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Lead Scoring Model Builder

Define weighted review criteria, set A/B/C score bands, and compare a sample lead list. Free, no signup, runs in your browser.

Scoring model

Compare manufacturers using a model adapted from a real workflow applied across more than 1,000 industrial prospects: sector, 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 receives the highest value; a committed competitor location receives the lowest.

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 C. These are review bands, not automatic decisions.

AHigher score
4
BMiddle score
2
CLower score
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 consistent comparison.

A scoring model makes a team's chosen review criteria explicit. Each lead gets a transparent 0 to 100 score from those inputs, and the score bands make it easier to compare the list consistently.

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

The score reflects the criteria and weights you choose. It does not predict fit or buying intent unless the model is validated against real outcomes.

1Define criteria and weights

Choose the signals you want to use for review and set how much each one contributes. Weights are relative and normalized, so they never have to add to 100.

2Set score bands

Choose the cutoffs for A, B, and C. The bands organize the comparison; they do not make an eligibility, fit, or buying-intent decision.

3Compare and review

Each lead gets a 0 to 100 composite and a band. Sort the list, inspect the contribution of each criterion, and export the comparison. Adjust a weight and the list updates immediately.

Frequently asked questions.

What is a lead-scoring model?

A lead-scoring model applies chosen criteria and weights to create a consistent comparison across a list. 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 selected options. The result reflects the model you define, not an independent prediction of fit or intent.

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 compares sample leads across company size, industry, engagement, tech-stack fit, and the contact's role. Nearshoring / Industrial is adapted from a qualification workflow used across more than 1,000 manufacturing prospects: sector, revenue band, location signal, expansion trigger, and decision-maker access. Both use fictional sample companies and values that 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?

Your tool inputs and generated results stay in this browser and are not sent to DIGITO. The site itself uses aggregate traffic and performance analytics described in the Privacy policy.