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Social Metrics and Analysis Pitfalls

Tuesday, Oct 1st, 2013.

Businesses are investing more money and time into data and analytics. At the same time, they are also investing more into social media and community building. Showing analytics-based ROI on social media efforts can be a titanic challenge with the host of reporting tools that currently exist.

Many social media giants (Facebook, YouTube, Flickr, and, more recently, Pinterest, to name a few) have their own custom frames for analytics. Each one uses that particular platform’s jargon (page likes, views, clicks, loads, fans, followers, reach, engagement, etc.) to describe what’s being reported. But when a company or organization is using more than one social media channel, it can become difficult to convey proper comparisons across channels.

That’s why we really appreciate a post by Avinash Kaushik on his Occam’s Razor blog. In his article, Kaushik defines a number of metrics built out of similar branded metrics from various social media platforms. This includes conversation rate, amplification rate, applause rate, and economic value. we’re not going to go into the details of each metric; if you want to know how they are calculated, you can read Kaushik’s post. What we do want to talk about is reporting on unified metrics compared to individual metrics.

We at Agile appreciate standardization when comparing across channels. When a client wants to know how Facebook is doing compared to Twitter, for example, it can be very helpful to report on the amplification rate. We can tell our clients, “Your tweets are being re-tweeted at a much higher rate than your Facebook posts are being shared,” which translates into a higher amplification rate.

That’s very useful information, but there’s a catch: One problem with reporting unified metrics on social media channels is that each channel approaches sharing in different ways. In the above example, our client might be led to believe that not very many people are seeing their Facebook posts because they aren’t being shared, when in reality they are getting tons of exposure when Facebook users see their friends have liked or commented on the posts. Facebook shares posts with other users no matter the form of engagement.

For data to be useful, it has to be framed in the right way, with the proper understanding of what each metric really means, and in the context of other data. Take the time to know your data, or at least get someone else to do the heavy lifting for you. Without the proper understanding, your insights might fall flat.