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How Engagement Prediction Works

Learn how to use FeedHive's Post Conditions to create advanced, event-driven posting behavior
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Simon Høiberg

CEO & Founder

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4 min read

Predict how well your post will perform

With FeedHive's new AI-powered tool, you can now predict how well your post will perform.

In this article we will cover:

  • What we mean by "perform"
  • How this tool works behind the scenes
  • How to interpret the result
  • FAQ

What we mean by "perform"

When we say "perform", we mean how likely your followers are to engage with your post.

So, the result does not indicate how many likes your posts will get.
Neither does it try to predict if the post will go viral.

Specifically, the score is a measure of how likely your post is to obtain a high engagement rate.

ℹ️ Engagement rate is the number of total engagements on a post (likes, comments, shares, link clicks, profile clicks, etc) divided by the total number of views.

So, in short: The higher the score, the higher engagement rate you can expect.

Even though the prediction tool doesn't attempt to predict likes, total views or virality, it's worth noting, that there is a direct correlation between engagement rate, and the likelihood of your post going viral and getting a high number of likes, comments and views.

How does this tool work behind the scenes

We analyzed more than 100,000 posts from FeedHive and used it to build a powerful machine learning model.
The reason why used engagement rate as the target variable is because:

  1. It's the most important metric to optimize your post agains.
  2. It's the most fair metric to judge other posts performance based on.

If we had trained our AI model on likes or views, accounts with a large following would always out-perform accounts with few followers.
That wouldn't be accurate.

So, in an extremely simplified version, we rank all posts in our database based on their engagement rate, and train our AI based on that ranking.
One by one, we are telling the AI that "this is what a highly-performing post looks like", and "this is what a poor-performing post looks like".

After doing that many, many times, the AI becomes quite good at understanding the common patterns that makes up a high-performing post.

How to interpret the result

The result consists of two parts:

  • An overall score from 0-100
  • A histogram showing how similar the post is to the posts from the trainingdata grouped by performance.

Example 1: A low performance post

In this example, the post has mostly similar traits to posts from the trainingdata that performed poorly.


Example 2: A mid-low performing post

In this example, the posts that has similar traits are a bid more spread out, and in the lower-middle part of the spectrum.


Example 3: A mid-high performing post

In this example, we see more similar traits with posts in the higher-middle part of the spectrum.


Example 4: A high performing post

In this example, the posts has mostly similar traits to posts from the trainingdata that performed well.



  • Does this also work on small accounts?

    Yes, it does.
    We have trained our AI on a large dataset with posts from both small and large accounts.
    We use engement rate as the target metric, so the prediction score is not based on any minimum number of followers, likes or views.

  • Does it take into account the time of the day

    No, it does not. It only takes into account the text content of a post.
    However, we are working on enhancing the AI to take into account the time of the day as well.

  • Does it take into account images or threads?

    No, it does not.
    But it is something we are working on as well.

  • How do I know what to do to improve the score?

    At the moment, it's a trial-and-error things.
    But we are working on a solution that allows our AI to generate suggestions on how to improve the post.