B2B lead scoring template you can adapt
Seventeen criteria, negative points and sample thresholds to sort your leads into cold, warm or hot.
Lead scoring means giving each prospect points based on how well they match your target (fit criteria) and on what they do (behavioral criteria), so you know who to contact first. This template offers 17 criteria, including negative points, and thresholds to sort leads.
The points are a sample scale, not a standard. They give you a consistent starting point: adjust them using your own won and lost deals. You are free to reuse and quote this template with a link to the source.
| Criterion | Type | Sample value | Points |
|---|---|---|---|
| Industry | Fit | Industry in your ICP (e.g. B2B SaaS) | +15 |
| Company size | Fit | Headcount range of your best customers (e.g. 50 to 500 employees) | +10 |
| Contact role and seniority | Fit | Decision-maker on your topic (e.g. head of sales) | +15 |
| Location | Fit | Country or region your team covers | +5 |
| Compatible technology in place | Fit | Uses a CRM or tool your product integrates with | +10 |
| Recent growth signal | Fit | Funding round or hiring wave in the last 3 months | +15 |
| New decision-maker in role | Fit | Appointment to the target role in the last 3 months | +10 |
| Pricing page visit | Behavioral | At least one visit in the last 30 days | +15 |
| Repeat website visits | Behavioral | Several sessions over 2 weeks | +10 |
| Positive reply to a message | Behavioral | "Send me more information" | +20 |
| Content download | Behavioral | Guide, template or scoring grid downloaded | +5 |
| LinkedIn engagement | Behavioral | Comment on a post from your team | +5 |
| Demo or contact request | Behavioral | Demo form submitted | +30 |
| Out-of-target profile | Fit | Competitor, student, job applicant | -30 |
| Personal email on a B2B form | Fit | Consumer email address instead of a work address | -10 |
| Long inactivity | Behavioral | No interaction for 90 days | -15 |
| Opt-out | Behavioral | Asked not to be contacted or unsubscribed | Exclude |
| Thresholds | Example | Cold: under 30 points; warm: 30 to 59 points; hot: 60 points and above | Adjust |
How to use the template
Add up the points for every criterion a lead meets. The total places the lead in one of three tiers, and each tier maps to an action: hot leads get a call from a sales rep, warm leads enter a sequence, cold leads stay on a watch list.
Keep two separate subtotals: a fit score (fit criteria) and an intent score (behavioral criteria). A well-matched account with no intent does not call for the same action as an account showing interest while sitting slightly outside your target.
- Apply a time window to behavioral criteria: a visit from six months ago no longer counts.
- Recalculate the score automatically after each new interaction, not once a quarter.
- Score at the account level as well as the contact level: in B2B, several people make the decision.
- Handle exclusion separately: an opt-out removes the contact from all prospecting, whatever the score.
Calibrate the weights
The weights in this template are an example. The right scale is the one that separates your won deals from your lost ones, and only your own history can tell you that.
Take a sample of recent opportunities, work out the score they would have had at first contact, and check whether won deals clearly score higher than lost ones.
- A criterion that shows up as often in lost deals as in won deals tells you nothing: lower its weight or remove it.
- Adjust in small steps and one criterion at a time, otherwise you will not know what changed.
- Ask sales reps which "hot" leads turned out to be cold: their feedback reveals overweighted criteria.
- Review the template at regular intervals, and after every change of offer or target.
- Move the thresholds rather than every weight if the cold, warm, hot split does not match your team's capacity.
Avoid false positives
A false positive is a lead that scores high with no real buying intent. It wastes sales time and erodes the team's trust in scoring.
Dated, cross-checked buying signals reduce this risk: a hiring wave confirmed by several sources is worth more than a single visit. In Prosperian, every detected account gets an intent score based on these signals, and the hottest ones rise to the top of the list.
- Exclude internal traffic, partner traffic and bots from your visit data.
- Be wary of pages that attract visitors with no buying project: careers page, blog, documentation.
- Do not let a single behavioral criterion push a lead to "hot": require at least one fit criterion.
- Decay points over time so an old burst of activity does not inflate the score.
- Check hot leads before calling: a contact may have left the company since their last interaction.
Your next customers are already out there. Let the agent find them.
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