Quick answer
Lead scoring assigns each lead a numeric value combining fit (how well it matches your Ideal Customer Profile) and intent (how much buying behaviour it has shown), so sales prioritises the highest-probability opportunities. Build it from closed-won data, keep it to five to eight criteria, weight them into a 0-100 score, route 80+ to active outreach and mid-range leads to nurture, decay old intent signals, and recalibrate quarterly against real outcomes.
Why lead scoring matters
Sales time is the scarcest resource in acquisition. Without scoring, reps work leads in the order they arrive — which means a high-fit, high-intent buyer can sit behind dozens of dead-end leads. Lead scoring fixes the ordering: it ranks the queue so the best opportunities get worked first and worst-fit leads get filtered out before they consume anyone's attention. Done well, it raises conversion rates simply by reallocating the same effort to better leads.
Fit vs intent — score both
The two-dimensional model is the foundation. Fit answers "should we want this lead?" and comes straight from your ICP. Intent answers "is this lead ready now?" and comes from behaviour. The interaction is what matters: a high-fit, high-intent lead is your top priority; a high-fit, low-intent lead belongs in nurture until intent appears; a low-fit, high-intent lead is a trap that will churn even if it buys. Scoring on intent alone is the most common and costly mistake.
A worked scoring model
Here is a simple, realistic 0-100 model splitting points between fit and intent.
Fit (max 70)
| Industry / vertical fit | 25 |
| Company size in target range | 20 |
| Target region / market | 10 |
| Technographic match | 15 |
Intent (max 50)
| Visited pricing page | 15 |
| Requested demo / contact | 20 |
| Repeat visits in 14 days | 10 |
| Engaged with a guide / resource | 5 |
Cap the combined score at 100 and tune the weights so they reflect what actually predicts conversion in your data. The exact numbers matter less than the discipline of deriving them from outcomes and reviewing them.
Common mistakes to avoid
- Scoring purely on intent and ignoring fit — you fast-track engaged leads who will never buy or will churn.
- Too many criteria — a 20-factor model is impossible to reason about and quietly accumulates noise.
- Never decaying intent — a pricing-page visit from six months ago should not count like one from yesterday.
- No feedback loop — if you never compare scores to closed-won, you cannot know the model works.
- Treating the score as truth instead of a prioritisation aid — it ranks effort, it does not replace judgement.
Keeping the model accurate
A lead scoring model decays. Every quarter, compare the conversion rate of each score band to what the model predicted: high scorers should convert meaningfully better than low scorers, and if they do not, your criteria or weights are wrong. Add a time-decay to intent signals so stale behaviour stops inflating scores, prune criteria that no longer correlate with closed-won, and document each change so the model stays explainable. Treat it as a living system, not a one-time spreadsheet.
How GeraReach automates scoring
GeraReach builds fit-and-intent scoring directly into its acquisition workflow: it scores every lead against your Ideal Customer Profile, layers in behavioural intent, and routes the highest-scoring leads into sequenced outreach while sending the rest to nurture — then attributes revenue back so the model keeps improving. The same engine runs acquisition across the Gera ecosystem, including GeraHome, GeraJobs, and GeraCompliance.
Frequently asked questions
What is lead scoring?
Lead scoring is the practice of assigning a numeric value to each lead based on how well it fits your Ideal Customer Profile and how much buying intent it has shown, so sales can prioritise the leads most likely to convert. A good model turns a long, undifferentiated list into a ranked queue, ensuring limited sales time goes to the highest-probability opportunities first.
What is the difference between fit scoring and intent scoring?
Fit scoring measures how closely a lead matches your ICP — industry, size, region, technology. Intent scoring measures how much buying behaviour the lead has shown — pricing-page visits, demo requests, content downloads, repeated engagement. The strongest models combine both into a two-dimensional view: high-fit-high-intent leads get immediate outreach, while high-fit-low-intent leads go into nurture until intent appears.
How do I choose lead scoring criteria?
Start from your closed-won and churned data, not intuition. Identify the attributes and behaviours that the customers who actually bought and stayed had in common, and that poor-fit or churned customers lacked. Those become your criteria. Avoid vanity signals (like generic page views) that do not correlate with conversion, and keep the model small — five to eight criteria is usually enough.
What threshold makes a lead "sales-ready"?
There is no universal number; you calibrate it from your own conversion data. A common pattern is a 0-100 scale where 80+ is sales-ready (route to active outreach), 50-79 is nurture, and below 50 is excluded or recycled. Set the threshold where your conversion rate from that score band justifies a salesperson's time, then move it as you gather evidence.
How often should I recalibrate a lead scoring model?
Review the model quarterly against actual outcomes. Check whether high-scoring leads really converted at a higher rate than low-scoring ones; if not, the weights or criteria are wrong. Lead scoring is not set-and-forget — markets, products, and buyer behaviour shift, and a stale model quietly misroutes good leads and wastes time on bad ones.
Score every lead automatically
GeraReach scores leads on fit and intent, then routes the best into outreach — and attributes the revenue.