How we calculate engagement rate at OwlScran

Engagement rate should tell you something simple: when people see a creator's content, do they actually engage with it?

There is no single standard way to calculate it. Some tools divide engagements by followers. Others use reach or views. They often disagree on what counts as an engagement, too. So two websites can look at the same creator and report two different rates.

This is how we calculate it at OwlScran, and why.

The short version

Our main engagement rate is:

Total engagements ÷ content views × 100

An engagement is an interaction with the content. Depending on the platform, that can include likes, comments, shares and saves.

So if a creator averages 20,000 views, 800 likes, 80 comments, 70 shares and 50 saves, that is 1,000 engagements from 20,000 views:

1,000 ÷ 20,000 = 5%

That is the rate we normally highlight.

We also show rates based on reach and on followers where we have the data. Wherever a rate appears, you can open the ⓘ to see the numbers behind it and exactly how we calculated it.

Why we don't default to followers

The traditional formula is:

Engagements ÷ followers × 100

A creator with 100,000 followers and 2,000 engagements has a 2% follower-based rate. It is easy to calculate, still widely used, and we provide it.

Recommendation feeds put content in front of people who do not follow the creator. In the 27 August 2026 edition of the OwlScran Creator Index, the median share of Instagram views from non-followers is 82.0% (middle half 66.9% to 92.8%, n = 45). On YouTube it is 95.1% of views from people who were not subscribed (middle half 86.0% to 98.8%, n = 32). TikTok and Facebook connected APIs do not return this split, so those cells are not available.

That is a percentage of views, not unique accounts reached, and not a raw view count. Follower count is not how many people saw the post.

So 100,000 followers is not 100,000 chances to engage. The same creator might get 20,000 views on one video and 500,000 on another. Dividing both by 100,000 followers hides that difference.

Follower-based engagement still says something useful about performance relative to audience size. It is not the best number for how the content itself performed.

Why we use views

Views are closer to what actually happened. If a video gets 20,000 views and 2,000 engagements, the rate is 10%: ten engagements for every 100 views. The engagement and the chance to engage are the same event.

It also treats creators whose content travels past their follower graph more fairly.

Neither calculation is wrong. They tell different stories. Creator A is getting distribution far beyond their follower base. Creator B's content earns more engagement each time it is watched.

For a brand trying to understand how the work performs, those are more useful facts than engagements divided by follower count.

We can see more than a public scraper

Creators connect their accounts directly to OwlScran, so we can often use first-party analytics that are not on the public profile.

On Instagram that can include likes, comments, saves and shares. A tool looking at the profile from the outside may only see likes and comments.

Saves and shares are useful signals. A save means someone wants to come back to it. A share means they passed it on. When the platform gives us those metrics, we include them.

What counts as total engagement still varies by platform, because the platforms do not give us the same data. We always show which metrics went into the calculation.

Why our number might not match another site

There is no industry-standard engagement rate. Another platform might use likes + comments ÷ followers, while we have first-party data for likes + comments + saves + shares ÷ views. Those numbers should not match. They are measuring different things.

We default to views because content consumption is a better denominator than potential audience size. We still show the follower-based rate next to it.

Showing our working

We want the numbers to be easy to check. Wherever we can, we show the data behind a metric.

For example: 8.4% engagement rate

• Average views: 24,200
• Average likes: 1,420
• Average comments: 92
• Average saves: 310
• Average shares: 211
• Total engagements: 2,033

2,033 ÷ 24,200 = 8.4%

If saves are not available on a platform, they will not appear in the calculation.

We also show engagement by reach

Where we have the data:

Total engagements ÷ accounts reached × 100

Reach is unique accounts who saw the content. Views are times it was viewed. One person can watch the same video twice and still count as one person reached.

That makes reach-based engagement a useful measure of how the people who actually saw it responded.

The catch is availability. We have reach for Instagram and Facebook. We do not have a comparable reach figure on every platform.

So views is the default we highlight in the product, because it exists everywhere we operate. Where we have reach, including in the Creator Index for Instagram, we publish that too.

And we still show engagement by followers

Total engagements ÷ followers × 100

It is widely used in influencer marketing, and it is useful when a brand is comparing you with a report that still uses this formula. We do not highlight it as the main rate. We keep it alongside the others.

Engagement rate does not tell the whole story

Two videos can share a 5% engagement rate and still do very different jobs.

Same rate. Video B was shared 17 times more often. For a brand that wants content people pass on, that is the whole story.

So we do not try to crush every signal into one number. We show the pieces as well.

Share rate

Shares ÷ views × 100

In the table above, Video A is 0.1%. Video B is 1.7%. That does not predict a video will go viral. It does tell you something the overall rate hides: how often people chose to send it on.

Save rate

Saves ÷ views × 100

A save usually means someone wants to come back: recipes, workouts, travel lists, tutorials, product round-ups. We do not weight saves more heavily inside the main engagement rate. We show save rate on its own, so a brand can see the signal without us deciding that one save equals three likes.

Comment rate

Comments ÷ views × 100

A high comment rate might be an active community, a video that starts an argument, or a lot of questions. The context matters. The rate is still a useful extra lens.

Why we don't weight likes, comments, saves and shares

You can build an engagement rate that scores a like as 1, a comment as 2, a save as 3, a share as 3. Some analytics tools do that.

The hard part is not the maths. It is deciding the weights. Why is a share worth three likes rather than two or five? Is a save more valuable than a comment for a recipe creator and a comedian?

Different interactions are valuable for different reasons. We would rather show them separately than bake those guesses into the headline rate.

If we build a broader impact score later, it will be a separate metric with its own explanation. It will not quietly change what "engagement rate" means.

Which rate should you use?

They answer different questions.

Engagement by views. How much engagement does the content generate relative to how often it is watched? This is the rate we highlight. You can run a follower-based version yourself on the Instagram, TikTok and YouTube calculators.

Engagement by reach. How much engagement among the unique people who saw it? We show this where we have reliable reach data.

Engagement by followers. How much engagement relative to audience size? We show this because the rest of the industry still uses it.

The useful part is being explicit about which one you are looking at.

Social platforms do not all give us the same data, and there is no single standard. We show the most comparable metric first, then the other calculations and the raw numbers for anyone who wants to go deeper.

If you think we have a metric wrong, or we are missing something useful, email contact@owlscran.com.