Lead Scoring That Sales Actually Uses

Most lead scoring systems are abandoned quietly. The model stays in the CRM, the scores keep calculating, and the sales team works the list in the order the leads arrived.

That is not a discipline problem. It is a signal problem: the model was scoring things that do not predict a sale.

What does not predict anything

Email opens. Affected by preview panes, image blocking and privacy features to the point where the number is close to noise.

Page views and time on site. A competitor researching you looks identical to a buyer.

Content downloads, which largely identify people who wanted the content.

Webinar attendance, which correlates with having a free hour.

A model built from those produces high scores for the most curious people in the market rather than the most ready, and a salesperson discovers that within about twenty calls.

What does predict

Two categories, and they need to be scored separately rather than added into one number.

Fit. Is this the kind of business we can help. Industry, size, location, whether they have the thing our service requires. Mostly static and mostly knowable from the record.

Intent. Are they in a buying window. Requested a quote, asked about price, named a timeline, asked who else we work with, came back to the pricing page three times in a week, replied to a sequence with a question.

A high-fit low-intent lead is a nurture. A low-fit high-intent lead is a polite no. A high-fit high-intent lead is a call today. Collapsing those into a single score of 73 destroys the distinction that makes the model useful.

The highest-weighted signals

In practice, in rough order.

A direct request. Quote, demo, call, price. Nothing else comes close, and a model that ranks a quote request below an accumulation of page views is broken.

A reply to a human. Any reply, even a negative one, outranks every passive signal.

Stated timeline. Somebody who says "we need this by March" has qualified themselves.

Repeat visits to a decision page. Pricing, terms, case studies. Different from general browsing because of where it sits in the journey.

Firmographic match, which does not move but decides whether the rest matters.

Score decay matters more than score accumulation

Intent is perishable. A lead that requested a quote six weeks ago and went silent is not hotter than a lead that requested one yesterday, however much score they accumulated in between.

A model without decay gradually promotes everybody who has been in the database longest, which is the opposite of useful. Intent signals should lose most of their weight inside a few weeks.

Build it with sales, not for them

The fastest way to a model that gets used: take the last fifty closed deals and the last fifty losses, and ask the people who worked them what the tell was.

The answers are usually specific and unglamorous. They mentioned a deadline. They asked about the contract. They came from a referral. They had already tried doing it themselves.

Those are the scoring criteria. A model derived from actual closed business is defensible in a way a model derived from a template is not.

Keep it simple enough to be legible

Three or four bands, not a hundred point scale. A salesperson needs to know what to do with a lead, not what percentile it sits in.

And the score has to be explainable in one line on the record. "Requested a quote, matches ICP, named a timeline" is actionable. "Score 84" is not.

Review it against outcomes quarterly

The only question that matters: of the leads the model ranked highest last quarter, what proportion closed, and of the ones it ranked lowest, how many closed anyway.

If the second number is not much smaller than the first, the model is decoration. Fix it or remove it, because a scoring system nobody trusts is worse than none, since it adds a step and changes no decisions.