How does lead scoring actually work?

I’m trying to understand how modern lead scoring systems really function beyond the basic “opens + clicks = points” model.

From what I’ve learned so far, lead scoring usually combines:

• Demographic fit (industry, role, company size)
• Behavioral signals (opens, replies, site visits)
• Intent data (timing, engagement depth)
• Negative signals (bounces, inactivity, unsubscribes)

But I’m curious how people are structuring it in real workflows.

Are you using rule-based scoring, predictive models, or something hybrid?

I’ve seen platforms like oppora.ai incorporate engagement + reply signals directly into pipeline tracking, which seems more actionable than static scoring.

Would love to hear how you’re doing it in practice.