Lead Scoring Model
A lead scoring model is the explicit set of rules and weights that produces a lead score — the formula behind "this lead is an A, that one is a C."
What is a lead scoring model?
A lead scoring model is the documented formula that turns lead attributes and behaviors into a score. Models can be simple weighted-sum rules ("title = VP +15, role = engineer −10, visited pricing +20, free-mail address −10") or full ML models trained on historical conversions.
Why it matters
- The model is the place where marketing and sales formally agree on what a "good" lead looks like
- A bad model wastes everyone's time; a good one focuses every downstream effort
- Models drift — without quarterly review, they decay as your ICP and market evolve
Rule-based vs. ML vs. AI
- Rule-based: explicit, transparent, easy to change; doesn't catch nuance
- ML: trained on historical conversion; catches patterns; needs enough data and ongoing retraining
- AI / LLM: reads unstructured signals (job description, website, social posts); explainable rationale per score; new in 2026 and rapidly becoming standard
How TexAu helps
AI Column is a working LLM-driven scoring engine — define the rubric in plain language, apply at scale, and audit the rationale for every score, no ML pipeline required.
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