Lead Scoring Model Template: Fit and Engagement Scores, Decay, and a Threshold Set from Closed-Won Data

A lead scoring model template ranks each lead on two separate numbers, a fit score for who the lead is and an engagement score for what the lead has done, then adds negative points, decay and a handoff threshold. Marketo's worked example sets the sales handoff at 50 points. We set the marketing qualified lead (MQL) cut from history, testing rules against the last 100 closed-won and 100 closed-lost opportunities.

What a lead scoring model template is and what it contains

A lead scoring model ranks each lead on two separate numbers. The fit score says whether the person and company look like the customers you win. The engagement score says how actively that person is acting now. Three controls sit around those numbers: negative points, decay, and a handoff threshold that turns a score into a routing decision. That is the whole template. The rest of this piece fills in the values. For the base definition, see lead scoring.

The platforms split the work the same way. Marketo uses two score types: demographic (person) score for fit and behavior score for engagement. HubSpot lets you create engagement scores, fit scores, or combined engagement and fit scores on contacts. In Salesforce Account Engagement, the score measures implicit interest in your product, and lead grading is a separate feature that sits alongside it.

The parts, in order

• Negative points. HubSpot criteria points can either be added to or subtracted from the score. The Trailhead example subtracts points for job seekers: View Jobs Opportunity Page | Decrease Score by 10 points.
• Caps. HubSpot's score limit is the maximum number of points for the overall score. Group limits cap each bucket of criteria separately.
• Decay. In Marketo, you apply decay with a smart campaign that subtracts points after a defined period of inactivity. Account Engagement defines it as reducing a prospect’s score for inactivity.
• Threshold. This is the score at which a lead becomes marketing qualified, which is the MQL line.

A worked example

HubSpot's own example uses an overall score limit of 100. A sales group is capped at 60 points: 15 for a started call, 10 for a booked meeting and 20 for a completed meeting. A marketing group is capped at 40 points: 6 for a call to action (CTA) click, 2 for an email open and 5 for an email link click.

A contact who books one meeting earns 10 sales points. Two CTA clicks, one open and one link click come to 12 + 2 + 5 = 19 marketing points. Our breakdown of that 19 is illustrative. HubSpot reports the overall score as 29 points. The caps matter in this model. With marketing capped at 40, email activity alone cannot reach a threshold set above 40.

What is the difference between fit and engagement scores

Fit describes who the lead is. Engagement describes what the lead has done. Every platform splits the score along these two axes, though each gives them different names, so a template that names the axes plainly travels across tools.

The "both axes" row is how we use each tool. It is not a feature claim for Marketo or Account Engagement.

What feeds each axis

In HubSpot, fit scores qualify records "based on their demographic information through property values, such as their age, job title, company size, or annual revenue." Engagement scores qualify records "based on their actions and interactions, such as visiting your website". Combined scores qualify records "based on engagement criteria and fit criteria."

Fit can also subtract points. In Marketo, a senior title at a target-industry company earns points, while a student email or an out-of-market region may lose them.

In Account Engagement, the two axes need different amounts of setup. Scoring comes enabled out-of-the-box, unlike grading, which requires setup. The default model weights activity so that a form submission is worth more than an email click. Scoring categories break up the overall value into categories to indicate a prospect's particular interests. In our reading, these are useful when one buyer could land in more than one product line.

A two-by-two routing template

Keeping the axes separate lets you route by quadrant instead of by one blended number. The Marketo framing is that a high demographic score with low behavior means a great fit who is not yet engaged, while high behavior with a low demographic score often means an interested but poor-fit contact.

Set the fit axis per segment where buyers differ. HubSpot notes that if your business has multiple regions, the qualities of a good fit customer may differ across each location and culture, requiring different fit scores. We think that holds for product lines and company-size tiers too. In our reading, one shared engagement score with segment-specific fit scores keeps the template auditable.

The scoring sheet you can copy

Copy the five blocks below into one sheet. Rows come from two platform examples: HubSpot's lead score setup guide and Adobe's Marketo Engage person scoring tutorial. Where neither source gives a point value, the cell says so. Fill it from your own closed-won review.

Block 1: fit criteria

HubSpot's example scores a company highly if its values are Northeast, 50-200 employees, and Hospitality. Marketo's example profile lists Director, Vice President, C-Level as job titles.

Block 2: engagement actions

HubSpot scores repeated actions by frequency. Its example: viewing an email Between 1 and 3 times adds two points and 4 or more times adds five points. It also scores contacts highly when they have interacted with a campaign more than five times.

The demo row follows Marketo's guidance that a demo request should go straight to sales, so its value matches the handoff threshold.

Block 3: negative criteria

List the attributes and actions that should lower a score: a competitor or student email domain, an unsubscribe, a hard bounce, a careers page visit. Give each one a negative value. The next section has a starting set.

Block 4: decay rule

Add one row per inactivity window, for example 30 and 60 days with no engagement. Each row runs when a record shows no activity across its full window, subtracts its value once per window, and touches the engagement score only.

