Lead scoring is the process of ranking leads based on how well they fit your ideal customer profile and how much buying intent they show. It gives sales and marketing teams a shared way to decide which prospects deserve attention first.

In B2B, that usually means combining profile data with behavioural signals. A strong-fit prospect repeatedly visiting product and pricing pages should normally be treated differently from a poor-fit contact who downloaded one piece of content months ago.

How lead scoring works

Most useful lead scoring models separate two questions:

• Fit: Is this a person or company you would want as a customer? Common inputs include job title, seniority, industry, company size, region and other firmographic data.
• Intent: Is there evidence they are actively interested now? Signals can include product or pricing page visits, demo requests, repeat website activity, webinar attendance, form submissions and campaign engagement.

Negative signals can also reduce priority. Examples include long periods of inactivity, careers-page visits, student email addresses or other activity that suggests the person is unlikely to buy.

Keeping fit and intent visible separately makes a score easier to understand. A perfect-fit account may still show little current interest, while a highly active visitor may be a poor commercial fit.

A simple lead scoring example

The numbers themselves are not the goal. A useful model creates a consistent prioritisation system that sales understands and trusts.

Why teams use lead scoring

• Prioritisation: Sales can focus first on the leads showing the strongest combination of fit and intent.
• Speed to lead: High-priority prospects can trigger faster follow-up while interest is still fresh.
• Sales and marketing alignment: Both teams work from agreed criteria for what makes a lead worth pursuing.
• Better routing: Scores can support rules that send the right leads to the right reps or teams.
• Cleaner reporting: Marketing can compare lead quality across campaigns, sources and segments rather than reporting volume alone.

Common types of lead scoring

Behavioural lead scoring focuses on what buyers do. Predictive lead scoring uses historical data and statistical or machine-learning models to estimate conversion likelihood. Lead grading or fit scoring focuses primarily on whether a lead matches the characteristics of an ideal customer.

Many B2B teams use a combination. For example, a fit grade can show whether an account is worth pursuing, while a behavioural score shows whether there is enough current interest to justify sales action.

Lead scoring in Salesforce

In Salesforce, lead scores can be stored on lead, contact or account records and updated as new data arrives. They can then be used in list views, reports, dashboards, routing rules, alerts and sales workflows.

The most useful setup is one where sales can see not only the score, but also the signals behind it. A score is much easier to trust when a rep can see that it increased because a prospect returned to a product page, viewed pricing and attended a webinar.

For a deeper look at scoring models, use cases and implementation inside Salesforce, see the Salesforce lead scoring guide.

Lead scoring vs lead qualification

Lead scoring supports qualification, but it does not replace it. A score helps identify which leads deserve attention. Qualification is the broader decision about whether a lead or opportunity is worth pursuing, often using additional context from a salesperson or revenue team.

What makes a useful lead scoring model?

Start with signals that have a clear relationship to buying intent or customer fit. Keep the model simple enough that sales can understand it, test it against real conversion outcomes, and review the rules as your products, channels and ideal customer profile change.

A scoring model should make the next action clearer. If nobody can explain why a lead has a particular score, or if sales ignores the result, adding more rules will not fix the underlying problem.