AI-Powered LinkedIn Content Strategy Using Claude And Your Data

I don’t rely on generic LinkedIn advice anymore. I use custom analytics and semantic analysis to understand what actually works. In 2026, your own data is the only real hack left. Export your posting history and analyze it with this prompt based on my framework:

"I want to analyse my LinkedIn publishing history to identify what works, what does not, and build a strategy for maximum reach and follower growth.
Attached is my LinkedIn statistics export containing all posts since I started publishing. The file includes impressions, reactions, comments, saves, followers gained, and publishing timestamps for each post.

Please produce a structured performance report covering the following sections:

1.Calculate median and mean for impressions, reactions, comments, saves, and followers gained per post. These are my baseline numbers.

2. For each post, calculate saves divided by impressions multiplied by 1,000. This is the RVI: how many people out of every thousand who saw the content thought it was worth keeping. Classify each post: below 1.0 is consumed and forgotten, 1.0 to 3.0 is useful content, above 3.0 is reference material. Show the posts with the highest RVI and identify what they have in common.

3. Classify every post as strong or very strong using these thresholds: strong means 3,000 or more impressions, or 30 or more reactions, or 3 or more followers gained. Very strong means 7,000 or more impressions or 10 or more followers gained. Calculate the strong post rate per month and show whether it is improving or declining over time.

4. Identify what the strong posts have in common. Look at content type, opening sentence structure, topic, length, publishing slot, and whether an attachment was present. Rank the drivers by how strongly they correlate with strong performance.

5. Identify what the weak posts have in common. Name the specific patterns (publishing slot, content type, topic, structure) and explain why each one likely suppressed performance.

6. Break down performance by day of week and hour of day. Show median impressions and strong post rate for each combination. Identify the best and worst slots.

7. Group posts by content type or topic theme and rank them by median impressions. Show which formats consistently outperform and which consistently underperform.

8. Calculate profile views per post where available and followers gained per post. Identify which content types drive the highest follower conversion relative to impressions.

9. Based on all of the above, write five to seven specific editorial rules for this account, things that should always be true about every post based on what the data shows.

10. Show how performance has changed across years and months. Identify whether the account is improving, plateauing, or declining, and what changed when performance shifted.

Please present the report with clear section headers, specific numbers, and concrete examples from the actual post data rather than general observations. Where patterns are ambiguous, say so rather than forcing a conclusion."

Видео AI-Powered LinkedIn Content Strategy Using Claude And Your Data канала Kateryna Babenko