Why score leads before outreach instead of after?

Your sending capacity is limited and personalization is only worth it on real prospects. Scoring first concentrates your effort on leads that can convert.

How does AI scoring read a profile?

It reads the whole profile, headline, summary, role, and company, and returns a score from 1 to 10 with a short reason, given your ICP description.

Why score leads before outreach instead of after?

Your sending capacity is limited and personalization is only worth it on real prospects. Scoring first concentrates your effort on leads that can convert.

How does AI scoring read a profile?| What happens to rejected leads?

They are logged with their score and reason. That rejected list is a mirror of your ICP and is how you refine your targeting over time.

Do I need Notion and Google Sheets?

The template uses Notion for input URLs and Sheets for output, but you can swap either for your own CRM or database.

How is this different from Clay?

Clay charges credits to enrich and score every row, so you pay across the whole list. This playbook drops obvious non-fits with free rules first, scores only the survivors with a cheap AI call, and drafts messages only for validated leads, so you spend on scoring and personalization where it matters, not on people who were never a fit.

What does Periodix Search return, and how does the AI use it?

Periodix Search returns clean, structured profiles for every result: name, headline, role, company, location, summary, and profile URL. The AI reads that structured data and scores each lead against your ICP, so the accuracy of the scoring comes directly from the rich, consistent fields the search returns, no manual cleanup needed.

Can I use OpenAI or Gemini instead of Claude?

Yes. The scoring and drafting steps are standard model calls, so you can run them on Claude, OpenAI GPT, or Google Gemini. The published template uses Claude, but swapping the model does not change the rest of the workflow.

How to Score LinkedIn or Sales Navigator Leads Against Your ICP and Draft Personalized Outreach

// WHEN TO USE

Problem

LinkedIn or Sales Navigator search returns hundreds of profiles, and most of them are not a fit.

Teams handle this in two expensive ways.

Some message everyone, which burns limited sending capacity, hurts reply rate, and trains them to blast more.

Others pay to enrich and AI-score every single row in a tool like Clay, spending credits on leads that were never going to qualify. Either way you pay full price for the whole list before you know who is worth it.

// WHAT YOU GET

Outcome

A funnel that spends effort only where it pays off. It collects clean, structured profiles from Periodix Search (name, title, company, location, and summary for every result), applies free rule-based filters to drop the obvious non-fits, then lets AI score only the survivors into validated and rejected, and drafts a personalized connection note and first message for the validated ones alone.

You do not blast everyone, and you do not pay to AI-score the whole list.

Validated leads land ready to send; rejected ones are logged with a reason to sharpen your ICP.

Waterfall outreach is usually expensive; run cheap-to-expensive, it is not.

// RESULTS

Proof

"We used to have SDRs do all of this by hand: pulling leads, scoring them against our ICP, and sending the same cold template. This workflow replaced that end to end, and now every first message is personalized, not a template. The output is as good as our best rep, and it runs every day without dropping a lead."

— Olga, Co-founder at Reivix

// BUILDING BLOCKS

Periodix Actions used

LinkedIn & Sales Navigator Search
Run a LinkedIn or Sales Navigator search by URL and get clean, structured rows back. Works for People, Companies, Posts, and Jobs on both Classic and Sales Navigator. Paste a search URL, and Periodix Actions returns the matching results with automatic rate limiting. This is the core of any LinkedIn lead generation or list-building workflow.

// HOW IT WORKS

Flow

Notion: pending Sales Nav URLsPeriodix SearchNormalize profilePre-filter (free rules, drop obvious non-fits)AI ICP score (1 to 10)AI draft note + message (validated)Google Sheets: Validated Google Sheets: Rejected (with reason)

// RUN IT

Implementations

Coming soon for this channel.

// OUTPUT SHAPE

Example JSON output

{
  "linkedin_url": "https://www.linkedin.com/in/jane-doe",
  "full_name": "Jane Doe",
  "headline": "VP Sales at Acme",
  "company": "Acme",
  "location": "San Francisco",
  "icp_score": 9,
  "reason": "VP-level at a 200-person SaaS, owns the buying decision for our category.",
  "connection_note": "Hi Jane, saw you lead sales at Acme ...",
  "message": "Following up with a quick thought on ...",
  "status": "validated"
}

// CONNECTS WITH

Integrations used

// FITS INTO

Use cases

Lead generation Data enrichment & scoring

// QUESTIONS

FAQ

Why score leads before outreach instead of after?: Your sending capacity is limited and personalization is only worth it on real prospects. Scoring first concentrates your effort on leads that can convert.
How does AI scoring read a profile?: It reads the whole profile, headline, summary, role, and company, and returns a score from 1 to 10 with a short reason, given your ICP description.
Why score leads before outreach instead of after?: Your sending capacity is limited and personalization is only worth it on real prospects. Scoring first concentrates your effort on leads that can convert.
How does AI scoring read a profile?| What happens to rejected leads?: They are logged with their score and reason. That rejected list is a mirror of your ICP and is how you refine your targeting over time.
Do I need Notion and Google Sheets?: The template uses Notion for input URLs and Sheets for output, but you can swap either for your own CRM or database.
How is this different from Clay?: Clay charges credits to enrich and score every row, so you pay across the whole list. This playbook drops obvious non-fits with free rules first, scores only the survivors with a cheap AI call, and drafts messages only for validated leads, so you spend on scoring and personalization where it matters, not on people who were never a fit.
What does Periodix Search return, and how does the AI use it?: Periodix Search returns clean, structured profiles for every result: name, headline, role, company, location, summary, and profile URL. The AI reads that structured data and scores each lead against your ICP, so the accuracy of the scoring comes directly from the rich, consistent fields the search returns, no manual cleanup needed.
Can I use OpenAI or Gemini instead of Claude?: Yes. The scoring and drafting steps are standard model calls, so you can run them on Claude, OpenAI GPT, or Google Gemini. The published template uses Claude, but swapping the model does not change the rest of the workflow.

// EXPLORE MORE

Related playbooks

// FROM THE BLOG

How to Score LinkedIn or Sales Navigator Leads Against Your ICP and Draft Personalized Outreach

Problem

Outcome

Proof

Periodix Actions used

LinkedIn & Sales Navigator Search

Flow

Implementations

Example JSON output

Integrations used

Use cases

FAQ

Related playbooks

Turn hiring signals into outbound leads

Mine LinkedIn post engagement for intent signals

Related articles

AI Lead Scoring: How to Qualify LinkedIn Leads by ICP Before You Reach Out

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