Content Performance Optimization

Content Performance Optimization

Track every piece of content

Required fields:

• content_id
• type (blog, tweet, thread, linkedin, reddit, newsletter)
• title / first line
• published date
• channel
• target audience (builders, labs, community)
• primary data point used
• views / impressions
• engagement rate
• signups attributed (if trackable)
• enterprise inquiries attributed (if trackable)

Monthly content review

1. Identify top 5 performing pieces (by views + signups + shares combined)
2. Identify bottom 5 performing pieces
3. Find the pattern between them
4. Adjust the following month's content mix accordingly

Pattern examples

• "Data comparison posts outperform methodology posts 3:1" → shift mix toward more data posts
• "Tweets with specific percentages get 2x more engagement" → always include a number
• "Tuesday threads get 40% more impressions than Thursday threads" → post threads on Tuesday

A/B testing

Test one variable at a time:

• Different tweet formats for the same data point
• Different email subject line patterns
• Different blog title structures Run each test for 7 days minimum. Apply winner immediately.

The optimization loop

Publish → Track (7 days) → Analyze → Identify pattern → Adjust next week's content

Monthly deliverable

One-page content performance summary with:

• Best 3 pieces with reason
• Worst 3 pieces with reason
• One content mix change for next month
• One format test to run next month