If you’re an executive in the sports media space, data extrapolation and analysis is going to be a big part of your job. Whether your attention is steeped in page views or social metrics, you will need to measure the performance of your content by the data. And that data analysis could precede consequential decisions about your strategy.
Having said all this, the sheer amount of data in the modern sports media space can be overwhelming. How can you collect, organize, and interpret the data in a way that’s easy, efficient, and unexposed to cognitive biases? Here’s a helpful guide.
5 Tips for Properly Evaluating Content Metrics
1) Pinpoint Data Collections Relevant to Your Goals
First, you need to identify which data sets will be relevant to your review. There are a lot of potential numbers to track out there, but different ones apply for every company, based on their long-term goals and objectives.
If your media outlet is primarily concerned with written content, then metrics like page-views, bounce rate, and time per session might be the most important to track. If you produce most of your content on video and social media, views, engagement rates, follows, and subscriptions may carry more weight.
Wherever you want to conduct self-evaluation, you need to confine the data set that is relevant to that area. Once you pinpoint the relevant data collection, then you can zero in on the numbers and start your evaluation.
2) Use Tables and Spreadsheets to Sort and Organize
Especially if you use data intermediaries like Chartbeat and Google Analytics – which are solely tasked with amassing data for interpretation – then you could be overwhelmed by the numbers that accumulate, even over a short time span.
To combat the massive volume of data that comes with data tracking as a media outlet, use tables and spreadsheets to enter, sort, and organize your relevant data. Even then, it’ll be a lot of numbers – but in a spreadsheet, you hold dominion over it all.
With spreadsheets, you can sort the data set to find outliers. You can organize the data set based on multiple relevant categories to your analysis, and you can isolate certain data points to assess articles, videos, and posts on a case-by-case basis. This organization helps expedite the interpretation process.
3) Clean the Data Wherever Applicable
When you transfer the data from an intermediary to a spreadsheet for independent analysis and interpretation, you may come across duplicates in the data, as well as clashing measurements. Once you transfer the data, you can go through the process of cleaning and streamlining the data, so no duplicates muddy your evaluation.
This task involves a microscopic eye and a thorough examination of the data set. While painstaking, it all guides toward having a complete, comprehensive, but also the most trimmed-down, manageable view of the data set, so you can again expedite interpretation.
4) Use Charts and Graphs to Visualize Correlations and Trends
Once your data set is compiled, cleaned, and organized, you can now begin the process of visualizing correlations and trends to guide the executive decisions that derive from data analysis. For this task, charts and graphs can be invaluable tools.
With interfaces such as bar and pie charts, you can measure volume stats and get a bird’s eye view of certain metrics – for example, making a pie chart assessing how many of your interactions and overall impressions come from different social channels.
Graphs tend to be a more versatile tool beyond measuring volume data. You can use scatter plots to explore potential correlations between data points, and you can use line graphs to analyze trends over time. All can be applied to improve your informed decision making.
5) Interpret With an Open Mind
Visualizing the data through charts and graphs is the last step of organizing one’s data set, but as an executive, you still need to make decisions based on that data – crucial decisions that could determine the short-term and long-term outlook for your company. The biggest key to remember in this phase is this: Interpret the data with an open mind and no pre-existing biases.
The process of cleaning data helps hedge for incongruences within the data set itself, but the human mind is naturally susceptible to skew through bias. When you view the organized data, enact a “clean slate” protocol in your mind. Take an accounting of your pre-existing understandings that might invite confirmation bias, and accept that the data can guide you to the right conclusions.
When you interpret your data set with an open mind, you remove the perception barriers ingrained in human analysis, and you open the door to capitalize in the best way.
