If you use influencer marketing at any level, you’re working with social media and the people who are masters at working the platforms. But how exactly do you calculate how large of an influence someone has in a single post? You don’t want someone to just be yelling into a void and hoping that they’re being heard. This is where we look at social media engagement.
Engagement is the total amount of interaction between people and a post. How engagement is calculated is different for each social media platform like Facebook and Twitter. But once you understand engagement, measuring it is simple math.
Unless you’ve been living under a rock, you know about the most standard parts of engagement when it comes to a Facebook post. These are pretty obvious, and they’re visible to anyone viewing a post.
- Likes: Total Likes or Emotion Reactions to a post
- Comments: Total comments on a post
- Shares: Total amount of times that someone shared a post
This isn’t the only way to look at audience interaction. Sometimes there are certain statistics that are only available to view if you are an admin of a Facebook page. On Facebook if you click “post details” you’re able to view a break-down of your post. These are called insights.
If you want insights from an influencer, just request snapshots of their Facebook, Twitter, or YouTube insights be sent to you to view.
- People Reached: Measures the number of people that saw a specific post in their news feed.
- Post Clicks: Measures total number of clicks on a post, not including comments, likes and shares. This includes every other type of click you can imagine (photo view, video play, reporting spam, expanding to read a post, expanding to read comments, clicking profiles within comments, etc.)
Twitter is another big social media platform where measuring engagement is easy. On Twitter we use likes and retweets to measure the engagement for a post.
- Likes: Total amount of times a user has liked the Tweet
- Retweets: Total amount of times a user has retweeted the tweet
Like Facebook, Twitter has some insights that you can view if it is your account. These Twitter Analytics can give you a more detailed breakdown of your tweets and how audiences have engaged it and it’s content.
- Tweet Impressions: Amount of times a Tweet has been seen
- Profile Visits: Amount of times a user clicks on the name, @handle, or profile photo of the Tweet author
Instagram, land of the visual post, is very uncomplicated when it comes to measuring engagement. This information is available to the public, so anyone who views the post. Currently there are no hidden clues for the admin about engagement. What you see is what you get.
- Likes: Total Likes on a photo/video
- Comments: Total comments on a photo/video
- Video Views: Total amount of times that someone watched your video. Note: photos do not show view count
YouTube is the number one reason why I stay up too late at night. Watching cute cat videos is addictive. But while I think adding cats into all your videos is a solid plan for increasing engagement, there are once again some key vocabulary you should know when looking at YouTube.
- Views: Total amount of people who have watched your video (Whether a person only watches one second of your video before turning it off, or watches the entire video, both count as a view.)
- Comments: Total amount of comments on your video
- Thumbs up/Thumbs down: Gauges how positively the video post is received
YouTube also has statistics for the person behind the curtain. YouTube dashboards display lots of useful information if you want a breakdown of how audiences actually look at posted videos. These are great to know because it doesn’t just tell you who clicked on the video, but how long they stuck around to actually watch it. Having a high view count isn’t helpful if everyone clicked away without really watching it.
- Watch Time: Estimated total minutes of viewing time of your video(s) from your audience.
- Average View Duration: Estimated average minutes watched per view for the selected content, date range, region and other filters