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ABOUT PERSOLLO

We are an Australian company powered by awesome brands and bloggers, for all those doing what they love. We crafted Persollo to help brands, big and small, and influencers to sell their goods across social networks without the need of building sophisticated and expensive e-commerce sites or online shopping carts. Our crazy simple and fast instant checkout turns 'likes' into 'sales', while increasing conversion and delivering the highest ROI from social media and influencer marketing. 

 

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How to Calculate an Influencer’s Engagement Rate in 2019? (benchmarking & scientific approach)

June 17, 2019

Engagement Rate (ER) is an important social media metric that is the currency of the social media marketing industry. This easy metric helps brands and agencies to measure the effectiveness of influencer actions and better understand how their audience engages  with their content. ER helps to determine which influencers to work with based on how their content performs across different platforms. Simply put, it is the percentage of the influencer’s audience that responds and reacts to their content. Think likes, comments, and shares.

 

Now that we’ve defined what engagement rate is in influencer marketing we’re ready to dive into the details and discuss how to measure an influencer’s engagement rate and what constitutes good engagement rate.

 

How to Calculate Engagement Rate

 

There are several most common manual ways to calculate engagement rate on social media.

 

This formula is typically used to calculate engagement rate by post (ER post):

 

  • ER (%) = (Total engagements on a post / Total followers) * 100%

  • Average ER (%) = ER (%) / Total Posts

 

Most social media influencers and brands manually calculate their average engagement rate on Instagram this way:

 

  • ER (%) on Instagram = ((Likes + Comments) / Follower Counts) * 100%

  • Average ER (%) on Instagram = ER (%) on Instagram / Total Posts

 

For example, let's say influencer has 100,000 followers, 2,000 likes and 300 comments on his / her last post. In that case:

 

  • ER (%) on Instagram = ((2,000 + 300) / 100,000) * 100% = 2.3%

 

If last post engagement = 2.3% and second last post engagement = 2.1%, then:

 

  • Average ER (%) on Instagram = (2.3%+2.1%) / (Total Posts = 2) = 2.2%

 

You can do it manually or you can have Persollo do all the work for you!

 

How Persollo Calculates Engagement Rate - Scientific Approach

 

In June 2019, Persollo ran a benchmark study, analysing the engagement rate on millions of influencer accounts. The covered pool of accounts was divided into 10 groups, based on the number of followers.

 

 

We calculated the average engagement rates for each group of similar accounts and set the benchmark for the groups. We haven't used the traditional formulas, but came up with our own calculation.

 

To achieve further depth in analysis we then ran analysis of trends in each group of similar accounts - and set benchmarks for the average engagement rate, likes, comments per the last 3, last 7 and last 12 posts.

 

 

Here is our dynamic benchmark on Average ER (%) on Instagram per aggregated groups:

 

 

Nano Influencers are those influencers who has less than 1k followers.

Micro Influencers are those influencers with 1k - 50k followers.

Macro Influencers are those influencers with 50k - 500k followers.

Super Influencers are those influencers with 500k - 3M followers.

Super Stars are those influencers with more than 3M followers.

 

The average ER on Instagram in 2019 is 2.81%, but this number is irrelevant because the ER depends on the follower count.

 

When checking influencer's ER you should always compare apples-to-apples and compare the engagement rate of influencer to other influencers in the similar group with similar follower count and similar trends across post engagement metrics.

 

With Persollo - you can easily run similar analysis on any influencer account. You can calculate influencer's engagement rate and you can also analyze it and compare it with other bloggers with the same size of following. Based on the output of Persollo unsupervised machine learning algorithms, each engagement rate is assigned into Very Low, Low, Medium, High and Very High group.

 

Persollo Post Analyzer Example is here.

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