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Visualizing Social Media Analytics: Beyond the Bar Chart

Recently, I rediscovered a TED Talk by David McCandless, a data journalist, called “The beauty of data visualization.” It’s a great reminder of how charts (though scary to many) can help you tell an actionable story about a topic in a way that bullet points alone usually cannot. If you have not seen the talk, I recommend you take a look for some inspiration about visualizing big ideas.

 

In any social media report you make for the brass, there are several types of data charts to help summarize the performance of your social media channels; the most common ones are bar charts, pie/donut charts and line graphs. They are tried and true but often overused, and are not always the best way to visualize the data to then inform and justify your strategic decisions. Below are some less common charts to help you tell the story about your social media strategy’s ROI.

 

For our examples here, we’ll primarily be examining a brand’s Facebook page for different types of analyses on its owned post performance.

 

Scatter plots

Figure 1: Total engagement vs total reach, colored by post type (Facebook Insights)

What they are: Scatter plots measure two variables against each other to help users determine where a correlation or relationship between those variables might be.

 

Why they’re useful:  One of the most powerful aspects of a scatter plot is its ability to show nonlinear relationships between variables. They also help users get a sense of the big picture. In the example above, we’re looking for any observable correlations between total engagement (Y axis) and total reach (X axis) that can guide this Facebook page’s strategy. The individual dots are colored by the post type — status update (green), photo (blue) or video (red).

 

This scatter plot shows that engagement and reach have a direct relationship for photo posts because it makes a fairly clear, straight line from the bottom left to the upper  right. For other types of posts, the relationships are less clear, although it can be noted that video posts have extremely high reach even though engagement is typically low.

 

Box plots

Figure 2: Total reach benchmark by post type (Facebook Insights)

 

What they are: Box plots show the statistical distribution of different categories in your data, and let you compare them against one another and establish a benchmark for a certain variable. They are not commonly used because they’re not always pretty, and sometimes can be a bit confusing to read without the right context.

 

Why they’re useful: Box plots are excellent ways to display key performance indicators. Each category (with more than one post) will show a series of lines and rectangles; the box and whisker show what’s called the interquartile range (IQR). When you look at all the posts, you can split the values up into groups called quartiles or percentiles based on the distribution of the values. You can use the median or the value of the second quartile as a benchmark for “average” performance.

 

In this example, we’re once again looking at different post types on a brand’s Facebook page, and seeing what the total reach is like for each. For videos (red), you can see that the lower boundary for the reach is higher than the majority of photo posts, and that it doesn’t have any outliers. Photos, however, tell a different story. The first quartile is very short, while the fourth quartile is much longer. Since most of the posts fall above the second quartile, you know that many of these posts are performing above average. The dots above the whisker indicate outliers — i.e., these posts do not fall within the normal distribution. You should take a closer look at outliers to see what you can learn based on what they have in common (seasonality/timing, imagery, topic, audience targeting, or word choices).

Heat maps

Figure 3: Average total engagement per day by post type (Facebook Insights)

 

What they are: Heat maps are a great way to determine factors like which posts have the highest number of engagement or impressions, on average, on a given day. Heat maps take two categories of data and compare a single quantitative variable (average total reach, average total engagement, etc.).

 

Why they’re useful: The difference in the shade in colors shows how values in each column are different from each other. If the shades are all light, there is not a large difference in the values from category to category, versus if there are light colors and darker colors in a column, the values are very different from each other (more interesting!).

 

You could run a similar analysis to see what times  of day your posts get the highest engagement or reach, and find the answer to the classic question, “When should I post for the highest results?” You can also track competitors this way, to see how their content performs throughout the day or on particular days of the week. You can time your own posts around when you think shared audiences may be paying less attention to competitors, or make a splash during times with the best performance.

 

In the above example, you can see that three post types from a brand’s Facebook page have been categorized by their average total engagement on a given day of the week. Based on the chart, photos do not differentiate much from day to day. Looking closer at the data from the previous box plot, we know that photo posts are the most common post, and make up a large amount of the data set; we can conclude that the user must be used to seeing those posts so they perform about the same day to day. We also see that video posts either perform far above or far below average, and that it appears the best day to post videos for this brand is typically on Thursdays.

Tree maps

Figure 4: Average total engagement by content pillar and post type (Facebook Insights)

 

What they are: Tree maps use qualitative information, usually represented as a diagram that grows from a single trunk and ends with many leaves. Tree maps typically have three main components that help you tell what’s going on — the size of each rectangle, the relative color and what the hierarchy is.

 

Why they’re useful: Tree maps are a fantastic way to get a high-level look at your social data and figure out where you want to dig in for further analysis. In this example, we’re able to compare the average total engagement between different post types, broken out by content pillar.

For our brand’s Facebook page, we have trellised the data by post type (figure 4); in other words, we created a visualization that comprises three smaller visualizations, so we can see how the post type impacts the average total engagement for each content pillar. It answers the question, “Do my videos in category X perform differently than my photos in the same category?” You can also see that the rectangles vary in size from content pillar to content pillar; they  are sized by the number of posts in each subset. Finally, they are colored by the average total engagement for that content pillar’s subset of the post type. The darker the color, the higher the engagement.

 

We immediately learn that posts in the status trellis aren’t performing anywhere near the other post types (it only has one post), and that photos have the greatest number of content pillars or the greatest variety in topic. You can see from the visualization that you want to spend more of your energy digging into why posts in the Timely, Education and Event categories perform well in both photos and videos. .  

 

TL;DR: Better Presentations are made with Better Charts

In your next analysis, you shouldn’t disregard the tried and true bar charts, pie graphs and line charts. However, these four different visualizations may offer a more succinct way to summarize your data and help you explain the performance of your campaigns. They’ll also make your reports and wrapups look distinctive when they’re used correctly. Although there are other chart types that are also useful for making better analyses and presentations, the ones discussed here are fairly simple to put together and nearly all of them can be put together in Microsoft Excel or visualization/analysis software such as TIBCO's Spotfire. 

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Comment by Mirio De Rosa on May 30, 2016 at 11:24pm

Hi Chris, glad you found the analysis interesting. Indeed, it applies very well to the data you mention. The software package I used is at http://www.mm4xl.com/mm4xl_tools.php#Forecast and Prediction. The tool name is Brand Mapping. I am always keen to help peers, colleagues and the like. If is there any analyses you need help with, let me know. Perhaps I can help. Also feel free to connect with me on LinkedIn. All the best and keep writing, Mirio

PS: This is another good article https://www.linkedin.com/pulse/how-analyze-big-data-excel-mirio-de-... 

Comment by Chris Atwood on May 30, 2016 at 7:29am

@Mirio that's a great technique! It would work fantastic combined with some of the data coding techniques many social media managers are using to analyze their content. I can see a number of people using that kind of framework to take qualitative coding types for content/topic pillars for different types of engagements. Great addition! 

Comment by Mirio De Rosa on May 23, 2016 at 7:16am

Interesting article, yet you are missing a key part of unstructured data visualization, namely: Multivariate Mapping. Visit the link below for an example (although the example doesn't use social data, the technique it presents applies to any kind data): How to go from data to information to insights and unleash the powe...

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