*Impressions Rate is the standard metric used by LinkedIn Analytics.
Understanding Your LinkedIn Engagement Rate
Calculating your LinkedIn engagement rate is crucial for understanding how well your content resonates with your professional network. Unlike vanity metrics such as total views alone, engagement rate measures the efficiency of your content by comparing interactions to the number of people who saw it.
The Formulas Used
This calculator provides two distinct metrics commonly used by social media managers:
Engagement by Impressions:((Likes + Comments + Shares) / Impressions) × 100. This is the most accurate measure of content performance relative to reach.
Engagement by Followers:((Likes + Comments + Shares) / Total Followers) × 100. This measures how active your specific audience base is, regardless of the algorithm's reach.
What is a Good LinkedIn Engagement Rate?
Benchmarks vary by industry and follower count, but general guidelines suggest:
1% – 2%: Good. This is a solid baseline for most corporate pages and personal profiles.
3% – 5%: Very High. Your content is sparking significant conversation.
Above 5%: Viral Territory. Exceptionally high resonance, usually driven by trending topics or highly personal storytelling.
Tips to Improve Engagement
To increase these numbers, focus on asking open-ended questions to drive comments, replying to every comment within the first hour, and using document posts (PDF carousels) which currently see high interaction rates on the platform.
function calculateLinkedInStats() {
// 1. Get input values
var likes = document.getElementById('li_likes').value;
var comments = document.getElementById('li_comments').value;
var shares = document.getElementById('li_shares').value;
var impressions = document.getElementById('li_impressions').value;
var followers = document.getElementById('li_followers').value;
// 2. Validate and Parse inputs
likes = (likes === "" || isNaN(likes)) ? 0 : parseFloat(likes);
comments = (comments === "" || isNaN(comments)) ? 0 : parseFloat(comments);
shares = (shares === "" || isNaN(shares)) ? 0 : parseFloat(shares);
impressions = (impressions === "" || isNaN(impressions)) ? 0 : parseFloat(impressions);
followers = (followers === "" || isNaN(followers)) ? 0 : parseFloat(followers);
// 3. Calculate Total Interactions
var totalInteractions = likes + comments + shares;
// 4. Calculate Engagement Rates
var erImpressions = 0;
var erFollowers = 0;
if (impressions > 0) {
erImpressions = (totalInteractions / impressions) * 100;
}
if (followers > 0) {
erFollowers = (totalInteractions / followers) * 100;
}
// 5. Update HTML Results
document.getElementById('res_interactions').innerHTML = totalInteractions.toLocaleString();
document.getElementById('res_er_impressions').innerHTML = erImpressions.toFixed(2) + "%";
document.getElementById('res_er_followers').innerHTML = erFollowers.toFixed(2) + "%";
// 6. Generate Assessment Badges
var impBadge = document.getElementById('badge_impressions');
impBadge.className = 'li-badge'; // reset class
if (erImpressions >= 3) {
impBadge.innerHTML = "Excellent";
impBadge.classList.add('badge-good');
} else if (erImpressions >= 1) {
impBadge.innerHTML = "Good";
impBadge.classList.add('badge-avg');
} else {
impBadge.innerHTML = "Low";
impBadge.classList.add('badge-low');
}
var folBadge = document.getElementById('badge_followers');
folBadge.className = 'li-badge'; // reset class
// Follower benchmarks are usually lower as reach is often = 2) {
folBadge.innerHTML = "High";
folBadge.classList.add('badge-good');
} else if (erFollowers >= 0.5) {
folBadge.innerHTML = "Avg";
folBadge.classList.add('badge-avg');
} else {
folBadge.innerHTML = "Low";
folBadge.classList.add('badge-low');
}
// 7. Show Results Container
document.getElementById('li_results').style.display = 'block';
}