What Is Lead Scoring?
Lead scoring assigns numerical values to prospects based on their likelihood to buy. Learn how scoring models work and how to build one.
Key Takeaways
- Lead scoring assigns points to prospects based on demographic fit and engagement behaviour.
- Scoring helps sales teams prioritise their time on leads most likely to convert.
- Models should be reviewed regularly because buyer behaviour and your product evolve over time.
How lead scoring works
Lead scoring is a methodology for ranking prospects on a numerical scale that reflects their perceived value to the business. Points are assigned based on two categories: who the lead is and what they have done. Demographic attributes like job title, company size, and industry contribute to a fit score. Behavioural actions like visiting pricing pages, opening emails, or attending demos contribute to an engagement score. The combined total determines priority.
Building a basic scoring model
Start by listing the attributes and actions common among your best customers. A decision-maker at a mid-market company might receive 20 fit points, while a junior employee at a startup receives 5. Visiting the pricing page might add 15 engagement points, while opening a newsletter adds 2. Set a threshold, say 50 points, above which a lead is flagged as marketing qualified. Keep the model simple initially and add complexity only as you gather data.
Predictive vs rule-based scoring
Rule-based scoring relies on manual point assignments defined by your team. Predictive scoring uses machine learning to analyse historical data and identify patterns that correlate with conversion. Predictive models can surface non-obvious signals, such as leads from specific referral sources converting at higher rates. Larger companies with substantial data sets benefit most from predictive approaches, while smaller teams often get better results from well-maintained rule-based models.
Common scoring mistakes
Over-weighting vanity actions like email opens inflates scores without reflecting real intent. Ignoring negative signals is another pitfall. A lead who unsubscribes from emails or visits your careers page instead of product pages should lose points. Finally, never set and forget your model. Review scoring accuracy quarterly by comparing predicted conversions against actual outcomes and adjusting weights accordingly.