Lead Scoring Worksheet template.

What's inside — the exact file

This is the complete template, not a sample. The rows are worked examples — replace them with your data (and delete them before any platform import).

What this template is for

Lead scoring fails when it's invented in a workshop instead of derived from evidence. This worksheet structures the derivation: twelve pre-written fit and engagement signals with point values, decay flags, and — the column everyone skips — a written rationale tying each signal to actual closed-won behavior.

Use it before touching HubSpot's score property. The pre-filled signals (title match, company size, pricing-page visits, demo requests, email engagement, and negative signals like competitor domains and student emails) are the common core; calibrate the points against your last quarter of closed-won deals, then build.

How to use it, step by step

1. Profile your closed-won deals first. Pull the last 20–50 closed-won deals and list what they had in common before they bought — titles, company sizes, pages visited, content consumed. Scoring derives from this, not from intuition.
2. Split fit from engagement. Fit is who they are (title, industry, size); engagement is what they did (visits, opens, demo requests). Track the split in the fit_or_engagement column — a high-fit no-engagement lead and a high-engagement bad-fit lead need different plays, and a single blended number hides that.
3. Assign points with negative scores included. Weight signals by how strongly they predicted closing. Negative points matter as much: competitor domains, students, and free-email-plus-tiny-company patterns are pre-filled as examples.
4. Mark what decays. A pricing-page visit means something for two weeks, not two years. Flag time-sensitive signals in the decays column so engagement scores reflect recency.
5. Set the MQL threshold against real examples. Score last quarter's leads retroactively with your draft model. The threshold goes where actual buyers separated from tire-kickers — not at a round number that felt right.
6. Build in HubSpot and review monthly. Implement in the score property, then compare score-at-handoff against what actually closed each month. Scoring is a model; models drift; the rationale column is what makes recalibration fast.

What each column means

Common mistakes to avoid

• Scoring email opens generously — opens are the weakest engagement signal (and privacy features inflate them); clicks and page visits carry real intent.
• No negative scoring, so competitors, students, and job seekers accumulate points and 'qualify'.
• Setting the MQL threshold before scoring historical leads against it — you're guessing where the line goes.
• Building the model once and never reconciling it against closed-won reality, so sales quietly learns the score means nothing and stops looking.

Questions, answered

What HubSpot tier does lead scoring need?

Score properties are available from Professional tiers as of July 2026 — Marketing Hub Professional is the usual home. Run our HubSpot Plan Finder if scoring is a deciding feature for your tier choice.

How many scoring signals should we use?

Ten to fifteen meaningful ones beat forty noisy ones. Every signal needs a rationale you can defend — if you can't explain why it predicts buying, it's decoration, and decoration in a scoring model is noise.

Should we use HubSpot's AI-predictive scoring instead?

Predictive scoring is worth testing once you have volume for it to learn from, but a transparent manual model is where most teams should start — you can explain it to sales, audit it monthly, and fix it when it drifts. Opaque scores that sales doesn't trust don't get used.

This is a Market Disrupt worksheet, not a vendor file. Import-format templates follow each platform's documented layout as of July 2026 — platforms evolve, so validate against current documentation before a large import.