Lead scoring template
Lead scoring answers one question: who do I work first? Score fit and engagement separately so a high-interest bad-fit lead does not jump the queue.
Start simple and tune from real conversion data.
Short answer
A lead scoring model ranks leads by combining fit (how well they match your ICP) and engagement (how much interest they show). Scoring both dimensions tells reps who to call first. Below is the template with example point values and the thresholds for marketing- and sales-qualified leads.
Key takeaways
- Score fit and engagement separately so a bad-fit lead cannot jump the queue.
- Use negative points for disqualifying signals, not just positive ones.
- Set clear MQL and SQL thresholds to hand off leads consistently.
- Tune the model from real conversion data over time.
Example scoring model
Fit points plus engagement points.
| Signal | Type | Points |
|---|---|---|
| Title matches buyer persona | Fit | +20 |
| Company size in ICP range | Fit | +15 |
| Industry in ICP | Fit | +10 |
| Out-of-ICP industry | Fit | -15 |
| Requested a demo | Engagement | +30 |
| Opened 3+ emails / visited pricing | Engagement | +15 |
| No activity in 30 days | Engagement | -10 |
Thresholds and use
Turn scores into action.
- MQL: fit is acceptable and engagement crosses a set bar (for example, 40+ total).
- SQL: strong fit plus a high-intent action like a demo request.
- Route the highest fit-plus-engagement leads to reps first; nurture the rest.
Generate and send this with Ardovo
You do not have to start from a blank page. Tell Rook, Ardovo's AI operator, what you need (for example, "lead scoring template") and it drafts a version grounded in your real pipeline data, personalizes it for the specific contact or deal, and can send it or attach it to the record in one step. Every template lives in one shared library, so your whole team stays on message and nothing gets rewritten from scratch.
Frequently asked questions
What is lead scoring?
A method of ranking leads by combining fit (match to your ICP) and engagement (interest shown), so reps work the most promising leads first. Scoring both dimensions prevents a high-interest but poor-fit lead from being over-prioritized.
What signals should a lead scoring model use?
Fit signals like title, company size, and industry, plus engagement signals like demo requests, pricing-page visits, and email opens. Include negative points for disqualifiers such as out-of-ICP industry or long inactivity.
What is the difference between an MQL and an SQL?
A marketing-qualified lead has enough fit and engagement to warrant sales attention; a sales-qualified lead has been vetted by a rep as a real opportunity worth pursuing. Clear score thresholds keep the handoff between the two consistent.