Block 5: threshold cells

Worked check: with a threshold of 50, a demo request scores 50 and crosses the threshold on its own. A contact who views four emails scores 5 and stays well below it. That gap is intended. Once the threshold is set, track the resulting marketing qualified leads against funnel conversion benchmarks.

How negative scores and score decay keep the list honest

A score that only goes up turns into a record of everything a lead ever did. Two mechanics pull it back toward the present: negative points and decay.

Negative points for disqualifiers

HubSpot's builder lets you assign negative point values to lower a lead's score based on undesirable actions or attributes. A standard example is giving negative scores for actions that indicate lower interest (like unsubscribing from emails).

Split negatives the same way you split positives. Attribute negatives belong on the fit side: a student email domain, a competitor's domain, a country you do not sell into. Action negatives belong on the engagement side: an unsubscribe, a hard bounce, a visit to the careers page.

Decay for inactivity

With decay, leads that are inactive for a while will have their scores decreased. Not every platform ships it. In Account Engagement, if you want your scores to decrease with inactivity, you need to set up this automation on your own, using an action called "Adjust Prospect score by".

Two cautions before you build. First, score changes as a result of automation will not appear in the prospect activities section, so keep a log column in the sheet showing when decay ran. Second, editing the baseline model there is retroactive: new activity points will be applied not only to the prospect activities that are happening from now, but also activities that happened in the past. Test a change on a copy of the sheet first.

Rules to paste into the sheet

The point values below are our illustrative template. Replace them with values you set from your closed-won data.

Apply decay to engagement only. Fit describes the company, and a company's headcount does not shrink because nobody opened an email.

Worked example

A lead sits at an engagement score of 45 after a webinar. Thirty quiet days take it to 35. Sixty quiet days take it to 20. If your MQL threshold is 40, the lead dropped below it at day 30 and should leave the sales queue then, not linger as stale pipeline. One return visit to the pricing page adds points back, and the lead requalifies on current behavior.

In our reading, this is what keeps pipeline coverage honest: the count of qualified leads reflects who is active this month.

How to set an MQL threshold from closed-won history

A single threshold picked in a meeting tells you nothing about the deals you actually won. We set it from history instead. Scalarly describes the check: test the rules against your last 100 closed-won and 100 closed-lost opportunities and confirm that won deals score higher. We extend that check into the way the threshold itself gets chosen.

Scalarly also says the MQL definition should include both a minimum fit score and a minimum engagement score. So the output of this method is two cut points. It is not one total.

The five steps

1. Pull every lead tied to an opportunity that closed in the last twelve months, both won and lost.
2. Score each lead with the new sheet, using only activity recorded before the opportunity was created. Sales calls after that date inflate engagement on won deals and flatter the model.
3. Build a grid: fit cut points down the side, engagement cut points across the top.
4. For each cell, count the share of won leads that clear both cuts and the share of lost leads that fail at least one.
5. Pick the cell where both shares are high, then write it into the MQL definition.

A worked example

The numbers in this example are illustrative, not a benchmark. Say a team has 80 won leads and 400 lost leads from last year.

The middle row keeps 85% of wins and screens out 85% of losses, so this team would set fit at 30 and engagement at 20.

Compare the result with published defaults before you commit. Scalarly suggests an MQL threshold at 65 points, roughly the top 15-20% of the lead database, and Axamit reports that Adobe's general point threshold is 65. Those figures sit on their own point scales. In our reading, a gap between your history-based cut and a default means your score distribution differs from theirs. It does not mean your cut is wrong.

Rerun the grid when you review the MQL definition. Scalarly says the definition should be documented in writing, and reviewed quarterly. Each review adds a quarter of closed deals, so the cut points rest on more evidence over time. Check the won share against your win rate benchmarks to see whether the sample is large enough to trust.

Worked example with four leads scored after decay

Here is the sheet run on four example leads. The cut points are the strictest row of the threshold grid: a lead becomes an MQL only when fit is at least 40 and engagement after decay is at least 30. In this example, any action older than 30 days counts at half its points. The point values are illustrative, so swap in your own before you trust the outcome.

Totals (fit plus engagement after decay) run from 20 for Lead D to 85 for Lead C, but as the table shows, the total is not what decides.

Worked example: fit and engagement scores for four leads (after decay)

Item	Value
Lead A total	75 points
Lead B total	60 points
Lead C total	85 points
Lead D total	20 points

Source: Take the Market worked example (illustrative points)

How each score was built

Lead A: high fit, low engagement. A VP (20) at a company inside the ICP band (20) in an ICP industry (10) gives fit 50. One pricing page visit (15) and two content downloads (5 each) give engagement 25. Nothing aged out. Fit clears 40, engagement misses 30, so Lead A stays in nurture.

Lead B: fell below after decay. A VP (20) at an ICP-sized company (20) gives fit 40, exactly on the cut point. Engagement reached 35 from two pricing page visits (15 each) and one content download (5). Both pricing visits are older than 30 days and count at half, 15 points instead of 30, which leaves 20. Lead B would have qualified before decay. After it, the lead does not.

Lead C: above both cut points. A director (15) at an ICP-sized company (20) in an ICP industry (10) gives fit 45. A demo request (40) gives engagement 40, and it is recent, so nothing decays. Lead C is the only MQL.

