Lead scorecard: a free template to score any lead in two minutes
A one-page card that rates a lead on who they are and what they did recently, each out of 10, with a rating key, a worked example, and the next step for every result.
A lead scorecard is a one-page form for rating a single lead on two things: how well they fit the customers you win, and how active they have been recently. Each factor gets a rating from 0 to 10, each rating has a weight, and the card adds them up into a fit score and an engagement score, both out of 10. The pair of scores points to a play: call now, follow up this week, nurture, or leave alone.
This guide walks through the scorecard line by line, with a rating key, a filled-in example, and the plays for every result. The worked example on this page shows the math in full, and a downloadable Excel version that does it for you is being added.
Lead scorecard vs. lead scoring model
The two get confused, and they do different jobs.
| Lead scorecard | Lead scoring model | |
|---|---|---|
| What it scores | One lead at a time, by a person | Every lead, automatically |
| Who fills it in | A rep, SDR, manager, or marketer | Nobody; it reads CRM data |
| Time per lead | About two minutes | None once it is built |
| Best for | Triage, handoff reviews, pipeline reviews, agreeing on definitions | Ranking a whole database, routing, and workflows |
| Main risk | Ratings drift from person to person | Wrong weights, applied to everyone |
Most teams need both, in that order. The scorecard is where sales and marketing agree on what matters. The model is how that agreement gets applied to every record.
The lead scorecard
Part 1: Fit (who they are)
Fit uses stable facts about the person and the company. Weights add up to 100%.
| Factor | Weight | What a 9 or 10 looks like |
|---|---|---|
| Company size | 30% | In the size range where most of your wins sit |
| Industry | 25% | A core industry for you |
| Role and seniority | 25% | The person who owns the problem and can buy |
| Systems they run | 15% | Runs the exact system your product needs |
| Disqualifier (subtracts) | 5% | A clear competitor, student, or job seeker |
The disqualifier works in reverse. A 0 means nothing disqualifying. Anything higher subtracts from the fit score, so a competitor who reads everything can never look like your best lead.
Part 2: Engagement (what they did recently)
Engagement uses recent activity, and the rating key builds recency in: activity from last week rates high, activity from last quarter rates low. Weights add up to 100%.
| Signal | Weight | What a 9 or 10 looks like |
|---|---|---|
| High-intent page visits | 30% | Pricing or comparison pages more than once in the last 7 days |
| Direct response | 30% | A demo request or meeting booked in the last 7 days |
| Events and webinars | 15% | Attended live in the last 30 days and asked a question |
| Others at the company active | 15% | Three or more other people active in the last 30 days |
| Content and repeat visits | 10% | Several return visits or bottom-of-funnel reads this week |
Email opens are not on the card. Litmus reports that over 50% of email opens come from devices with Apple Mail Privacy Protection turned on, which loads tracking pixels whether or not anyone reads the email. Replies and meetings carry the email signal instead.
The rating key
Ratings only mean something if two people would give the same lead the same number. The key keeps everyone calibrated. Half points are fine.
| Factor or signal | 9 to 10 | 6 to 8 | 3 to 5 | 0 to 2 |
|---|---|---|---|---|
| Company size | Where most of your wins sit | Just outside that range | Well outside, with a few wins | Far outside, or unknown |
| Industry | Core industry | Adjacent, with some wins | Rarely won | Not served, or unknown |
| Role and seniority | Owns the problem and can buy | Senior influencer | User or junior team member | No role in the purchase |
| Systems they run | The exact system you need | A close alternative you support | Makes adoption harder | Just bought a competitor |
| Disqualifier | Competitor, student, job seeker | Existing customer, or outside your market | Personal email, company identifiable | None (rate 0) |
| High-intent page visits | Last 7 days, more than once | Last 30 days | Last 90 days | None, or older |
| Direct response | Demo or meeting, last 7 days | Replied to a rep, last 30 days | Replied, last 90 days | None |
| Events and webinars | Attended live and engaged, last 30 days | Attended, last 30 days | Attended or watched, last 90 days | None |
| Others at the company active | Three or more, last 30 days | One or two, last 30 days | Others, last 90 days | Nobody else |
| Content and repeat visits | Several, this week | Last 30 days | Last 90 days | None |
If you do not know a value, leave the rating blank. A blank adds nothing, and it does not count against the lead. If the same factor keeps coming up blank, that is a data problem worth fixing before you build a model on it.
A filled-in example
Here is a demo lead scored on the card. The fit side, with every factor and weight visible:
- Company size (about 450 employees) 30% 8.5
- Industry 25% 9.5
- Role and seniority (VP, operations) 25% 6.5
- Runs the system you need 15% 8.5
- Personal email at sign-up 5% -4.5
The math: 8.5 at 30% is 2.55, 9.5 at 25% is 2.375, 6.5 at 25% is 1.625, and 8.5 at 15% is 1.275, which adds to 7.825. The disqualifier subtracts 4.5 at 5%, or 0.225, for a fit score of 7.6.
The engagement side:
| Signal | Rating | Weight | Points | What the rep saw |
|---|---|---|---|---|
| High-intent page visits | 7.5 | 30% | 2.25 | Pricing page twice, most recently 12 days ago |
| Direct response | 6.5 | 30% | 1.95 | Replied to a rep email 19 days ago |
| Events and webinars | 4.5 | 15% | 0.675 | Registered for a webinar two months ago and watched half |
| Others at the company active | 2.5 | 15% | 0.375 | One other person visited once, 70 days ago |
| Content and repeat visits | 8.5 | 10% | 0.85 | Read two case studies this week |
| Engagement score | 6.1 |
The “what you saw” column matters as much as the numbers. It is what lets a manager check the rating, and what gives the rep something specific to say on the call.
