Predictive Lead Scoring
Using machine learning to predict which leads are most likely to convert.
Definition
Predictive lead scoring uses machine learning to analyze historical conversion patterns and score new leads based on their likelihood to convert. Unlike rule-based scoring that requires manual configuration, predictive models automatically identify the characteristics and behaviors that indicate buying propensity.
Predictive scoring continuously improves as it learns from new data, adapting to changing patterns that would require constant manual adjustment in rule-based systems.
Why It Matters
Rule-based scoring requires constant tuning and misses non-obvious patterns. Predictive scoring automatically identifies what matters, providing more accurate prioritization without manual optimization.
For sales and marketing teams, predictive scoring improves conversion rates by ensuring the best leads get the most attention.
Examples in Practice
Predictive scoring identifies that page sequence matters more than page count, surfacing insights that improve manual scoring too.
A model trained on closed-won opportunities scores new leads, improving sales efficiency by 35% compared to rule-based scoring.
Predictive scores update in real-time as leads engage, enabling immediate response when high-potential leads take action.