Fit × intent × timing: a score sales actually trusts

Why points-based scoring fails

Classic lead scoring adds up points: ten for a whitepaper download, five for a newsletter click, twenty for the job title. The result is a number without context. A student who downloads three whitepapers ranks above the CEO who visited the pricing page once.

Sales notices after the first misses – and stops believing the score.

Three dimensions instead of one number

• Fit: does the account match your ICP? Industry, size, tech stack, region, exclusion criteria.
• Intent: is the account engaging with your topic right now? Website behavior, content engagement, third-party intent data, inquiries.
• Timing: is there a trigger? New executive, funding round, job postings, expiring contract, reorganization.

An account with high fit and no intent belongs in awareness. High intent without fit is often a competitor or a student. Only when all three come together is personal outreach worth it.

What agents take over

The data for all three dimensions usually already exists, scattered: in the CRM, in analytics, in enrichment data, in public sources. A scoring agent brings it together, rates it by your rules and writes a short rationale for every score: why the account fits, which signal counts, what the trigger is.

A score without reasoning is a claim. A score with reasoning is something you can work with.

Be honest about the limits

Intent data is probability, not certainty. Website visits can usually only be attributed at company level, and not always even that. So scoring also needs an uncertainty indicator – and the rule that a high score triggers a proposal, not automatic contact.

How to roll it out

Start with the deals of the last twelve months: which fit, intent and timing patterns did the won ones share? That gives you the first version of the rules. The agent applies them, sales gives feedback in the weekly review and the rules get sharper. After a few weeks the score has a hit rate worth talking about.