Reading the result: plays for every pair of scores
The card places each lead in one of three fit bands and one of three engagement bands. Each pair gets a play. The cutoffs below are a starting point; set your own by looking at where conversion clearly rises in your past leads.
| Engagement 6.0 and up | Engagement 3.0 to 5.9 | Engagement under 3.0 | |
|---|---|---|---|
| Fit 7.0 and up | Rep reaches out within one business day, referencing what they looked at | Rep follows up this week with a specific reason to talk | Right company, not active yet. Stay in front of them and alert the owner when engagement rises |
| Fit 4.0 to 6.9 | Qualify fast with a short call or a self-serve path | Nurture with content matched to what they read, and rescore in 30 days | Nurture only. No rep time yet |
| Fit under 4.0 | Check the fit data first. If it holds, answer their question and route to self-serve | Self-serve and content only | No action |
The demo lead scores 7.6 on fit and 6.1 on engagement, so the play is a rep reaching out within a day, opening with the pricing visits and the case studies.
One rule sits above the grid: a demo request or a direct reply gets an answer right away, whatever the band. The band sets the order of work, not whether someone gets a reply.
How to use a lead scorecard
- SDR triage. Score the day’s inbound before the first call block, and work the list top band first.
- Marketing to sales handoff. Attach a completed card to every lead you pass over. If sales disagrees with a rating, the evidence column shows exactly where.
- Pipeline reviews. Score the open deals that came from new leads. It surfaces deals with strong engagement and weak fit, which are the ones most likely to stall.
- Calibration. Have two people score the same ten leads separately, then compare. Where ratings differ by more than two points, tighten the wording in the rating key.
- Designing a scoring model. After a few weeks of cards, check which ratings the leads that converted had in common. Those are your model’s factors and weights.
When to move from a scorecard to a scoring model
A scorecard works by hand. It stops working when the volume gets too high to score every lead, or when you need the score to trigger something, such as routing or a sequence. The usual signs:
- You get more inbound leads in a week than anyone can score.
- Different people rate the same kinds of leads differently, even with the key.
- You want the score on the CRM record, where reps, workflows, and agents can act on it.
- You need activity to fade on its own instead of someone rerating it.
At that point, take the factors and weights you have proven on the card and turn them into a model. Our five-step framework for building an ICP scoring model covers the fit side, and your ideal customer profile is where the fit factors come from.
What is in the template
The lead scorecard template has five parts, and the downloadable Excel file is being added:
| Tab | What it does |
|---|---|
| Start here | How to use the card in five steps |
| Scorecard | A blank card that calculates both scores, both bands, and the play. Duplicate it to score more leads |
| Example | The demo lead above, filled in, so you can see the format |
| Rating key | What each rating looks like for every factor and signal, plus the band cutoffs, which you can edit |
| Plays | The play for each pair of bands, which you can edit |
The card checks that each section’s weights add up to 100, treats a blank rating as zero, and prints on one page.
Where BoostIdeal fits
A scorecard proves your model by hand. BoostIdeal runs it on every record in HubSpot. You build the model and set the weights, the same way you do on the card, and every score writes back to the record with its reasons, so a rep can see why a lead scored what it did without opening a spreadsheet. It scores any record, including leads, contacts, and accounts, and scores recalculate hourly by default, or in near real time with webhook sync. If you are not on HubSpot, the scorecard and a spreadsheet will take you a long way on their own.
Frequently asked questions
What is a lead scorecard?
A lead scorecard is a one-page form for rating a single lead on fit and recent engagement. Each factor gets a rating from 0 to 10 and a weight, and the card adds them into a fit score and an engagement score that point to a next step.
What should be on a lead scorecard?
Fit factors such as company size, industry, role and seniority, and the systems the company runs; a disqualifier that subtracts; and engagement signals such as pricing page visits, direct responses, event attendance, and activity from other people at the same company.
How is a lead scorecard different from lead scoring?
A scorecard is filled in by a person, one lead at a time. Lead scoring applies a model to every lead automatically, usually in a CRM. Teams often use a scorecard first to agree on the factors and weights, then build the model from it.
Should fit and engagement be combined into one score?
Usually not. A single blended number hides which half is doing the work. A lead with perfect fit and no activity needs a different play from a lead with lots of activity and poor fit, even if their totals match.
How do you keep ratings consistent between people?
Use a written rating key with examples for each level, ask for the evidence behind every rating, and have two people score the same leads now and then to check that they agree.
Should email opens count on a lead scorecard?
No. Privacy features load tracking pixels automatically, so opens no longer show that a person read anything. Use replies, meetings, and clicks instead.
How often should you update the scorecard?
Review it every quarter. Check whether the leads that scored highest converted better than the rest, and adjust one or two weights or cutoffs at a time.
The takeaway
A lead scorecard is the cheapest way to get sales and marketing to agree on what a good lead looks like. Rate fit and engagement separately, write down the evidence, and use the pair of scores to choose the play. Once the card holds up, it becomes the blueprint for a scoring model your whole team can trust.