Lead scoring
Why it matters
Sales time is limited. Without a score, reps work leads in the order they arrive or by gut feel, and strong leads wait while weak ones get attention. A score gives a shared, consistent rule and makes the handoff from marketing to sales less of an argument.
It also speeds up the first reply. A lead who visits the pricing page twice and books a demo is warmest within hours, and a score can alert a rep while it matters.
The score is a tool for prioritising, not a verdict on a person. It is only as good as the data behind it, so it needs checking against which leads actually became customers.
What it is
A scoring model adds and subtracts points on each lead. There are usually two kinds of points:
- Fit: who the lead is. A company in the target industry and size range, with a decision-making job title, earns points.
- Behaviour: what the lead does. Visiting the pricing page, replying to an email or booking a demo earns points. Long silence or an unsubscribe removes them.
When the total passes an agreed threshold, the lead becomes a marketing qualified lead or goes straight to sales.
Common mistakes
- Scoring only activity. A student reading every blog post can outscore a buyer who visited once.
- Never checking the model against results, so it drifts away from reality.
- Hiding the logic. When reps cannot see why a lead scored high, they stop using the score.
How to build one
- Start from the ideal customer profile. Fit points follow from it.
- Look at past customers and ask which actions they took before buying. Give those actions the most points.
- Set a threshold and a rule for what happens above it.
- Test the model on closed deals: did the won deals score higher than the lost ones?
- Review quarterly and adjust the weights.
A simple model with six or eight rules that people trust beats a complex one nobody understands. Enrichment supplies the missing fit data, as covered under data enrichment.
