Every team has more leads than time. Lead scoring is the habit of ranking them, so the best ones get attention first and the rest get the right amount of nurture instead of none.
Scoring does not need a data science team. It needs three honest questions and a way to write the answers down as numbers.
The three questions
- Fit: does this person or company look like your best customers? Market, location, role, age group or company size.
- Intent: is there evidence they need what you sell? The community they joined, the brand they follow, the questions they ask.
- Freshness: how recent is that evidence? A signal from last week is worth more than one from last year.
Fit without intent is a cold prospect. Intent without fit is someone else's customer. Both without freshness is a memory. A Qualified Lead scores reasonably on all three.
A simple scoring model
Score each question from 1 to 10 and average them. Start simple and adjust after a month of results.
| Question | High (8–10) | Medium (5–7) | Low (1–4) |
|---|---|---|---|
| Fit | Aged 18–30, lives in a city you serve | Right age, other city | Outside your market |
| Intent | Member of an exam or study abroad community | Follows general education pages | No related signal |
| Freshness | Signal in the last 30 days | Signal in the last 6 months | Older than a year |
Leads above 7 go to sales or a direct offer. Leads from 5 to 7 go into nurture. Leads below 5 stay in ad audiences until a new signal appears.
Scoring a whole audience
The same logic works one level up, for the communities your leads come from. In every audience review, our research team gives an editorial Lead Potential Score built from four sub-scores:
- Size: how many members or followers the community has.
- Engagement: how many posts per month per thousand members.
- Intent Fit: how strongly membership signals a buying need.
- Freshness: whether the community is still posting and growing.
| Audience | Size | Engagement | Intent Fit | Freshness | Overall |
|---|---|---|---|---|---|
| HannahOlala | 9.5 | 7.0 | 8.0 | 8.5 | 8.3 |
| Vietnamese in USA | 9.0 | 8.0 | 8.0 | 7.0 | 8.0 |
| Columbia University | 9.5 | 6.5 | 7.0 | 8.5 | 7.9 |
| $100M Mastery Club | 9.0 | 4.0 | 8.0 | 5.0 | 6.5 |
| IELTS Forecast Group | 9.0 | 2.5 | 9.0 | 5.0 | 6.4 |
| IPL Scholarship | 7.5 | 3.5 | 8.5 | 5.0 | 6.1 |
| Keap | 9.0 | 3.0 | 8.5 | 2.5 | 5.8 |
The table shows why one number is never enough. The IELTS group has the highest Intent Fit in the set but low engagement, so it is a list to use now rather than a community to grow with. Columbia University scores lower on intent but high on freshness, which suits ongoing paid campaigns.
Common scoring mistakes
- Too many rules. Twenty criteria nobody understands are worse than three everyone trusts.
- No feedback loop. Compare scores with actual deals every month and adjust the weights.
- Ignoring freshness. Old intent quietly turns good leads into cold ones.
Scoring tells you who to contact. Contact Coverage Explained covers how you can reach them. Or explore scored audiences on QualifiedLead.
