Lead Scoring Model Template

Use this template when building or revising your lead scoring model. A good scoring model separates high-intent, high-fit leads from noise so your SDR team spends time on the right prospects. Build this in your marketing automation platform (HubSpot, Marketo, Pardot) and sync scores to your CRM for routing and prioritization.

Scoring Model Structure

Lead scores are made up of two independent components. Both matter, and they should be tracked separately, not combined into a single number.

Total Score = Fit Score (demographic/firmographic) + Engagement Score (behavioral)

Why separate scores? A VP of Sales at a $50M company who visited your pricing page once is different from a marketing intern at the same company who downloaded 8 ebooks. The first has high fit, low engagement. The second has low fit, high engagement. Neither is an MQL yet, but for very different reasons.

Part 1: Fit Score (0-50 points)

Fit scoring evaluates whether the lead matches your ideal customer profile. This score does not change based on behavior — it is static unless the lead’s profile data updates.

Job Title / Seniority (0-15 points)

Criteria	Points	Examples
Decision maker (C-level, VP)	15	CRO, VP Sales, VP Marketing, CMO
Senior influencer (Director, Head of)	12	Director of RevOps, Head of Growth
Mid-level practitioner	8	SDR Manager, Marketing Manager, RevOps Lead
Junior / IC	3	SDR, Marketing Coordinator, BDR
Irrelevant title	0	Student, Intern, Consultant (unless target)
Unknown title	2

Company Size (0-10 points)

Adjust these ranges to match your ICP:

Employee Count	Points	Rationale
50-200 (mid-market sweet spot)	10	Core ICP
201-1000	8	Strong fit, longer sales cycle
20-49	5	Can convert but lower ACV
1000+	5	Enterprise — needs different motion
Under 20	1	Likely too small
Unknown	3

Industry (0-10 points)

Industry	Points	Rationale
SaaS / Technology	10	Primary vertical
Financial Services	8	Strong use case, proven results
Healthcare Tech	7	Growing vertical
E-commerce	5	Moderate fit
Non-profit / Education	1	Low fit, long procurement cycles
Unknown	3

Geography (0-5 points)

Region	Points
North America	5
Western Europe	4
ANZ	3
Other	1

Technology Stack (0-10 points)

Check for technologies in the lead’s stack that indicate fit:

Technology	Points	Rationale
Salesforce	5	Core integration, high intent signal
HubSpot (Marketing Hub)	4	Indicates marketing sophistication
Outreach or Salesloft	4	Indicates outbound motion exists
Using a competitor	3	Aware of the category
No relevant technology detected	0

Maximum technology points: 10 (cap to prevent over-scoring)

Use data enrichment tools to fill in firmographic and technographic data automatically.

Part 2: Engagement Score (0-50 points)

Engagement scoring measures how actively a lead is interacting with your brand. This score changes over time and includes decay rules.

Website Behavior (0-20 points)

Action	Points	Decay
Visited pricing page	10	Resets after 30 days
Visited product/feature pages (3+)	5	Resets after 14 days
Visited case studies	4	Resets after 14 days
Visited careers page	-5	They might be job hunting, not buying
Visited blog post	1 (max 5)	Resets after 30 days
Visited comparison page (vs. competitor)	8	Resets after 14 days
Returned to site 3+ times in 7 days	5	Weekly reset

Content Engagement (0-15 points)

Action	Points	Decay
Downloaded gated asset (ebook, guide)	5	Resets after 30 days
Attended webinar (live)	7	Resets after 30 days
Registered for webinar but did not attend	3	Resets after 14 days
Watched product demo video (> 50%)	6	Resets after 14 days
Downloaded template	4	Resets after 30 days

Email Engagement (0-10 points)

Action	Points	Decay
Opened email	1 (max 3)	Rolling 30 days
Clicked email link	3	Rolling 30 days
Replied to email	5	Rolling 30 days
Unsubscribed	-10	Permanent

Direct Intent Signals (0-15 points)

Action	Points	Decay
Requested demo / contact sales	15	No decay — immediate MQL
Started free trial	12	Resets after 30 days
Visited pricing page + demo page in same session	10	Resets after 7 days
Asked question in chat	5	Resets after 14 days

MQL Threshold Matrix

A lead becomes an MQL when it crosses BOTH a fit threshold and an engagement threshold:

	Engagement < 15	Engagement 15-29	Engagement 30+
Fit < 20	Nurture	Nurture	Monitor (high activity, low fit)
Fit 20-34	Nurture	Review	MQL
Fit 35+	Fast Track Nurture	MQL	MQL (Priority)

MQL actions by category:

Category	Action	SLA
MQL (Priority)	Route to SDR immediately. Call within 5 minutes.	< 5 minutes
MQL	Route to SDR. Call within 1 hour.	< 1 hour
Review	RevOps reviews weekly. May manually qualify or return to nurture.	Weekly review
Monitor	Add to targeted nurture campaign. Alert SDR if fit improves.	Automated
Nurture	Standard nurture email sequence. No SDR action.	Automated
Fast Track Nurture	High-fit leads that haven’t engaged yet. SDR proactive outreach.	Within 24 hours

Score Decay Rules

Engagement scores must decay over time. A lead who downloaded an ebook 6 months ago is not the same as one who downloaded it yesterday.

Time Since Last Activity	Score Adjustment
14 days inactive	-5 points from engagement
30 days inactive	-15 points from engagement
60 days inactive	Reset engagement to 0
90 days inactive	Move to recycled status

Run decay calculations daily in your marketing automation platform.

Validation & Calibration

Review your scoring model monthly for the first quarter, then quarterly:

Check	How	Target
MQL → SQL acceptance rate	Track how many MQLs sales accepts	> 30%
MQL volume vs. capacity	Compare MQL count to SDR team capacity	15-25 MQLs per SDR per week
Score distribution	Histogram of all lead scores	Should be a normal curve, not clustered at extremes
False positive rate	MQLs that sales rejects — review the top 5 each month	Identify scoring rules to adjust
False negative rate	Closed-won deals where the lead never scored as MQL	< 10% of deals

Feed scoring performance data into your GTM metrics dashboard and discuss trends in your weekly GTM report.

How to Customize

• For PLG companies, add a Product Engagement category (0-20 points) that tracks in-app behavior: features used, integrations connected, team members invited, usage frequency. Product-qualified leads (PQLs) often outperform marketing-qualified leads on conversion rate, so weight product signals heavily.
• For ABM-focused teams, add an “Account Tier” modifier to the fit score. Tier 1 target accounts get a 10-point bonus, Tier 2 gets 5 points. This ensures that even moderate engagement from a strategic account gets flagged for sales attention. Reference the ABM approach for account tier definitions.
• For high-volume inbound (500+ leads/month), raise your MQL thresholds to prevent SDR overload. It is better to have fewer, higher-quality MQLs than to flood the team with marginal leads. Track your lead generation metrics to find the right threshold.