Lead D: low fit, high engagement. The only fit attribute that matched is a personal email domain (-20). Six content downloads (5 each) and a webinar (10) give engagement 40. The activity is real, but fit sits 60 points short of the cut point.

Why the total is not the gate

Add fit and decayed engagement and Lead A totals 75, Lead B 60, Lead C 85 and Lead D 20. Lead A outscores Lead B on the combined number and still fails, because the rule tests each dimension separately. In our reading, that is the reason to keep two columns instead of one sum: a single total would have let Lead A through on title alone.

Your decay schedule decides cases like Lead B. One published template suggests you halve behavioral points at six months, zero beyond twelve. Whatever schedule you use, rerun these four rows after changing it and check that each lead's status still matches what sales would say.

Then check the model against outcomes each quarter. The same template says that if fewer than 25% converted, adjust thresholds, measured on the highest-scored leads against closed-won deals. Feed the MQL volume your thresholds produce into the reverse funnel calculator to see whether it supports the pipeline number you own.

How to know if lead scoring works

Sales trusts a score when it predicts what sales already measures: opportunities and wins. Our test compares leads above the threshold with leads below it, over one full sales cycle, on two conversion rates. Agree the pass condition with sales before the model goes live. 4Thought Marketing's overview gives the same order of work: "Align marketing and sales on score thresholds before you route any lead."

The test, step by step

1. Pick the window. Use your median cycle from first touch to close, and check it against the sales cycle length benchmarks. A shorter window undercounts wins.
2. Freeze the cohort. Take every lead created in the first month of the window and record whether it crossed the MQL threshold.
3. Route both groups. Sales works below-threshold leads that ask for contact, so the comparison stays fair.
4. Measure at the end of the window: lead-to-opportunity rate, opportunity-to-won rate, and won deals per lead for each group.
5. Apply the pass condition written into your service level agreement (SLA).

Worked calculation

These are illustrative numbers for one cycle.

Lead-to-opportunity lift is 22.0 ÷ 6.0 = 3.7x. Opportunity-to-won lift is 27.3 ÷ 13.5 = 2.0x. Won per lead lift is 6.0 ÷ 0.81 = 7.4x.

Then check coverage. Above-threshold leads produced 24 of 37 wins, or 64.9%, from 20% of the leads. If below-threshold leads had produced most of the wins, the threshold would be filtering out buyers, and the high lift would hide that.

A pass condition template

Over cycle length days, leads above threshold convert to opportunity at least X times the rate of leads below it, convert opportunity to won at no lower a rate, and account for at least Y% of won deals.

Set X and Y with sales from your own history, not from our example. The second clause matters. In our reading, a score that only lifts opportunity creation can be teaching reps to open opportunities on high scorers, which inflates the first rate without producing revenue.

If the model fails, change point values and the threshold, then rerun on the next cohort. Keep the rules readable while you iterate. In our reading, rule-based scoring stays common because it "is transparent and easy to defend to sales" (4Thought Marketing). Feed the passing rates into the pipeline velocity calculator to see what the lift means in pipeline per month.

Steps to launch the model and review it with sales

The scoring sheet and threshold mean little until sales agrees to them and the platform fires on them. Run these five steps in order, and file the output of each one so the next review starts from a record.

1. Agree definitions with sales

Book one working session before anything is built. The Marketo Engage guide suggests asking reps directly, for example "Which activities indicate a good or bad lead to you?", and then taking your closed-won analysis and asking reps to bring your findings to a workshop with some of your sales team to validate or refine your conclusions. Leave the session with three signed definitions: what counts as fit, what counts as engagement, and what makes an MQL become a sales qualified lead (SQL).

2. Build the scores in the platform

Create fit and engagement as separate score properties and enter the sheet's five blocks as rules, negatives and decay included. Then add the special cases. For trade show lists, Salesforce Account Engagement lets you add points at import by including a column with the desired bonus points and then mapping that column to Add To Score.

3. Set alerts at the threshold

A score nobody sees does not change follow-up. HubSpot’s automation features can trigger notifications to your sales team when a lead hits this threshold. Route the alert to the owning rep, not a shared inbox, and log the timestamp so response time can be measured.

4. Write follow-up terms into the service level agreement

When a lead crosses threshold with fit of at least minimum, the assigned rep makes first contact within hours and records an accept or reject reason within days. Rejected leads return to nurture with the reason attached.

Our marketing and sales SLA template has the full set of clauses.

5. Rerun the above-versus-below test every quarter

Manual models drift. Each quarter, repeat the closed-won comparison from the earlier sections and bring the result to the same sales group.

What this page does not cover

This page does not compare scoring platforms or recommend one, and it does not supply benchmark point values: the numbers in the worked examples are illustrative and should be replaced with values from your own closed-won data. It is not financial or legal advice.

Related on Take the Market: Marketing and Sales SLA Template: Stage Definitions, Response Times and Handoff Rules; Demand Generation Dashboard Template: 15 Metrics, Formulas, CRM Fields and the Weekly Review; Pipeline coverage calculator: the ratio you need from your own win rate and slippage.