What is AI lead scoring, and how is it different from rule-based scoring?

Q: What is AI lead scoring, and how is it different from rule-based scoring?

AI lead scoring uses a model trained on your own won and lost deals to estimate how likely each new lead is to become revenue, then ranks the queue so reps work the best ones first. Rule-based scoring adds points for attributes someone guessed were important; AI scoring learns which attributes actually predicted a sale in your history.

What the model looks at

Fit, meaning who the company is: size, industry, technology, growth. Intent, meaning what they did: pages visited, content read, replies, meetings. And timing, meaning signals such as a new executive or a hiring push. A rule-based score weighs these by opinion. A model weighs them by what happened in your closed deals.

Where rule-based scoring breaks

Points systems drift. Someone adds ten points for a webinar, nobody removes them, and within a year the score rewards activity rather than likelihood to buy. Reps learn to ignore it. The test of any score is simple: do the top-scored leads close at a visibly higher rate than the rest? If not, it is decoration.

What you need before you start

Enough history to learn from, usually a few hundred closed deals across won and lost. Fields that are filled in, which often means enrichment comes first. And agreement on what a good outcome is, because a model trained to predict meetings will find different leads from one trained to predict revenue.

What it changes day to day

Reps open the queue and the order already makes sense. Routing sends the strongest leads to the right owner immediately. Marketing sees which sources produce leads that close rather than leads that click. And the score explains itself, listing the reasons, so a rep can disagree with it intelligently.

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