# Lead Scoring
**Source:** https://glossary.keenfunnel.com/terms/lead-scoring
**Language:** German

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## Technische Erklärung

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.

## Geschäftliche Relevanz

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

## Implementierungsbeispiel

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.

## Einschränkungen und häufige Missverständnisse

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.

## Themen

Revenue Operations Data Engineering Intelligente Automatisierung KI-Governance

## Quellen

HubSpot — Lead Scoring Explained — NIST — AI Risk Management Framework — https://www.nist.gov/itl/ai-risk-management-framework

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