Lead Scoring That Actually Works

Lead scoring sounds straightforward: assign points to leads based on their characteristics and behavior, and prioritize the ones with the highest scores. In practice, most lead scoring models fail because they are built on assumptions rather than data.

The Assumption Trap

A common approach is to give points for job title, company size, and industry. CEO of a company with five hundred employees in manufacturing gets a high score. But that score says nothing about whether this person is actually interested in buying anything. A marketing coordinator at a fifty-person company who has visited your pricing page three times and downloaded a case study is a better lead.

Behavior Over Demographics

Effective lead scoring weights behavior heavily. Page visits, content downloads, email opens, webinar attendance, and — most importantly — pricing page views and demo requests. These actions indicate intent. Demographics provide context, but intent drives conversion.

The Decay Factor

Lead scores should decay over time. A lead who visited your site every day last month but has gone silent for six weeks is not the same lead. Without a decay factor, your pipeline fills up with stale scores that misrepresent reality.

Calibrating With Closed-Won Data

The best way to validate a scoring model is to look at your closed-won deals and reverse-engineer the scores. What did those leads do before they converted? How long was the cycle? What actions correlated most strongly with closing? Use that data to weight your model, and recalibrate quarterly.