lead-scoring-qualificationlisted

# Lead Scoring and Qualification Scoring exists to answer one question: given more leads than capacity, which ones get worked first? Anything more elaborate than that is usually a points system nobody trusts and everybody overrides. The most common failure is a model with fifteen weighted attributes that produces a number correlating with nothing. Start with two dimensions and only add complexity when the data forces it. ## Two dimensions, not fifteen **Fit** — how closely they match the ICP. Mostly static, knowable before contact. **Intent** — evidence they might act soon. Mostly dynamic, from triggers and behaviour. ``` High intent Low intent ┌────────────────────┬────────────────────┐ High fit │ Work now. │ Nurture. │ │ Personalise fully, │ Sequence normally, │ │ multichannel. │ wait for a trigger.│ ├────────────────────┼────────────────────┤ Low fit │ Check the ICP — │ Don't contact. │ │ is it wrong? │ Suppress. │ └────────────────────┴────────────────────┘ ``` The bottom-left quadrant is the interesting one. When low-fit leads show high intent repeatedly, the ICP is too narrow or simply wrong, and that's worth more than the individual leads. ## Scoring fit Score against observable ICP criteria only — things you can determine before a conversation. Keep it to five or six. ``` Fit score (0-100) Company size in range