When marketing generates hundreds of leads a month, sales cannot call them all at once. A lead scoring model ranks leads so the most promising ones get attention first, while others are nurtured until they are ready.
Fit vs behaviour
Effective lead scoring combines two dimensions:
| Dimension | Question | Example criteria |
|---|---|---|
| Fit (explicit) | Is this the kind of customer we want? | Company size, industry, location, job title, technology used |
| Behaviour (implicit) | Are they showing buying interest? | Pricing page visits, demo requests, webinar attendance, email clicks, return visits |
A perfect-fit lead with no engagement is not ready yet. A highly engaged student is not a buyer. Scoring both dimensions avoids both mistakes.
Step 1: Learn from closed deals
Before assigning points, analyse recent customers and lost deals. Which company sizes, industries and roles convert best? Which actions did buyers take before requesting a demo? This evidence makes scoring criteria credible to sales. Our ideal customer profile guide explains the fit analysis.
Step 2: Define lead scoring model criteria
Example fit scores
- Company size in target range: +20
- Target industry: +15
- Decision-maker role (director and above): +15
- Located in a served market: +10
- Student, competitor or personal email domain: −30
Example behaviour scores
- Demo or consultation request: +50 (often an automatic MQL)
- Pricing page visit: +15
- Case study or comparison page visit: +10
- Webinar attendance: +10
- Email click on high-intent content: +5
- No activity for 60 days: −15 (score decay)
Keep the first version simple. Ten to fifteen criteria are usually enough.
Step 3: Set the MQL threshold
Choose a threshold where leads are likely enough to convert that sales will accept them. Start with a threshold based on historical data, then adjust after reviewing results with sales.
MQL vs SQL
| Stage | Definition | Owner |
|---|---|---|
| Lead | Any contact who converted | Marketing |
| MQL (marketing qualified lead) | Meets fit and engagement threshold | Marketing hands to sales |
| SAL (sales accepted lead) | Sales agrees the lead is worth pursuing | Sales |
| SQL (sales qualified lead) | Sales has confirmed need, authority, budget or timing | Sales |
| Opportunity | Active deal in the pipeline | Sales |
Clear definitions prevent arguments about lead quality. Our lead handoff checklist explains the handoff process.
Step 4: Implement in your CRM
Most CRMs and marketing automation platforms support scoring. Set up:
- Fit scores from form fields and enrichment data.
- Behaviour scores from website tracking and email engagement.
- Score decay for inactivity.
- Automatic notifications and task creation when leads cross the threshold.
- Dashboards showing MQL volume and conversion rates.
Connecting website tracking, forms and email to the CRM is essential; see our CRM integration service.
Predictive lead scoring
Some platforms offer predictive lead scoring, which uses machine learning to estimate conversion likelihood from historical data. It can uncover patterns humans miss, but it needs enough historical deals to be reliable and can be hard to explain to sales. Many teams start with a rules-based model and add predictive scores later as a second signal.
Using scores beyond sales handoff
- Personalise nurture tracks for high-fit but low-engagement leads.
- Trigger retargeting for leads approaching the threshold.
- Prioritise product-qualified leads in product-led companies; see product-led growth marketing.
- Feed qualified stages back to ad platforms; see offline conversion tracking.
Lead scoring model example for a B2B agency
A digital agency selling to mid-sized B2B companies might use: target company size +20, target industries +15, marketing or executive role +15, served region +10, consultation request +50, pricing or engagement-model page visit +15, case study view +10, two or more visits in a week +10, and −20 for students, job seekers or vendors. With an MQL threshold of 60, a marketing director at a 200-person SaaS company who reads two case studies and views the engagement page in one week would cross the threshold and be routed to sales, while a student who downloads three guides would not.
Measuring lead scoring effectiveness
| Metric | What it shows |
|---|---|
| MQL to SQL conversion rate | Whether MQLs are genuinely qualified |
| Sales acceptance rate | Sales trust in the model |
| Time from MQL to first contact | Speed of follow-up |
| Win rate by score band | Whether higher scores predict revenue |
If high-scoring leads do not convert better than low-scoring ones, the model needs work.
Common lead scoring mistakes
- Scoring only behaviour, so engaged non-buyers flood sales.
- Too many criteria with arbitrary points.
- No score decay.
- No feedback loop with sales.
- Treating the model as finished after launch.
If you want a lead scoring model built on your real deal data and implemented in your CRM, contact our team.
Scoring capabilities differ between CRMs; our HubSpot vs Salesforce comparison covers two common options.
