Lead scoring

Lead scoring is a way to rank prospects so a sales team spends its limited hours on the leads most likely to convert. A score combines two distinct dimensions. Fit measures how closely a lead matches your ICP, using traits like company size, industry, role, and geography. Behavior, sometimes called engagement, measures what the lead has actually done, such as visiting key pages, opening emails, attending a webinar, or engaging with your content. A high score usually requires both. A perfect-fit company that has never engaged is a cold prospect worth nurturing, while a highly engaged person at a company that will never buy is a distraction. Scoring models come in two styles. Rule-based models assign points by hand: plus 20 for a director title, plus 15 for a pricing page visit, minus 10 for a free email domain. Predictive models train on your historical closed-won and closed-lost data to find the patterns that actually predicted revenue, which removes guesswork but needs enough data to be trustworthy. Whichever style you use, the score is only as good as the data behind it, which is why enrichment usually comes first. The output is a ranked queue and, often, automatic routing: the top tier goes to sales now, the middle tier to nurture, and the bottom tier is ignored or recycled.