Score leads before you send a single email
Upload any prospect list. AI scores each lead against your ideal customer profile. Focus on the top 20% — better reply rates, fewer burned domains.
What you provide
Prospect list + ICP description
e.g. prospect_list.csv + "Series A–C SaaS companies in EMEA with 50–500 employees"
What you get
Scored list with ICP fit %, rationale, and recommended action
✓ From 500 prospects → 97 high-priority targets (top 20%)
Why people use this
The list is public. Getting it out is the problem.
Nothing here is secret. It is published, on purpose, so people can find it. What it is not is usable: it is spread across hundreds of pages, and it names companies rather than people you can write to.
By hand
- 1Read every row and form an opinion
- 2Argue about what counts as a good fit
- 3Sort by something proxy like headcount
- 4Email the whole list anyway because cutting it feels wasteful
An afternoon, and no two people agree
and nobody can say why a row scored what it scored
With Lead Scoring
- 1Upload your prospect list and describe your ICP. Paste your list and describe your ideal customer: industry, company size, stage, geography, tech stack, or behavioral signals like recent hiring or funding.
- 2AI scores each prospect. Kuration evaluates every lead against your ICP, assigns a fit percentage, explains the rationale, and flags relevant signals (hiring, funding, product news).
- 3Focus on your top 20%. Filter to high-score leads, export a prioritized list, and route directly to your SDR team. Less spray-and-pray, more precision.
From 500 prospects → 97 high-priority targets (top 20%)
and every row arrives with a named person and a verified address
The playbook
How to get the most out of Lead Scoring
Specific, tactical ways teams run this template on Kuration — not just what it does, but how to win with it.
Describe your ICP in plain English
Industry, size, stage, geography, and the signals that matter. No rigid filter menus, just tell Kuration what a good customer actually looks like for you.
Weight the signals that predict deals
Tell it to value active hiring or a fresh raise over raw headcount, so a fast-growing company shows up even when a static size filter would have hidden it.
Work the top 20% first
Filter to the high scores and route them to your SDRs. Fewer sends, better reply rates, and far fewer domains burned on prospects who were never a fit.
Score any list, from anywhere
Run it on a Kuration output or an imported CSV. It is the final step that turns a raw list into a priority order your team can work top to bottom.
Example output
Sample output — actual results vary by query
| Company | Decision Maker | ICP Score | Fit Rationale | Signals | Action |
|---|---|---|---|---|---|
| FinScale AI | Tom B. (CRO) | 94% | Series B, EMEA HQ, 180 employees | Hiring SDRs | Outreach now |
| CloudEdge | Priya S. (VP Sales) | 88% | Series A, UK, 65 employees | Product match | Outreach now |
| DataNest | Marc F. (CEO) | 72% | Seed stage, below ideal size | Partial match | Nurture |
| RegOps | Lisa T. (Head of BD) | 91% | Series B, Germany, 210 employees | Active hiring | Outreach now |
| PayStream | Ahmed K. (CRO) | 65% | Too early stage, limited signals | Low priority | Revisit Q3 |
This template is perfect for:
- SDR teams prioritizing outbound outreach to maximize booked meetings
- Agencies delivering scored, prioritized lists to clients
- RevOps teams cleaning and qualifying inbound pipeline
- Growth teams focusing resources on highest-probability accounts
Frequently asked questions
What signals does the scoring use?
Can I customize the scoring criteria?
How is this different from Apollo's filters?
Can I score a list I extracted from another template?
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