Lead scoring assigns points to a person or lead record according to how well they fit the target buyer and how they behave, such as opening emails, visiting a pricing page or attending a webinar, so that sales can follow up the leads most likely to buy first.
How it works
Two kinds of points are added. Explicit points reward fit: job role, seniority, company size and industry. Implicit points reward behaviour: pages visited, content downloaded, replies sent. Most models also subtract points for poor fit, let behavioural points fade with time, and pass a lead to sales when its total crosses a threshold. The scores live where the lead record lives, in a CRM or marketing automation system, because the behaviour they use is captured there.
Acme Robotics (illustrative) might add 10 points for a director title and 15 for a pricing-page visit, subtract 20 for a student email domain, and pass leads to sales at 50.
Where company data fits
A lead's company says as much about fit as the lead's own title does. Match the lead's company website to company-level attributes (the technologies it runs, its hiring momentum, its recent announcements) and score them as explicit points. Company signals change more slowly than behaviour, so they suit the fit side and the tie-break between two lively leads. Behaviour still tells you what a person is doing now, and company data tells you about the account around them.
In Fokals data
Fokals supplies company-level features for the account behind a lead, reached by domain matching: match the lead's company website to a Fokals company ID using the domain that Technology Stack carries, then add fields such as the first-seen date of a technology, the open postings in Hiring Activity or the surge flag in Intent Scores. Each feature is a fit or timing point on the company, with the dated evidence behind it.
The guide to adding company signals to lead scoring covers weights and decay, and the intent dataset describes the scores.
Related terms
Lead scoring is the person-level counterpart of account scoring. Company points rest on an ideal customer profile, and adding them to a lead record is data enrichment. Company-level timing comes from an intent surge.
Frequently asked questions
What is a good lead score?
No score is good in itself; the number means something only against outcomes. Set the threshold where leads above it convert to opportunities at a rate that sales can serve with its capacity, and revisit it when the mix of leads changes. Compare conversion by score band every quarter. If the bands do not separate converting leads from the rest, change the weights, not the threshold.
What is the difference between an MQL and an SQL?
A marketing qualified lead (MQL) is a lead whose score or behaviour meets marketing's threshold for passing to sales. A sales qualified lead (SQL) is one that sales has reviewed and accepted as worth pursuing, usually after a conversation or a check of fit and need. Lead scoring sets the MQL threshold, and the review that follows turns some MQLs into SQLs and returns the rest to nurture.
How do you stop a lead score going stale?
Let behavioural points decay, so that a visit last week outweighs one last quarter. Review the weights against outcomes each quarter and drop points that no longer separate leads that converted from leads that did not. Re-score leads when their company changes, for example after a funding round or a change of technology, because the fit half of the score moves with the company even when the person does nothing.