai fits
Common questions
What is a good lead score?
There is no universal number. A good score is the threshold above which your own leads tend to turn into meetings. Set it from past deals, then check it every few months.
Is lead scoring the same as lead qualification?
No. Scoring ranks leads automatically from data. Qualification is the decision a person makes, often in conversation, about whether a lead is worth pursuing. Scoring decides who to qualify first.
Who owns lead scoring?
Usually marketing or RevOps builds the model and sales agrees the threshold. It works best when both teams review it together against real outcomes.
About this page. Written by the Pebbles Ai team for people who run sales, marketing and growth. Last updated .
