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Tag: R

Maximize Your Facebook Post Reach with Statistics (in R)

When working with clients on social media marketing projects, I’ve always been fascinated by ways to bring quantifiable results and hard statistics into consultant-client conversations. Seeing numbers and graphs to represent the progress that a brand has made on its social media properties is incredibly reassuring to a client, especially when compared to the vague assertions that other so-called “experts” will pass off as justification for their fees (e.g. “You’re getting a lot more user engagement now that you hired me!”).

I recently saw a great opportunity to pull out some of my old high school AP Statistics learnings when brainstorming ways to improve Facebook post reach. Any page owner could tell you that it’s discouraging to see what a low proportion of their audience actually sees any particular post- in my experience an average (unpromoted) post reaches less than 30% of a page’s audience. While there’s certainly a time and place for paid promoted posts (and other ads) in any brand’s Facebook strategy, it’s important to do everything possible to make sure that every post reaches as many customers as possible.

I’ve heard a number of different ideas for strategies on how to accomplish that- some marketers say to make sure you post a lot of a particular kind of content, like photos. Some assert that it’s all about frequency of posting and post scheduling. A very popular strategy in the live music industry is to have a core street team that will engage with every post on the page- liking, commenting, and/or sharing it. All of these strategies have their own benefits- having an optimal content mix and posting schedule is important from an audience interest and engagement perspective, and making sure that every post is engaged with can create powerful social proof.

But do any of these things actually influence Facebook page reach? I set to find out using some statistical tests (calculated by using the statistical programming language R), and some dreadfully boring data collection work (hint: maybe outsource this bit). I’ll share some of my generalized findings on all this below, but more importantly, here’s how to run a similar test for yourself. By completing these tests, you’ll gain insights that will help with determining post types, scheduling, and seeing if engagement numbers bear significant influence on your reach.

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