Your social data needs a context

A few weeks ago I had a discussion on the Internet about social media data. It sounds like a futile exercise, and for the most part it was. It however spurred me to write something up on the matter, so you could say it wasn’t all bad (you would be wrong though, arguing on the Internet is as bad as it sounds).

One of the biggest fallacies that I come across when I see discussions about social media, is that your data is a gold repository that can be effortlessly mined. For some people, and in some businesses, that is definitely true, but to elevate it to being a universal truth is not only wrong, it could potentially be harmful.

But before I go any further, let me throw a statement out there:

Context is king

When you work with social data, context is the most important factor that you need to consider in your analysis. Period.

Consider the following three data points:

  • Content piece X has reached 5,000 people.
  • Content piece Y has reached 5,000 people out of 10,000.
  • Content piece Z has reached 5,000 people out of 10,000, with 100 responses.

Which one do you reckon someone else will be most likely to understand and contextualize within their own area of expertise?

Contextualization is an art

When our head of sales and partnerships in Astralis are pitching ideas, concepts and campaigns to potential clients and partners, it is of utmost importance that the data I give him is contextualized. If he just grabs our most recent social media statistics, we lose the subtle art of contextualizing the stats in a way that suits the project.

Unless you are one of those horrible “always on” brands that do a football backdrop for your candy and ask “who’s ready for some football this Summer?”, your activity will rise and fall over the course of a year. The same is true for Astralis, even more so because the breadth of our activity is centered around tournaments that take a lot of attention and leave the audience “fed” for a few days afterwards.

That ebb and flow will not show up in the raw data. You have to have someone who is familiar with the data to paint that picture. What could be a Picasso might be interpreted as a crude five year old’s attempt to draw a face without the proper context.

And contextualizing is a subtle art. Despite what your run-off-the-mill Social Media SaaS provider claims, the best work is done by humans. Consider this a hopelessly skewed example:

According to our social backend, the post below was the best in August. It’s hard not to agree with it: Hitting 44% of your fans and having an organic reach that exceeds your overall fanbase on Facebook is not an ordinary result.

Now this is where it gets tricky. Our social backend consider “organic reach”, “fan reach” and “engagement” to be the most important contextual values for our posts. Let’s see how that works for the “worst posts”:

Okay it seems like we whiffed on these ones. Oh wait! The posts are in Danish and not in English, which means that we have targeted these posts for our Danish audience only. I ran the numbers and this is the organic reach of the posts in the Danish audience.

Post 1: 37.8%

Post 2: 54.9%

Post 3: 56%

So in the context of how many people these posts hit, compared to the part of the fanbase it was served to, two of these posts actually did better on all three parameters.

But what about the post type? Is it an image, is it a video? What about the time it was posted? Did it cater to our audience? The truth is that “best” and “worst” is not something that an algorithm, with no input from the end user, can determine. Maybe my aim is to drive traffic to a website. In that case, do I care how many people have commented on my posts?

Why is this important?

Well first of all we would all like to understand our data a little better, right? It should go without saying that providing a proper context to the data will allow you, or anyone else looking at it, to have a much better base understanding of it.

Secondly it does away with the fallacy that data is effortlessly minable with a quick (and beneficial) outcome. Despite the fact that social media sites often offer it in fancy piecharts and graphics that are easy on the eye, analysing data is hard work.

Thirdly, contextualizing your data means that you start to bracket it according to your goals or ambitions. If your goal is to drive conversions to a webshop, you start looking at click through rates. Maybe you have a post with a high CTR but a low reach. The context you attribute to that determines what to do next. If the context is that it hit a very small subset of your fans, but it drove a high percentage to your website, you want to do a paid push to a similar audience outside of your current followers. The worst thing you can do is look at it and determine it was unsuccessful because it didn’t hit a large audience.

Be the hero your company (doesn’t) deserve!

The real reason to practice in contextualizing data is however that it will make you smart and valuable. Anyone can pull big numbers out of a Facebook insights report, put them in a fancy slideshow and shove them in the faces of C-levels. And sometimes that will work. You might get that raise; you might get to spend more money on boosting posts and creating pretty stats for pretty slides. But it won’t create value.

Being able to properly contextualize your data and make it readily available for your Sales, PR and Marketing department to act on is not only an impressive feat, it is also a very sought after quality. The better you are at explaining the data you have, the better your company can utilize it and the more important your position become.

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