LinkedIn Post Analytics: Key Metrics to Track and How to Improve Them

You can’t improve what you don’t measure. LinkedIn post analytics tell you exactly what’s working, what’s not, and where to focus your effort. Yet most creators barely glance at their numbers.

This guide walks you through the key LinkedIn metrics, how to access them, and — most importantly — how to use analytics to make every post better than the last.

Key LinkedIn Post Metrics Explained

Impressions

Impressions count how many times your post appeared in someone’s feed. This is a measure of reach — how many people had the opportunity to see your content.

A high impression count with low engagement may indicate that your headline or first line isn’t compelling enough to stop the scroll.

Engagement Rate

Engagement rate is the percentage of people who interacted with your post (likes, comments, shares, clicks) relative to the number who saw it.

Formula: (Total Engagements / Impressions) × 100

A good engagement rate on LinkedIn is typically 2–5%. Anything above 5% is excellent. Below 1% suggests your content isn’t resonating with your audience.

Click-Through Rate (CTR)

CTR measures how many people clicked on a link in your post. This is especially important if you’re driving traffic to a website, landing page, or resource.

Note: LinkedIn’s algorithm deprioritizes posts with external links. If CTR is your goal, consider putting the link in the first comment instead of the post body.

Comments

Comments are the most valuable form of engagement on LinkedIn. The algorithm weighs comments more heavily than likes, and each comment increases your post’s visibility to the commenter’s network.

Not all comments are equal — longer, more thoughtful comments signal higher quality engagement to the algorithm.

Shares and Reposts

When someone shares your post, it gets exposed to their entire network — essentially free distribution. Shares indicate that your content was valuable enough for someone to put their name behind it.

Follower Demographics

Beyond individual post metrics, LinkedIn provides demographic data about your followers: job titles, industries, locations, and company sizes. This helps you understand whether your content is reaching your target audience.

How to Access LinkedIn Analytics

For Personal Profiles

Go to any of your published posts
Click “View analytics” below the post (visible only to you)
You’ll see impressions, engagements, and demographic breakdowns of who engaged

For Company Pages

Navigate to your company page
Click the “Analytics” tab
Browse Content, Visitors, Followers, Leads, and Competitors sections

Limitations of Native Analytics

LinkedIn’s built-in analytics are useful but limited:

No historical trend analysis across multiple posts
No easy way to compare post performance over time
No recommendations or actionable insights
Data is siloed — you have to check each post individually

This is where third-party analytics tools become valuable, offering dashboards that aggregate your data and surface patterns automatically.

Using Analytics to Improve Your Content

1. Identify Your Top Performers

Look at your 10 best-performing posts from the past 90 days. What do they have in common?

Topic or content pillar
Format (text, carousel, video, image)
Day and time posted (see our guide on best times to post)
First-line hook style
Post length

These patterns reveal what your specific audience responds to — far more valuable than generic best practices.

2. Track Trends Over Time

A single post’s performance can be misleading. What matters is the trend: are your impressions growing month over month? Is your engagement rate stable or declining?

Build a simple spreadsheet or use a tool to track weekly averages for impressions, engagement rate, and follower growth.

3. Measure What Matters for Your Goals

Different goals require different metrics:

Brand awareness: Focus on impressions and follower growth
Thought leadership: Track comments and shares
Lead generation: Measure CTR and profile views
Community building: Monitor comment quality and repeat engagers

4. Run Content Experiments

Use analytics to test hypotheses:

Do text posts outperform carousels for your audience?
Does posting at 8 AM vs 10 AM make a measurable difference?
Do posts with a question at the end get more comments?

Test one variable at a time and give each experiment at least 4–6 data points before drawing conclusions.

Analytics Mistakes to Avoid

Chasing vanity metrics: 10,000 impressions mean nothing if none of those people are your target audience
Overreacting to single posts: One underperforming post isn’t a trend. Look at 30-day rolling averages.
Ignoring qualitative signals: A post with 5 comments from ideal customers is more valuable than one with 100 likes from random connections
Not connecting analytics to strategy: Data is useless without action. Every time you review analytics, identify one thing to try differently. Build this into your content strategy review cycle.

Key Takeaways

Engagement rate (2–5%) and comment quality are the most important metrics for most LinkedIn creators
LinkedIn’s native analytics are a starting point, but third-party tools provide deeper insights
Analyze your top-performing posts to find patterns specific to your audience
Track trends over time, not individual post performance
Always connect your analytics review back to actionable changes in your content strategy