Prospecting

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%)

How it works

01

Upload 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.

02

AI 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).

03

Focus 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.

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.

1

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.

2

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.

3

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.

4

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

CompanyDecision MakerICP ScoreFit RationaleSignalsAction
FinScale AITom B. (CRO)94%Series B, EMEA HQ, 180 employeesHiring SDRsOutreach now
CloudEdgePriya S. (VP Sales)88%Series A, UK, 65 employeesProduct matchOutreach now
DataNestMarc F. (CEO)72%Seed stage, below ideal sizePartial matchNurture
RegOpsLisa T. (Head of BD)91%Series B, Germany, 210 employeesActive hiringOutreach now
PayStreamAhmed K. (CRO)65%Too early stage, limited signalsLow priorityRevisit 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?
ICP scoring uses firmographic signals (size, industry, geography, stage) plus behavioral signals (hiring patterns, recent funding, product signals from job posts). You can customize which signals matter most.
Can I customize the scoring criteria?
Yes. Describe your ICP in plain English, or provide a weighted scoring rubric. Kuration adapts to your specific criteria.
How is this different from Apollo's filters?
Apollo filters are static — headcount range, industry, title. Kuration's AI scoring reads context: a company hiring 5 SDRs signals active growth in a way a headcount filter can't.
Can I score a list I extracted from another template?
Yes. Lead scoring works on any Kuration output or imported list. It's designed to be the final step in any research workflow.

Try Lead Scoring now

No credit card required. Results in under 90 seconds.