Lead scoring: a simple model sales will actually trust
Lead scoring ranks leads so sales spends time on the people most likely to buy. Most models fail because they reward activity instead of fit. Here is how to build one that predicts pipeline.
What is lead scoring?
Lead scoring gives each lead a number based on who they are and what they have done, so marketing and sales agree which leads are ready for a sales conversation. A good score predicts which leads become qualified opportunities, not just who clicks most.
Fit score vs intent score
Keep the two scores separate. Fit tells you whether the company is worth selling to; intent tells you whether now is the right time. A high-intent lead with poor fit is still a poor lead.
- Fit: company size, industry, role, region, technology used
- Intent: demo or pricing page visits, form type, return visits, replies
- Negative signals: students, competitors, personal email, job seekers
How to build a lead scoring model in five steps
Start from your own closed deals, not from a template. The goal is a short list of signals that clearly separate leads that became opportunities from those that did not.
- Pull closed-won and closed-lost deals from the last 6 to 12 months
- List the fit and behaviour traits those buyers shared
- Give points to the few traits that show the biggest difference
- Agree a threshold with sales for when a lead is handed over
- Review conversion by score band every month and adjust
Signs your scoring model is broken
If sales ignores scored leads, the model is wrong, not the reps. Watch for high-score leads that rarely convert, scores that rise from email opens alone, and no feedback loop from closed deals back into the model.
How Ideabazi helps
We build scoring around buyer-intent data and the full lead lifecycle in your CRM. For a vertical software company this was part of the work that delivered 8x more qualified leads and 32% lead-to-sales-qualified conversion.
Frequently asked questions
What is a good lead scoring model?
A good lead scoring model uses a short list of fit and intent signals taken from your own closed deals, has a handoff threshold agreed with sales, and is checked monthly against real conversion. If high-score leads do not become opportunities more often than low-score leads, the model needs changing.
What is the difference between an MQL and an SQL?
A marketing-qualified lead (MQL) has reached the score marketing agreed signals buying interest. A sales-qualified lead (SQL) has been reviewed or spoken to by sales and accepted as a real opportunity to pursue. Lead scoring sets the MQL line; sales feedback confirms the SQL.
Should lead scoring use AI?
Predictive scoring can help once you have enough closed deals for the pattern to be reliable. Most brands get more from a simple, transparent points model first, because sales can see why a lead scored high and trust the handoff.
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