What is Lead Scoring?

How lead scoring actually works

Two categories of signal. Fit covers attributes like company size, job title, city, stated budget and industry, while behaviour covers actions such as pages viewed, emails opened, a pricing page visit, a demo booking and how long it has been since they last did anything.

Assign points, sum them, set thresholds. High scores route straight to a senior salesperson, middle scores enter a nurture sequence, low scores wait. Negative scoring matters just as much, so a competitor domain, a student email or a city you do not serve should subtract rather than be ignored.

Where teams get lead scoring wrong

Building the model on opinion. Somebody in a meeting decides an email open is worth five points and nobody ever checks whether opens correlate with closed deals, so go back through the last two hundred won deals, find what those leads actually did, and weight from evidence instead.

Second, ignoring decay, because a pricing page visit from four months ago is not the same signal as one from yesterday and your top scores slowly fill with people who went cold long ago.

Third, over-engineering. A forty-variable model nobody understands gets abandoned within a quarter. Start with six signals that clearly predict conversion.

What good looks like in India

For Delhi NCR real estate the signals predicting a site visit are usually blunt: stated budget band, whether the preferred location matches an available project, how quickly they answered the first call, and whether they asked about possession timelines. Four inputs get you most of the way there.

Add a channel dimension too, since leads from a detailed landing page behave nothing like leads from a pre-filled instant form. Review quarterly against what actually closed. A scoring model nobody has audited in a year is just an opinion with a number stapled to it.