Lead Scoring
Assigning numeric values to prospects based on fit and engagement so that limited sales attention goes to the most promising ones.
Also known as: Predictive lead scoring, Lead grading
Category: Business & Economics
Tags: businesses, marketing, sales, lead-generation, processes
Explanation
Lead scoring ranks prospects so that follow-up effort is allocated by likelihood to buy rather than by arrival order. Scores usually combine two dimensions. Fit attributes describe who the contact is: company size, industry, job title, geography, technology in use, and how closely these match the ideal customer profile. Behavioural attributes describe what they have done: pages viewed, emails opened, demos requested, pricing page visits, trial activity, and how recently. Points are added for positive signals and subtracted for negative ones such as a personal email domain, a student title, or long inactivity. Simple scoring uses rules chosen by the team; predictive scoring fits a model to historical won and lost deals, which tends to be more accurate and less intuitive. The common failure is building an elaborate model on guesses and never validating it. A score is only useful if high-scoring leads convert at a measurably higher rate than low-scoring ones, so the scoring rules deserve periodic recalibration against actual outcomes.
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