Traditional lead scoring asks: what does this company look like? Signal scoring asks: what is this company doing right now? The difference determines whether you call the right people or waste time on the wrong ones.
Traditional lead scoring assigns points based on firmographic data — company size, industry, job title, geography. A "good fit" company gets a high score regardless of whether they are actively looking or completely cold.
Signal scoring incorporates behavioral data — what the contact is actually doing. Have they opened your last three emails? Did they visit the pricing page twice this week? Did their payment come in on time for the past 12 months? Those behaviors are orders of magnitude more predictive than company size.
Not all signals are equal. A direct reply to a sales email is worth more than a single email open. A payment failure is more significant than a 30-day email open gap. Signal Engine weights signals based on their historical correlation with conversion and churn outcomes.
Scores are also time-weighted — recent signals count more than old ones. A prospect who opened an email yesterday is scored higher than one who opened the same email 60 days ago, even if the older engagement was more intense.
Scores update continuously as new behavioral events arrive. When a prospect visits your pricing page, their score updates within seconds via the webhook pipeline.
Signal scores are most valuable when they drive specific actions rather than just ranking lists:
Signal Engine gives you behavioral signal scoring, churn prediction, and revenue intelligence — built for your specific industry.
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