How to Find Stories in Your Brand Data


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“Brand storytelling” is on track to become the hottest buzzword in 2015. This popular new word is even making it’s way into employee titles. What’s a Chief Storyteller, and where can I apply? Long story short, the phrase “brand storytelling” is being overused more than an online coupon code at Sears.com. But there is value and meaning in brands that can clearly provide interesting insights into the world around us. So how can a brand find interesting data and effectively communicate compelling stories that provide value to their users? Below, we break down the process in a very practical way.

Aggregate

aggregate

You can’t visualize data you don’t have. It may seem like an obvious statement, but that’s surprisingly where a lot of brands get hung up. When creating data-driven brand stories, it’s crucial to fully understand what data you have at your disposal internally along with what credible public data you can leverage. For some, this may be millions, billions, or trillions of data points, but for many brands this means doing some investigative work to understand what your brand is tracking/measuring. Are you tracking website visits? Referrals to specific pieces of content? Maybe even conversions on a content level? These are all great metrics that, when analyzed, could potentially result in some interesting findings.

Brand Data Dump

data-dump

Once you know what data you can access, your next step is to organize that data. Personally, I usually just gather all the data and try to organize it loosely in an Excel doc. I use Excel because it’s a bit easier to work with to filter data points and insert necessary functions. If you’re not an Excel guru, don’t worry: Some quick shortcuts will make your life a whole lot easier. Your doc may end up being multiple sheets or hundreds of rows long, but organizing the data is perhaps the most important step when trying to uncover interesting stories.

Analyze

analyze

Once you have a master Excel Doc with all your data, it’s time to analyze. This is the stage where you’re going to find interesting data correlations or patterns that lend themselves to worthwhile brand stories. When trying to dig up stories, you can look out for the following for insights:

Correlations
Outliers
Trends
Averages

From here, you’ll probably start seeing the makings of some interesting stories. BE CAUTIOUS! There are best practices for data analysis, and you want to make sure you avoid any accidental pitfalls. For example, correlation does NOT equal causation. My favorite example is the chart below, which shows that both global temperatures and piracy have increased over the last 200 years. Data journalists know that, although these two metrics correlate, it does NOT mean killing pirates could reduce global warming.

For more best practices in data visualization, read up on fundamentals of core chart types including area, pie, bar and line (and grab our free ebook on Data Visualization 101).

Aside from correlations, outliers and trends are two other great data analysis aspects to watch for. Outliers are data points that exist outside the normal range of other data points. These data points can often be interesting anecdotes you can use in the narrative stage, but they’re just that. For a broader understanding, seeking trends, patterns, and averages are all great exercises for conducting any form of data analysis. These approaches will help you understand where a majority of the sample size lands and prove (or disprove) a hypothesis you may have had when you started.

Narrate

Narrate

So, some great findings have come out of your data analysis phase. Now what? Those are foundational pieces to brand storytelling, now all you need is the glue. Remember, you’ve had your hands on this data for a couple days, maybe even weeks at this point, but your audience is not going to have the same familiarity with the data as you. For that reason, it’s crucial to develop some sort of narrative to provide context to what you’re showing them. If I just showed you a pie chart with 65% blue and 35% red, you’d have no idea what blue or red represent—the data would be useless to you. By adding a legend, clear title, and short description of the data, however, you’d immediately be able to comprehend and digest that information quickly. Think of this step as the icing on the cake. No one wants cake without icing. NO ONE.

If you have any questions or thoughts, we’d love to hear them in the comment section below. Also check out our easy-to-use tool to help bring your brand stories to life.

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