scoring-models

Detailed scoring templates, example models by business type, and calibration guidance.

Lead Scoring Models

Detailed scoring templates, example models by business type, and calibration guidance.

Explicit Scoring Template (Fit)

Company Attributes

| Attribute | Criteria | Points | |-----------|----------|--------| | Company size | 1-10 employees | +5 | | | 11-50 employees | +10 | | | 51-200 employees | +15 | | | 201-1000 employees | +20 | | | 1000+ employees | +15 (unless enterprise-focused, then +25) | | Industry | Primary target industry | +20 | | | Secondary target industry | +10 | | | Non-target industry | 0 | | Revenue | Under $1M | +5 | | | $1M-$10M | +10 | | | $10M-$100M | +15 | | | $100M+ | +20 | | Geography | Primary market | +10 | | | Secondary market | +5 | | | Non-target market | 0 |

Contact Attributes

| Attribute | Criteria | Points | |-----------|----------|--------| | Job title | C-suite (CEO, CTO, CMO) | +25 | | | VP level | +20 | | | Director level | +15 | | | Manager level | +10 | | | Individual contributor | +5 | | Department | Primary buying department | +15 | | | Adjacent department | +5 | | | Unrelated department | 0 | | Seniority | Decision maker | +20 | | | Influencer | +10 | | | End user | +5 |

Technology Attributes

| Attribute | Criteria | Points | |-----------|----------|--------| | Tech stack | Uses complementary tool | +15 | | | Uses competitor | +10 (they understand the category) | | | Uses tool you replace | +20 | | Tech maturity | Modern stack (cloud, SaaS-forward) | +10 | | | Legacy stack | +5 |

Implicit Scoring Template (Engagement)

High-Intent Signals

| Signal | Points | Decay | |--------|--------|-------| | Demo request | +30 | None | | Pricing page visit | +20 | -5 per week | | Free trial signup | +25 | None | | Contact sales form | +30 | None | | Case study page (2+) | +15 | -5 per 2 weeks | | Comparison page visit | +15 | -5 per week | | ROI calculator used | +20 | -5 per 2 weeks |

Medium-Intent Signals

| Signal | Points | Decay | |--------|--------|-------| | Webinar registration | +10 | -5 per month | | Webinar attendance | +15 | -5 per month | | Whitepaper download | +10 | -5 per month | | Blog visit (3+ in a week) | +10 | -5 per 2 weeks | | Email click | +5 per click | -2 per month | | Email open (3+) | +5 | -2 per month | | Social media engagement | +5 | -2 per month |

Low-Intent Signals

| Signal | Points | Decay | |--------|--------|-------| | Single blog visit | +2 | -2 per month | | Newsletter open | +2 | -1 per month | | Single email open | +1 | -1 per month | | Visited homepage only | +1 | -1 per week |

Product Usage Signals (PLG)

| Signal | Points | Decay | |--------|--------|-------| | Created account | +15 | None | | Completed onboarding | +20 | None | | Used core feature (3+ times) | +25 | -5 per month inactive | | Invited team member | +25 | None | | Hit usage limit | +20 | -10 per month | | Exported data | +10 | -5 per month | | Connected integration | +15 | None | | Daily active for 5+ days | +20 | -10 per 2 weeks inactive |

Negative Scoring Signals

| Signal | Points | Notes | |--------|--------|-------| | Competitor email domain | -50 | Auto-flag for review | | Student email (.edu) | -30 | May still be valid in some cases | | Personal email (gmail, yahoo) | -10 | Less relevant for B2B; adjust for SMB | | Unsubscribe from emails | -20 | Reduce engagement score | | Bounce (hard) | -50 | Remove from scoring | | Spam complaint | -100 | Remove from all sequences | | Job title: Student/Intern | -25 | Low buying authority | | Job title: Consultant | -10 | May be evaluating for client | | No website visit in 90 days | -15 | Score decay | | Invalid phone number | -10 | Data quality signal | | Careers page visitor only | -30 | Likely a job seeker |

Example Scoring Models

Model 1: PLG SaaS (ACV $500-$5K)

Weight: 30% fit / 70% engagement (heavily favor product usage)

Fit criteria:

Company size 10-500: +15
Target industry: +10
Manager+ role: +10
Uses complementary tool: +10

Engagement criteria:

Created free account: +15
Completed onboarding: +20
Used core feature 3+ times: +25
Invited team member: +25
Hit usage limit: +20
Pricing page visit: +15

Negative:

Personal email: -10
No login in 14 days: -15
Competitor domain: -50

MQL threshold: 60 points Recalibration: Monthly (fast feedback loop with high volume)

Model 2: Enterprise Sales-Led (ACV $50K+)

Weight: 60% fit / 40% engagement (fit is critical at this ACV)

Fit criteria:

Company size 500+: +20
Revenue $50M+: +15
Target industry: +15
VP+ title: +20
Decision maker confirmed: +15
Uses competitor: +10

Engagement criteria:

Demo request: +30
Multiple stakeholders engaged: +20
Attended executive webinar: +15
Downloaded ROI guide: +10
Visited pricing page 2+: +15

Negative:

Company too small (<100): -30
Individual contributor only: -15
Competitor domain: -50

MQL threshold: 75 points Recalibration: Quarterly (longer sales cycles, smaller sample size)

Model 3: Mid-Market Hybrid (ACV $5K-$25K)

Weight: 50% fit / 50% engagement (balanced approach)

Fit criteria:

Company size 50-1000: +15
Target industry: +10
Manager-VP title: +15
Target geography: +10
Uses complementary tool: +10

Engagement criteria:

Demo request or trial signup: +25
Pricing page visit: +15
Case study download: +10
Webinar attendance: +10
Email engagement (3+ clicks): +10
Blog visits (5+ pages): +10

Negative:

Personal email: -10
No engagement in 30 days: -10
Competitor domain: -50
Student/intern title: -25

MQL threshold: 65 points Recalibration: Quarterly

Threshold Calibration

Setting the Initial Threshold

Pull closed-won data from the last 6-12 months
Retroactively score each deal using your new model
Find the natural breakpoint — what score separated wins from losses?
Set threshold just below where 80% of closed-won deals would have scored
Validate against closed-lost — if many closed-lost score above threshold, tighten criteria

Calibration Cadence

| Business Type | Recalibration Frequency | Why | |---------------|------------------------|-----| | PLG / High volume | Monthly | Fast feedback loop, lots of data | | Mid-market | Quarterly | Moderate cycle length | | Enterprise | Quarterly to semi-annually | Long cycles, small sample size |

Calibration Steps

Pull MQL-to-closed data for the calibration period
Compare scored MQLs vs. actual outcomes:

High score + closed-won = correctly scored
High score + closed-lost = possible false positive (tighten)
Low score + closed-won = possible false negative (loosen)

Adjust weights based on which attributes actually correlated with wins
Adjust threshold if MQL volume is too high (raise) or too low (lower)
Document changes and communicate to sales team

Warning Signs Your Model Needs Recalibration

MQL-to-SQL acceptance rate drops below 30%
Sales consistently rejects MQLs as "not ready"
High-scoring leads don't convert; low-scoring leads do
MQL volume spikes without corresponding revenue
New product/market changes since last calibration