Lead scoring
lead qualification scoringLead scoring means ordering leads by purchase likelihood by assigning points for attributes and behaviour. When the points cross an agreed threshold, the lead moves to sales. That way the seller’s time goes to the ripest.
Good to know
Points accrue from two sources: fit, whether the lead looks like your ideal customer (industry, role, size), and activity, what the lead does (pricing views, opened messages, returning visits). Both must weigh: an active wrong buyer and a passive right one are both bad deals.
The model lives on data: the first version is an educated guess corrected by realised deals. Quarterly, check whether the points predicted sales and adjust the weights. A marketing automation tool computes the scores and triggers the handovers; without one, scoring works as a light manual list too.
IN PRACTICE
Building the scoring model
Fit points
Ideal customer traits score plus, disqualifiers minus: a student or a wrong country drops points. Based on customer history.
Activity points
Buying signals weigh: pricing and contact pages more than the blog. Points decay so old activity does not mislead.
Thresholds and handovers
The MQL line triggers nurture, the SQL line a handover to sales within an agreed response time. The lines are agreed with sales.
PITFALLS
Two scoring mistakes
The model is never calibrated
A model of guessed weights steers sellers wrong for years. Realised deals are the only true teacher.
Activity without fit suffices
A curious student scores like a buyer. Without fit weighting, sales calls the wrong people.
FREQUENTLY ASKED
Frequently asked questions
Does scoring require an automation tool?+
At larger volumes yes: computing and handovers need automation. At tens of leads a month, a light model built into CRM fields works too.
Where do I start the model?+
Study your last 20 won and 20 lost deals: which traits and actions separated them. Those become the first weights.
What is a good MQL threshold?+
One at which sales can handle the handed-over leads well. Too low floods sales, too high wastes deals: tune the line by response time.
Can AI handle scoring?+
Predictive scoring learns from deals automatically and beats a hand-tuned model once data reaches hundreds of deals. With less data, a rule model is more transparent and better.
How do negative points work?+
Disqualifying signals subtract: a competitor’s domain, a student email or careers-page browsing. They save the seller’s time for the right ones.
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Maintained by the VSCY editorial team · Updated 6 Aug 2026 · Part of the marketing glossary (99 terms)
