Lead scoring
Lead scoring is the practice of assigning a numeric value to each lead based on how well they fit your ideal customer profile (demographic/firmographic fit) and how engaged they are (behavioral signals like email opens, site visits, demo requests). The score ranks leads so sales works the hottest ones first.
How it works
You assign points for fit attributes (right title, right company size) and for behaviors (opened a pricing page, replied, booked). Scores accumulate and decay over time; once a lead crosses a threshold it is routed to sales as sales-ready.
Why it matters
Reps have finite hours. Scoring focuses them on the leads most likely to convert and gives marketing an objective, shared definition of when a lead is ready to hand off — reducing the friction and disputes at the marketing-to-sales boundary.
What does a simple points-based scoring model look like?
Split it into fit and engagement. For fit, give points for attributes that match your ICP: say 20 for the right industry, 15 for the right headcount band, 10 for a senior title in the buying function, and minus 30 for a disqualifier such as a student email or a competitor domain. For engagement, give points for actions that signal interest: 25 for a reply, 20 for a pricing page visit, 5 for a click, and nothing for an open.
Then set a threshold for handoff, such as 60, and look at which leads cross it. Keeping the model small, with fewer than ten rules, makes it possible to explain why any lead has the score it has. What is lead scoring covers how teams extend this.
Why should email opens carry little or no weight?
Because they are unreliable. Apple Mail Privacy Protection preloads images for many users, which registers an open whether or not a person read the message, and some corporate security scanners do the same. A lead can collect a string of opens without ever seeing the email. Clicks are affected by scanners too, though less often.
Replies, meetings booked, calls answered and visits to high-intent pages such as pricing are far better signals. If your scoring model gives meaningful points for opens, a few leads with privacy-protected inboxes will look hotter than a lead who replied asking for a demo. Email open tracking explained covers the mechanics.
How do you know if the scoring model is working?
Check the conversion rate of scored leads by band. Say leads scoring above 60 convert to opportunities at 18% and leads scoring 30 to 60 convert at 6%: the model separates good leads from average ones. If both bands convert at roughly the same rate, the score is not predicting anything and reps are right to ignore it.
Look also at the opportunities that came from low-scored leads. If a large share of closed deals started with a score under the threshold, some important signal is missing from the model. Review it every quarter with the reps who work the leads; they usually know which signals mean something.
Should lead scores decay over time?
Engagement points should. A pricing page visit from last week says more than one from eight months ago, so many models subtract engagement points after 30 to 90 days of inactivity while keeping fit points unchanged. Without decay, old leads accumulate scores and crowd out newer, warmer ones in a rep's queue. Autocloz keeps opens, clicks, replies and calls on one activity timeline per contact, and its analytics reports read from the same records, which is the raw material a decaying model needs.
How Autocloz handles it
Autocloz captures the engagement signals lead scoring depends on — opens, clicks, replies and calls across every channel — in one activity timeline, so fit and behavior data live together on the same contact record.
Free tools for this
No signup required — they run in your browser.
FAQ
What is the difference between fit and engagement in lead scoring?
Fit (or demographic/firmographic) scoring measures how closely a lead matches your ICP; engagement (or behavioral) scoring measures their actions and intent. Strong models combine both — a high-fit, high-engagement lead is the priority.
Do I need AI for lead scoring?
No. Rule-based point systems work well and are transparent. Predictive (AI) scoring can find non-obvious patterns at scale, but it needs enough historical data to train on and should still be validated against real conversion.
Related terms
Lead enrichment is the process of automatically adding missing data to a lead or company record — job title, company size, industry, verified email, phone, LinkedIn, technographics — from third-party data sources, so reps can segment, personalize and prioritize without manual research.
An Ideal Customer Profile (ICP) is a description of the company that gets the most value from your product and is easiest to win and retain — defined by firmographics like industry, company size, revenue, geography and technology stack. It targets accounts (the company), distinct from a buyer persona, which describes the individual within the account.
A Marketing Qualified Lead (MQL) is a lead that has shown enough interest and fit — through behaviors like downloading content, attending a webinar or repeated site visits — that marketing deems it worth passing to sales for follow-up. It is more engaged than a raw lead but not yet vetted by a salesperson.
Intent data is behavioral information that signals a company or person is actively researching a product, category or solution — indicating they may be in-market to buy. It includes first-party signals (activity on your own site and content) and third-party signals (content consumption and search behavior across a data provider's network of sites).