Lead scoring
Lead scoring is the practice of assigning numerical values to prospects based on their profile attributes and behavioural signals, producing a cumulative score that indicates how likely they are to become a sales opportunity. A lead score is built from two types of signals: demographic or firmographic fit (how well the lead’s characteristics match your ideal customer profile) and behavioural engagement (what actions they have taken, such as visiting specific pages, opening emails, downloading content, or requesting a demo).
Lead scoring models are typically configured in a marketing automation platform or CRM. Positive scores are assigned for qualifying attributes and high-intent behaviours; negative scores may be applied for disqualifying attributes such as company size below your threshold, or negative signals such as unsubscribing from email. When a lead reaches a defined score threshold, they are automatically designated as a marketing qualified lead and routed to sales for follow-up. This automation removes the need for manual lead review at scale.
For B2B marketing teams, lead scoring is the bridge between raw lead volume and qualified pipeline. Without scoring, sales teams receive all leads equally and must manually assess each one, wasting time on poor-fit prospects. With scoring, the highest-intent, best-fit leads surface automatically. The key to effective lead scoring is calibrating the model against actual sales data: which attributes and behaviours genuinely predict that a prospect will become a customer? This should be reviewed and refined regularly as sales outcome data accumulates.
What is the difference between demographic scoring and behavioural scoring?
Demographic (or firmographic) scoring assigns points based on how well a lead’s profile attributes match your ideal customer: positive scores for target job titles, company sizes, and industries; negative scores for students, competitors, or companies outside your target range. Behavioural scoring assigns points based on actions that indicate buying intent: visiting the pricing page scores higher than reading a blog post; attending a demo request scores higher than downloading an introductory guide. Both dimensions together give the most accurate picture of a lead’s readiness.
How do you set lead scoring thresholds?
The MQL threshold should be set empirically based on conversion data rather than arbitrarily. Start by analysing your historical data: at what score level do leads most commonly convert into sales opportunities and customers? Set your threshold at or near that level. If you have no historical data to work from, start with a provisional threshold, review conversion rates after a few months, and adjust the threshold and scoring weights to improve accuracy. Lead scoring is an iterative model that improves with data.
What signals should increase a lead score?
High-value positive signals include: visiting the pricing page, requesting a demo or trial, viewing multiple product pages in a session, attending a webinar, opening and clicking multiple emails in a short period, spending significant time on the site, filling in a contact form, and downloading high-intent content such as a comparison guide or ROI calculator. Lower-value signals include opening a single email or reading a blog post. Each signal’s weight should reflect how much it predicts actual purchase intent based on your own conversion data.