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Lead Scoring
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.
When to use this skill
Trigger this skill when the user:
- Needs to define or refine an Ideal Customer Profile (ICP)
- Wants to build or overhaul a lead scoring model with point values
- Asks how to identify, classify, or weight intent signals (first-party or third-party)
- Needs to set MQL, SQL, or PQL thresholds and handoff criteria
- Wants to implement score decay for aging or disengaged leads
- Asks about BANT, MEDDIC, CHAMP, or any qualification framework
- Needs to validate whether a scoring model is actually predicting conversions
- Wants to align sales and marketing on lead definitions and SLA terms
Do NOT trigger this skill for:
- CRM technical implementation or integration wiring - use a CRM/engineering skill
- General demand generation strategy unrelated to lead qualification
Key principles
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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.
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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.
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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.
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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.
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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.
Core concepts
Demographic vs. behavioral scoring are the two axes of every lead score. Demographic (also called profile or fit) scoring assigns points based on static attributes: company size, industry, job title, tech stack, geography, funding stage. These attributes describe who the prospect is. Behavioral scoring assigns points based on actions: page visits, content downloads, email opens, webinar attendance, free trial sign-ups. These attributes describe what the prospect is doing right now. Most models maintain separate fit and behavioral sub-scores and require a minimum threshold on each before routing to sales.
MQL / SQL / PQL definitions are the thresholds that gate handoffs between teams. A Marketing Qualified Lead (MQL) has crossed a score threshold indicating marketing believes it warrants sales attention. A Sales Qualified Lead (SQL) is an MQL that sales has accepted as worthy of active pursuit, typically after a discovery call confirms fit, budget, and timeline. A Product Qualified Lead (PQL) is specific to PLG motions - it is a user (not just a lead) who has reached a product activation milestone that predicts conversion, such as inviting a second user, creating three projects, or integrating with a key tool.
Intent signals taxonomy classifies signals by source and strength. First-party signals come from your own properties (website visits, docs engagement, trial usage, email clicks) and are the highest-confidence because you own the data. Second-party signals come from partner ecosystems (co-marketing events, integration marketplace installs, referral partner activity). Third-party intent signals come from vendors like Bombora, G2, TechTarget, or 6sense - they aggregate content consumption across publisher networks to surface companies researching your category. Rank signals from strongest (pricing page visit, free trial start) to weakest (single blog visit, newsletter open).
Score decay is the mechanism that reduces a lead's behavioral score over time without fresh engagement. Without decay, a lead's score only ever increases, making old engagement permanently inflate priority. Implement decay as a scheduled job (daily or weekly) that multiplies behavioral sub-scores by a decay factor (e.g., 0.9 per week of inactivity). Reset the decay clock when a new qualifying action occurs. Fit scores are not decayed because firmographic attributes do not change frequently.
Common tasks
Define an Ideal Customer Profile (ICP)
An ICP is a description of the company type (not individual) most likely to buy, retain, and expand. Build it from closed-won analysis, not intuition.
Firmographic criteria:
Industry verticals: e.g., FinTech, HealthTech, B2B SaaS
Company size (employees): e.g., 50-500
ARR / Revenue range: e.g., $5M-$50M ARR
Geography: e.g., North America, EMEA
Funding stage: e.g., Series A - Series C
Technographic criteria:
Tech stack signals: e.g., uses Salesforce + Slack (integrates well)
Competitor usage: e.g., currently on legacy tool X (displacement motion)
Infrastructure: e.g., AWS/GCP (cloud-native, not on-prem only)
Negative ICP (disqualifiers): Explicitly list company types to reject: e.g., solo-founder, pre-revenue, regulated industries you cannot serve, or geographies you do not support. These should auto-fail leads regardless of behavioral score.
Pull your last 50 closed-won deals and cluster them by firmographic attributes. The cluster with the shortest sales cycle and highest NRR is your ICP. Do not define ICP by who you want to sell to - define it by who actually bought and stayed.
Build a scoring model - point system template
A point-based model assigns values to attributes. Sum the points to produce a score from 0 to 100. Divide into a fit sub-score (0-50) and a behavioral sub-score (0-50).
Fit scoring template:
Attribute | Match | Points
-----------------------------|-------------------|-------
Industry match | Exact ICP | +15
| Adjacent | +8
| Outside ICP | 0
Company size | ICP range | +12
| One tier off | +6
Job title / seniority | Economic buyer | +10
| Champion / user | +7
| Unrelated | 0
Technographic signal | Key tech match | +8
Funding stage | ICP stage | +5
Geography | Target region | +0 (neutral)
| Excluded region | -20 (hard block)
Behavioral scoring template:
Action | Points | Decay
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