Lead scoring worksheet: build and test your model before the CRM
Most lead scores are switched on first and fixed later. This worksheet lets you build the model, test it on real leads, and trust the ranking before it touches a single record.
Most lead scoring projects start in the CRM. Someone opens the scoring tool, adds points for job titles and form fills, switches it on, and finds out a month later that the top of the list is wrong. By then the score is on every record, reps have learned to ignore it, and changing it means explaining why the number moved.
A spreadsheet is a better place to start. You can build the model, run it against a sample lead, score a real list, and see whether the ranking matches how your best rep would triage, all before anything touches a live record. This worksheet walks through that in five tabs.
What the worksheet does
It builds two scores and keeps them apart.
- Fit is a score out of 10. It asks whether this is the kind of company and person you win.
- Engagement is a second score out of 10. It asks whether they are active right now, and it fades as activity gets older.
Two cutoffs on each score split leads into nine bands, from fit 7.0 and up with engagement 6.0 and up, down to fit under 4.0 with engagement under 3.0. Each band has a play. HubSpot’s lead scoring tool also keeps fit and engagement as separate scores, so whatever you settle on here carries into the CRM.
Tab 1: The fit model
Name four fit factors and give each a weight. The weights must add up to 100%, and the sheet tells you when they do not. The template starts with company size, industry, role and seniority, and whether the company runs a system your product needs.
Under each factor, list the values you actually see in your data and give each one points from 0 to 10. The best values score near 10. The rest scale down from there.
Two rules are built in:
- Unknown scores zero. A missing field adds nothing, but it does not count against the lead either. If a factor you care about is blank on half your records, fill the field before you lean on it. Validity’s State of CRM Data Management in 2025 found 76% of CRM users say less than half of their data is accurate and complete, so expect some gaps.
- Disqualifiers subtract. Competitors, current customers, students, and personal email domains each take points off. A competitor or a current customer drops to zero on fit, so they never rank as a new lead worth calling.
Set the band cutoffs last. The template starts at 7.0 and 4.0. Move them once you see how your own leads spread.
- Company size 30% 9.4
- Industry 25% 9.1
- Role and seniority 25% 8.9
- Required system in place (unknown) 20% 0
The sample lead matches closely on three factors and scores 7.3. The fourth is unknown, so it adds nothing. That is the right outcome: the lead still clears the 7.0 cutoff, and the first call has an obvious question to ask.
Tab 2: The engagement model
Each signal gets three settings:
| Setting | What it does |
|---|---|
| Weight | Its share of the engagement score. All weights add up to 100%. |
| Events for full credit | How many events earn the full weight. More than that adds nothing, so one busy visitor cannot run up the score. |
| Half-life | How many days until the credit halves. A pricing visit from last week counts. One from last quarter barely does. |
The template uses six signals: pricing, comparison, and integration page visits; bottom-of-funnel content; webinar attendance; repeat site sessions; email clicks and replies; and other people from the same company being active.
Email opens are on the list with a weight of zero. Litmus reports that over 50% of email opens happen on a device with Apple’s Mail Privacy Protection turned on, which loads tracking pixels whether or not anyone reads the email. Clicks and replies carry the email signal instead.
The scoring rule for each signal is short:
Credit = the smaller of events or the full-credit number, divided by the full-credit number, times 0.5 raised to the power of days since the most recent event divided by the half-life.
One pricing visit 15 days ago, with a cap of two and a 30-day half-life, earns 0.35 credit. At a 35% weight, that adds about 1.2 to an engagement score out of 10.
The engagement cutoffs start at 6.0 and 3.0.
Tab 3: Test a lead
Before scoring a list, pick values for one lead and read the result. The tab shows the points behind every factor and signal, the two scores, the band each one falls in, and the play.
Test the edge cases on purpose. A perfect-fit lead with no activity should land at fit 7.0 and up with engagement under 3.0, not at the top of the call list. A student who downloaded everything should never clear the upper fit cutoff. A competitor should score zero on fit however active they are. If any of those come out wrong, go back to tabs 1 and 2.
Tab 4: Score your leads
Paste in a list, one lead per row. The fit columns use dropdowns fed by tab 1, so the values always match. For each engagement signal, enter the number of events and the days since the most recent one. Fit score, fit band, engagement score, engagement band, and play all calculate on the right.
Then read the list the way a rep would. Sort by fit score and look at the top 20 and the bottom 20. If the order does not match your gut about who is worth a call, the gut is usually spotting a factor the model is missing. Add it, reweigh, and run the list again.
Tab 5: Plays by band
Every band gets a play, and you can edit them to fit your team:
| Engagement 6.0 and up | Engagement 3.0 to 5.9 | Engagement under 3.0 | |
|---|---|---|---|
| Fit 7.0 and up | Rep outreach the same day, referencing what they looked at | Rep outreach within 48 hours with a specific offer | Stay on the shortlist with ads and useful content. Alert the owner when engagement climbs |
| Fit 4.0 to 6.9 | Qualify fast, through a short call or a self-serve path | Qualify through a self-serve path or a light sequence | Nurture. No rep time yet |
| Fit under 4.0 | Check the fit data before writing them off, then route to self-serve | Self-serve or a lighter offer | Leave them alone |
The ranges in the table follow the cutoffs you set on tabs 1 and 2.
One rule sits above the table: a demo request routes on the spot, whatever its band. The band decides the order, not whether someone gets a reply.
From spreadsheet to CRM
Once the ranking holds up, move it into your CRM. In HubSpot, the lead scoring tool supports fit, engagement, and combined scores, with decay on engagement, so the weights, cutoffs, and half-lives from this worksheet map across. HubSpot shows its own labels for bands. Keep your numeric cutoffs as the reference.
Keep the spreadsheet as the record of why the model looks the way it does. Every quarter, pull the leads from that quarter and check conversion by band. Leads with fit 7.0 and up and engagement 3.0 and up should convert clearly better than everything else. If they do not, change one or two weights, write down why, and check again next quarter.