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Social MediaintermediatePro

Build a LinkedIn performance learning loop from post analytics

Turn LinkedIn post performance into a practical learning system that improves future hooks, formats, topics, and founder-led content decisions.

What you will have

A recurring LinkedIn learning loop with post tags, performance analysis, content rules, and a next-month posting plan.

Setup time
2-3 hours
Time saved
3-5 hours per monthly content review
Estimated cost
$50 to $250 per month
Tools used
5 tools

Why this works

Most LinkedIn analysis stops at vanity metrics and generic advice. This workflow connects performance to specific post traits: hook type, format, topic, CTA, narrative structure, and audience response. Over time, the team builds a practical content playbook based on what their actual audience rewards.

Step-by-step workflow

Preview the workflow

The first 2 steps are open. Pro unlocks the remaining steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and implementation details.

1

Export recent LinkedIn performance

30 min

Export 30-90 days of post performance from Shield or your LinkedIn analytics tool. Include post text, publish date, impressions, reactions, comments, reposts, profile views, follower growth, clicks if available, and any inbound conversations created. Stage the data in Google Sheets.

Output

A clean LinkedIn performance dataset with post text and engagement metrics.

ShieldGoogle Sheets
Pro tip

Capture business-relevant notes manually. A post with modest likes but two target-account comments may be more valuable than a viral post with irrelevant engagement.

2

Tag posts by format, hook, and topic

45 min

Use Claude to classify each post by hook type, format, topic, point of view strength, CTA, story use, and audience target. Store the tags in Airtable so future content reviews do not start from scratch. Keep the taxonomy lightweight enough to use every month.

Output

Tagged LinkedIn post library organized by content traits.

ClaudeAirtableGoogle Sheets
Pro tip

Tag 'why it worked' separately from 'what it was about.' Topic and format are not the same thing.

Prompt template
Classify these LinkedIn posts into a reusable performance taxonomy.

Posts and performance data:
{{linkedin_posts_with_metrics}}

Business goals:
{{business_goals}}

For each post, tag:
1. Hook type
2. Format
3. Topic
4. Audience segment
5. Point of view strength
6. Story or example used
7. CTA type
8. Likely reason it performed or underperformed

Return the tagged dataset and a short definition for each tag.

Pro workflow preview

Previewing 2 of 7 steps

Pro membership

Unlock the full workflow

Get the remaining 5 steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and weekly new workflows.

$9/month

Separate vanity engagement from business engagement
Extract content rules from the winners
Create next month's posting plan
Draft posts with explicit pattern labels
Review monthly and update the ruleset
See Pro plan
3Separate vanity engagement from business engagement
Locked
4Extract content rules from the winners
Locked
5Create next month's posting plan
Locked
6Draft posts with explicit pattern labels
Locked
7Review monthly and update the ruleset
Locked

Expected results

Posts analyzed

30-90 posts

This range is enough to identify recurring patterns without overfitting to one week of performance.

Content rules created

8-15 rules

A practical ruleset should be small enough for writers to remember and specific enough to guide drafting.

Review time saved

3-5 hours per month

Structured exports, tags, and AI-assisted pattern analysis reduce manual spreadsheet review and subjective debate.

Content quality

Performance-backed planning

The next calendar is built from actual audience response, not generic LinkedIn best practices.

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