Most lead scoring models fail for the same reason: someone picked point values in a workshop, loaded them into the CRM, and never checked them against closed deals. Six months later sales ignores the score because "MQL" means someone downloaded two ebooks. This template gives you a lead scoring model you can fill in, with the structure that holds up in B2B: separate fit and behavior scores, negative points, decay, and a back-test before launch.
Copy the tables into a spreadsheet, replace the example values with your own, and run the calibration steps at the end before anyone in sales sees a number.
How the Model Is Structured
The template scores every lead on two separate dimensions:
- Fit score (0-100): does this person and their company look like the people who buy from you? Based on firmographic and role data. Changes rarely.
- Behavior score (0-100): is this person actively evaluating right now? Based on website and campaign activity. Decays over time.
Keep them separate. A single combined score lets a poor-fit lead with lots of clicks look the same as a strong-fit lead with real buying signals. If you want the reasoning behind the two-axis approach in more depth, our post on account scoring covers it at the company level, and our guide to lead scoring for high-intent B2B accounts covers the signals that predict buying.
Worksheet 1: Fit Criteria
Fill in the criteria that separate your won deals from lost ones. Points within a row are mutually exclusive (a lead gets one value per criterion). Total possible fit points should add to 100.
| Criterion | Value | Example points | Your points |
|---|---|---|---|
| Company size | Core band (e.g. 200-2,000 employees) | 30 | |
| Adjacent band (e.g. 50-199 or 2,001-10,000) | 15 | ||
| Outside target | 0 | ||
| Industry | Top-converting vertical | 25 | |
| Secondary vertical | 12 | ||
| Other | 0 | ||
| Role / seniority | Economic buyer (VP+, budget owner) | 25 | |
| Champion (director, head of function) | 20 | ||
| Practitioner (manager, specialist) | 10 | ||
| Student, job seeker, unrelated function | 0 | ||
| Region | Region you sell and support in | 10 | |
| Elsewhere | 0 | ||
| Tech stack | Uses a platform you integrate with | 10 | |
| Unknown or incompatible | 0 |
How to set the weights: pull the last 12 months of opportunities. For each criterion, compare the share of closed-won deals with the share of all leads. If 55% of wins come from your core size band but only 20% of leads do, size deserves heavy weight. If industry splits roughly the same way among wins and leads, it deserves little. Our post on building firmographic segments that convert shows this analysis step by step.
Worksheet 2: Behavior Points
Behavior points should reward buying activity, not general content consumption. Use caps so no single repeated action can carry a lead past the threshold.
| Action | Example points | Cap | Your points |
|---|---|---|---|
| Demo or contact-sales request | Route immediately (skip scoring) | n/a | |
| Pricing page view | 15 | 30 | |
| Comparison / alternatives page view | 15 | 15 | |
| Security, compliance, or integration docs view | 10 | 20 | |
| Case study view (any) | 5 | 15 | |
| Case study from the lead's own industry | +5 bonus | 10 | |
| Webinar attended (live) | 10 | 10 | |
| Gated content download | 5 | 10 | |
| Blog post view | 1 | 5 | |
| Email click | 2 | 6 | |
| Return visit within 7 days | 5 | 15 | |
| Colleague from same company active this week | 10 | 20 |
Two notes. First, hand-raisers (demo requests, "talk to sales") should bypass scoring entirely. Making a buyer wait for a score to cross a threshold after they asked for a meeting is the most expensive bug in lead management. Second, the colleague row matters more than it looks. A second person from the same company is often the earliest sign a buying group is forming.
Worksheet 3: Negative Scoring
Negative points keep the queue clean. Without them, students, competitors, and existing customers pile up at the top.
| Signal | Example points | Your points |
|---|---|---|
| Careers page view | -10 | |
| Personal email domain (gmail, outlook) on a sales-led plan | -10 | |
| Competitor domain | Set fit to 0 | |
| Existing customer domain | Route to account manager, exclude from MQL | |
| Unsubscribed from marketing email | -5 | |
| Support or login page visits only | -5 |
Worksheet 4: Decay Rules
Behavior that happened months ago should not keep a lead hot. Pick one decay rule and apply it to the behavior score only (fit does not decay).
- Short sales cycle (under 30 days): reduce behavior points by 50% every 14 days of inactivity.
- Mid-length cycle (1-3 months): reduce by 50% every 30 days of inactivity.
- Long enterprise cycle (6+ months): reduce by 25% every 30 days, and reset to zero after 180 days of no activity.
Most CRMs and marketing automation tools support time-based score reduction through workflows. If yours does not, a scheduled job that recalculates behavior from the last 90 days of events gives the same effect.
Worksheet 5: Thresholds and Actions
Combine the two scores into four routing tiers. Write the action and owner next to each tier before launch.
| Tier | Rule | Action | Owner | SLA |
|---|---|---|---|---|
| A | Fit 70+ and behavior 50+ | Route to SDR/AE as MQL | Same business day | |
| B | Fit 70+ and behavior under 50 | Account-specific nurture, personalized site experience | Weekly review | |
| C | Fit 40-69 and behavior 50+ | SDR qualification check before AE time | 2 business days | |
| D | Fit under 40 | Self-serve path or newsletter only | None |
Tier B is where the website does most of the work. Those leads fit well but are not active yet, so the job is to earn the next visit. Markettailor's segmentation lets you build each tier as a website segment and show tier B visitors industry-specific proof instead of a generic demo ask.
Calibrate Before Launch: The Back-Test
Run this before the model goes live, and again every quarter.
- Score history. Apply the model to every lead created in the last 6-12 months, using the data you had about them at the time.
- Check tier A. What share of tier A leads became opportunities? If it is not clearly higher than tier B and C, the behavior weights are off.
- Check the misses. List closed-won deals whose leads never reached tier A. Find what they had in common (often a role or industry you underweighted).
- Check volume. Count how many tier A leads per week the model would have produced. If that exceeds what your SDRs can work within the SLA, raise the threshold.
- Agree with sales. Show sales the 20 highest-scoring leads from last month. If they would not have called most of them, fix the model before launch.
Fit data is the usual weak point. Our 2026 B2B personalization research found that only 5-10% of website visitors ever fill out a form, which means a lead scoring model only ever sees a small slice of the companies researching you. Pair it with company-level visitor identification so anonymous activity from an account is counted before anyone submits a form.
Quick Checklist
- ☐ Fit and behavior scored separately, each out of 100
- ☐ Fit weights derived from closed-won vs. all-lead distributions
- ☐ Caps on every repeatable behavior
- ☐ Demo requests bypass scoring
- ☐ Negative scoring for careers, competitors, customers, personal domains
- ☐ Decay rule applied to behavior only
- ☐ Four tiers with named owners and SLAs
- ☐ Back-test run and reviewed with sales
- ☐ Quarterly recalibration on the calendar