Use this skill when defining ideal customer profiles, building scoring models, identifying intent signals, or qualifying leads. Triggers on lead scoring, ICP definition, scoring models, intent signals, MQL, SQL, lead qualification, BANT, and any task requiring lead prioritization or qualification framework design.
When this skill is activated, always start your first response with the 🧢 emoji.
Lead scoring is the discipline of quantifying how likely a prospect is to become a paying customer so sales teams spend time on the right people. A good scoring model combines profile fit (does the company match your ICP?) with behavioral intent (are they actively signaling purchase readiness?). This skill equips an agent to define ICPs, build point-based or predictive scoring models, weight intent signals, set MQL/SQL thresholds, implement score decay, and create a shared sales-marketing framework that drives consistent, measurable pipeline qualification.
Trigger this skill when the user:
Do NOT trigger this skill for:
Fit + intent = score - Profile fit answers "should we ever sell to this company?" Intent answers "should we reach out right now?" Neither alone is sufficient. A perfect-fit company with zero intent is a nurture candidate. High intent from a poor-fit company wastes sales cycles. Weight both dimensions and require minimum thresholds on each, not just a combined total.
Decay scores over time - A lead who downloaded a whitepaper six months ago and has not engaged since is not still a hot prospect. Apply time-based decay to behavioral scores so inactivity reduces urgency. Fit scores (firmographic, technographic) typically do not decay; behavioral scores should decay 10-25% per month of inactivity.
Align sales and marketing on definitions - "Marketing Qualified Lead" means nothing if sales uses a different threshold to decide whether to work it. Define MQL, SQL, and PQL in a shared document, tie them to specific score thresholds, and measure SLA compliance. Misalignment here is the single largest source of pipeline leakage.
Start simple, iterate often - Begin with a manual point model covering 8-12 attributes. Get sales and marketing to validate it on historical closed-won data before layering in predictive ML. Complexity that has not been validated destroys trust faster than simplicity.
Validate with closed-won data - Build the model, then score your last 100 closed-won deals and 100 lost/no-decision deals. If the model does not clearly separate the two populations, the attribute weights are wrong. Recalibrate before deploying to live pipeline.
Demographic vs. behavioral scoring are the two axes of every lead score. Demographic (also called profile or fit) scoring assigns points based on static