What is lead scoring?

Lead scoring assigns numerical values to prospect attributes and actions so sales teams can rank leads by conversion likelihood. High-scoring leads get contacted first; low-scoring leads stay in nurture sequences.

What criteria should I weight most heavily?

It depends on your sales motion. B2B SaaS companies typically weight company size, job title, and product engagement highest. E-commerce businesses weight recent purchase behavior and email clicks. Start with equal weights, then adjust based on data from closed deals.

What score should trigger a sales call?

A common threshold is 70+ out of 100 for an immediate follow-up call. Scores between 40–69 often go to a nurture email sequence. Below 40 is usually left for retargeting ads or passive content. Your thresholds should reflect your historical conversion data.

How often should I recalibrate the weights?

Review weights quarterly or after 50+ closed deals — whichever comes first. Compare the scored values at time of first contact against actual close rates. Criteria with high scores but low close rates should have their weights reduced.

What is the difference between explicit and implicit scoring?

Explicit scores come from information the lead provides directly (job title, company size, industry). Implicit scores come from observed behavior (pages visited, emails opened, demo requested). This calculator covers both — demographic fit captures explicit data; behavioral and engagement cover implicit signals.

Lead Score Calculator

Name: Lead Score Calculator
Availability: InStock
Author: Nham Vu

Assign weights and scores across demographic, behavioral, and engagement criteria to compute a composite lead score and conversion priority tier.

Demographic Fit

Category weight %

Company size fit 5

Industry / vertical fit 5

Job title / seniority 5

Geography / region 5

Behavioral Signals

Category weight %

Pages / sessions visited 5

Demo / trial requested 5

Content downloads 5

Pricing page viewed 5

Engagement Level

Category weight %

Email opens / clicks 5

Recency of last touch 5

Social / event interaction 5

Direct reply / response 5

Composite Lead Score

50 / 100

Warm

Demographic 20.0

Behavioral 17.5

Engagement 12.5

Priority tiers

Hot — 75–100

Warm — 50–74

Cool — 25–49

Cold — 0–24

Summary

Assign weights and scores across demographic, behavioral, and engagement criteria to compute a composite lead score and conversion priority tier.

How it works

Enter a weight (0–100) for each scoring category — demographic fit, behavioral signals, and engagement level.
Rate each criterion on a 0–10 scale based on what you know about the lead.
The calculator computes a weighted composite score out of 100 in real time.
Read the priority tier (Hot, Warm, Cool, Cold) to decide next-step urgency.
Adjust weights to match your sales cycle — B2B deals often weight company fit higher; B2C weights behavior more.

Use cases

Rank inbound leads before handing them to sales to maximize rep time.
Compare multiple prospects side by side to decide who gets the next call slot.
Calibrate scoring model weights after reviewing closed-won and closed-lost deals.
Set MQL (marketing qualified lead) thresholds for automated CRM routing.
Coach new SDRs on which signals drive conversion using the weighted breakdown.
Forecast pipeline quality by scoring leads before a quarterly review.

Frequently Asked Questions

Last updated: 2026-07-01 · Reviewed by Nham Vu