Estimated Reach is a crucial metric that estimates how many individuals engage with your brand. By analyzing Estimated Reach, you can gauge the actual visibility of your content and refine your media strategy to optimize audience engagement.

Estimated Reach is a measure of the estimated unique audience reached through mentions (posts or comments). This metric is based on engagement and other factors, ensuring an accurate reflection of visibility. See the How is Estimated Reach Calculated? section for network-specific calculation factors.

Estimated Reach is available in your dashboard engagement metrics (e.g. Total Reach widget and the Reach field on individual mentions. Filter your data by platform/social network to see specific reach estimates for mentions.

Existing Reach values may change because the methodology calculates Reach differently across networks..

Once Estimated Reach is enabled, newly collected mentions will use the new Estimated Reach automatically. To backfill historical data, click the Backfill new Reach values for historical data button at the top of your dashboard.

Estimated Reach is calculated differently by social network:

Estimated Reach is calculated using the Reddit post’s comment count. As the comment count increases (more conversation), Estimated Reach increases. You have the option to historically backfill Estimated Reach for Reddit content.

Full formula:
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​y = mx + A (exp (kx) - 1)

y = Estimated Reach (output)

x = derived comment count

m = 111

A = -549,000

k = -0.00193

We estimate Reach on Instagram using a non-linear model, so there’s no single equation that explains the prediction. This allows us to model more complex behaviors, such as the relationship between likes and reach for video posts, which will be different for big channels compared to smaller channels.

The model uses multiple curves to estimate how many people have seen your content. The curves describe the relationship between likes and reach.

Three factors affect the final prediction for Instagram Estimated Reach: Likes, account size, and content type.

The Engagement Curve (the "Likes" factor): Reach doesn't grow in a simple straight line. Instead, it follows a curved path.

The Early Surge: When a post first starts getting likes, it reaches new people very quickly.

The Leveling Off: As the post gets older or reaches its peak audience, each new like adds a little less extra reach than the first few did.

This behavior is modeled using a curve. By looking where the likes count falls along the curve, we get a value for reach.

Follower Weight (the "Account Size" factor): The relationship of a like to reach depends on the follower count of the post’s author. We group accounts into five categories: Lowest, Low, Medium, High, and Highest.

To make sure your estimates are always smooth, we don’t use "hard" categories. If you are currently growing from "Low" to "Medium," our model blends the reach values from both groups. This prevents your estimates from making huge, unrealistic jumps the moment you hit a certain follower milestone.

Different Post Type Curves (the "Content Type" factor): When looking at how different posts perform, we see that the type of post performs differently at the same account size and likes count. For each of these different types of posts, we use a separate set of curves. There are curves for Image, Video, and Carousel posts.

These curves allow us to model complex behavior like one type of post getting more likes to reach at a higher follower count, or a post type tending to get a lot of views early on and then slowing quickly for a certain size of channel.

We’ve built a safety check into the system called Monotonicity. In simple terms, this means the model is programmed to ensure that more followers should never result in less reach. If the raw data ever shows a larger account getting a lower estimate than a smaller one for the same number of likes, the model automatically bumps the larger account up to the higher value. We want to ensure your growth is always rewarded with more accurate, positive predictions.

Three factors affect the final prediction for Facebook Estimated Reach: Reactions, account size, and content type.

Facebook Estimated Reach works using the same model as the Instagram reach model with different values for the curves and some minor differences to adapt to Facebook:

Use of Facebook’s post types: Status, Link, Photo, Video and Event.

Use of Reaction Count, which is the sum of all different Facebook reactions, instead of Likes count.

Use of Fan Count instead of Followers.

Two factors affect the final prediction for TikTok Estimated Reach: Likes and content type.

TikTok Estimated Reach works using a very similar model to the Instagram reach model, with one major difference. As Follower count for users is unavailable today, Follower count has no effect on reach for TikTok Estimated Reach. So, TikTok reach also does not need to implement follower count curve weighting or monotonicity.

The other minor difference is the use of TikTok's post types: Video and Reply posts.

For X (Twitter) and Threads, we use the publicly available views count to estimate reach. Since the views of a post are very related to the reach of a post, we can use a simple calculation to find Estimated Reach.

Estimated Reach = Views x De-duplication Factor

The de-duplication factor is an estimate of how many people have viewed a post multiple times. There are different de-duplication factors for X (Twitter)'s and Threads's different post types: Text, Photo, Video, Link, Animated GIF, and Poll posts.

Our Bluesky calculation uses Likes and Followers to estimate reach. We use a different style of model than other models that use likes and followers, using a single formula.

Bluesky reach = a × Likesᵇ × Followersᶜ

Our reach calculations are the same for all types of Bluesky post.