Lead Scoring

Technical explanation

Rules-based or predictive models combine fit, engagement, intent and negative signals. Scores require a defined target, reliable event data, thresholds, decay, versioning and validation against downstream conversion rather than arbitrary activity counts.

Business relevance

Well-governed scoring can focus sales effort, trigger appropriate nurturing and make qualification criteria explicit across marketing and sales.

Implementation example

A B2B model weights verified company fit and high-intent actions, applies inactivity decay and sends only leads above a validated threshold to sales review.

Limitations and common misconceptions

Scores can encode bias, reward noisy activity and drift as markets or tracking change. A score is not certainty and should not replace agreed qualification or human judgement.

Topics

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