Lead scoring model template
Most lead scoring models are a points table someone built in a workshop and never tested against revenue. They reward activity (webinar attendance, email opens) because activity is easy to count, and sales learns to ignore the MQL flag within a quarter. This template separates fit from engagement, adds decay and negative scoring, and makes you back-test the threshold against deals you have already won before anyone sees an MQL.
- Category:
- CRM & Data Hygiene
- Formats:
- PDF + CSV + web view
- Sections:
- 6
- Updated:
What you get
- A fit scoring table (who they are) and an engagement scoring table (what they did), scored separately
- Negative scoring rules for competitors, students, job seekers, and unsubscribes
- A decay schedule so a download from six months ago stops counting
- Worked examples that show how the math produces an MQL or not
- A back-test sheet that compares your threshold against closed-won and closed-lost leads
Who it's for
- Marketing ops managers rebuilding an MQL definition sales ignores
- RevOps leads aligning marketing and sales on what counts as qualified
- Demand gen teams that need to prove lead quality, not just volume
What's inside
- 1
Fit scoring (maximum 50 points)
4 columns, 6 worked example rows
- 2
Engagement and negative scoring
4 columns, 9 worked example rows
- 3
Decay and threshold rules
5 fields to complete
- 4
Worked examples
6 columns, 4 worked example rows
- 5
Back-test against closed-won
6 columns, blank working sheet
- 6
Why scoring models drift into noise
Guidance notes
Preview of section 1
Fit scoring (maximum 50 points)
Point values are an example. Replace them with values derived from your own closed-won analysis. Only the highest-matching row per attribute counts.
| Attribute | Value | Points | Evidence from closed-won |
|---|---|---|---|
| Company size | 200 to 2,000 employees | 20 | |
| Company size | 50 to 199 employees | 10 |
The preview shows part of section 1. The full template has all 6 sections (5 not previewed here), with blank rows ready to fill in. Download the full template
How to use it
- 1
Build fit from closed-won, not from opinion
Pull the last 12 to 24 months of closed-won deals and look at the company size, industry, and buyer titles that actually appear. Points go to attributes that show up in wins at a higher rate than in your overall database. If an attribute does not separate wins from everything else, it gets zero points no matter how much someone likes it.
- 2
Score fit and engagement separately
A perfect-fit account with no engagement and a student who downloaded everything can reach the same total. Keep two scores and require a minimum on fit for MQL. Most CRMs and marketing automation platforms support two score fields; use them.
- 3
Weight intent over activity
A pricing page visit or a demo request says more than five email clicks. Give high-intent actions most of the engagement points and cap low-intent actions (email clicks, blog visits) so they cannot push a lead over the threshold by volume alone.
- 4
Back-test before launch
Score last year's leads with the new model and check what share of eventual closed-won leads would have crossed the threshold, and what share of all MQLs would have been junk. Adjust the threshold until the trade-off is one sales will accept, then write that trade-off down.
- 5
Review conversion by score band every quarter
Track MQL-to-opportunity conversion for each score band. If leads scoring 60 to 70 convert no better than leads at 40 to 50, your points are not separating anything and need rework.
Frequently asked questions
What is a good lead scoring model?
One that separates fit (who the lead is) from engagement (what they did), uses negative scoring and decay, and has a threshold that was back-tested against leads that actually became closed-won. If it cannot be shown to predict conversion, it is a points table, not a model.
What score should qualify an MQL?
There is no standard number. The threshold depends entirely on your point values. Pick it by back-testing: score historical leads and choose the threshold that captures most eventual wins at an MQL volume sales can work.
How does lead score decay work?
Engagement points lose value as they age, so a download from six months ago stops counting as current interest. A simple approach is to halve engagement points after 30 days and zero them after 90. Fit points do not decay unless the underlying data changes.
Should demo requests go through lead scoring?
Demo and contact-sales requests should route to sales right away, usually with a minimum fit check to filter out students and competitors. Making a hand-raiser wait for a score threshold costs you the fastest-converting leads you have.
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