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DIGITO

Lead Scoring Model Builder

Define weighted ICP criteria, set A/B/C thresholds, and rank leads live. Free, no signup, runs in your browser.

Scoring model

Qualify manufacturers as nearshoring leasing prospects. Anonymized from a real system that scored 1,000+ industrial accounts: sector fit, revenue band, location signal, expansion trigger, and decision-maker access.

Criteria weightstotal 100
30% · 30

How well the industry fits the park's tenant profile.

20% · 20

Mid-market firms move fastest; enterprises are slow, micro-firms lack capital.

25% · 25

Already in the target metro is the strongest signal; a competitor metro is a disqualifier.

15% · 15

A recent, dated event that creates a reason to move now.

10% · 10

How close you are to a person who can sign.

Tier thresholds

Below the B cutoff is tier C (disqualify or recycle).

APriority
4
BNurture
2
CRecycle
3
Avg score
71
9 leads, ranked by score

Score = weighted average of each criterion’s points (0–100), normalized by total weight. Adjust the weights and thresholds on the left and the ranking re-sorts live. Everything stays in your browser.

From a list to a priority.

Most teams work a lead list in whatever order it arrives. A scoring model turns the same list into a ranked queue: the criteria that actually predict a good account get weighted, every lead gets a transparent 0 to 100 score, and the thresholds decide who sales works now versus who gets nurtured or dropped.

The point isn’t the number, it’s the agreement. A model makes the qualification logic explicit, so why a lead is an A is something you can see and argue with, not a gut call.

1Define criteria & weights

Pick the ICP signals that predict a good lead, and weight them by how much each one matters. Weights are relative and normalized, so they never have to add to 100.

2Set tier thresholds

Choose the score cutoffs for tier A (work now), B (nurture), and C (recycle). The same model can run hot or conservative just by moving the cutoffs.

3Score & route

Each lead gets a 0 to 100 composite and a tier. Sort, route the A's to sales, drop the C's, and export the scored list. Adjust a weight and the whole list re-ranks instantly.

Frequently asked questions.

What is a lead-scoring model?

A lead-scoring model assigns each lead a number that estimates how good a fit and how ready-to-buy they are, so a team can prioritize the right accounts instead of working the list top to bottom. This builder uses a transparent weighted average: each criterion has a weight and a set of options worth 0 to 100 points, and the composite is the weighted average of the chosen options.

How is the score calculated?

For each lead, every criterion contributes its option's points times that criterion's share of the total weight. Add the contributions and you get a 0 to 100 composite. Because weights are normalized by their total, you can set them however you like, in whatever scale, and the math still works. The per-lead breakdown shows exactly how each criterion moved the score.

What are the two built-in models?

B2B SaaS scores inbound and outbound leads on company size, industry fit, engagement signal, tech-stack fit, and the buying role of the contact. Nearshoring / Industrial is an anonymized version of a real system used to qualify over a thousand manufacturing prospects for an industrial leasing pipeline: sector fit, revenue band, location signal, expansion trigger, and decision-maker access. Both ship with fictional sample leads you can edit.

Is the sample data real?

No. The company names and values are fictional, built only to demonstrate the model and the format. No client or proprietary data is used. Edit any lead, add your own, or rebuild the criteria around your own ICP.

Is my data sent anywhere?

No. Everything runs in your browser. Your weights, thresholds, and leads are saved to local storage on your device and never leave it. There is no account, no tracking, and no server.