Taking a cue from the rise of data journalism, brands that rely on content marketing are starting to use data to tell stories about their brands, customers and products. What makes this kind of “data storytelling” distinct from any other kind of narrative is, obviously, data: it centers on the numbers. And it’s effective; whether you’re talking about holiday shopping or employee health spending, quantitative information is uniquely able to capture attention, convey a story visually and bolster your credibility.
I am just the kind of person who might be scared off by this turn. I still have nightmares about my high school math teacher—and my grad school stats class took a backseat to my grad school social life. But I’m not alone. Data storytelling has yet to take its place at the heart of the content marketing toolkit, and I suspect that’s because so many of the folks who work in marketing and communications are like me and think of themselves as story people, not numbers people.
But to reach audiences with data-driven content, what really matters is your ability to craft a good story, and there’s very little math required. In fact, discomfort with numbers may actually lead you to ask the right questions to create the most interesting and accessible data-driven content.
As with any story, with data storytelling you’re trying to convey a larger message—and as with any good story, you’re conveying that message in a way that offers a memorable insight or surprise: Facebook shares are highest on Saturdays? 70% of Americans re-arrange the dishes in the dishwasher that somebody else has loaded? The ability to recognize an insight or finding that is really engaging and actionable—versus one that is boring or irrelevant—matters a lot more than your ability to determine levels of statistical significance (though you do need to know a few rules of thumb). And that ability is inextricable from the core competencies of a good communicator: knowing how to identify and target an audience, how to craft a key message, and how to ensure quality in execution.
First, know your audience: most of the time, even when you’re telling a story with data, you’re telling it to an audience that includes many people who have nightmares about their high school math teachers, too. So tell your story in words and numbers: make your key point in text, follow it with a chart that backs it up. That chart itself should contain plenty of clues about how to read the data: not just a legend and axis labels, but highlights that spell out the significance of one or two key data points. Show your chart to someone who knows very little about your subject and about data visualization; if they’re confused you’ll know where to keep working.
At the same time, some of your audience will also be very adept with numbers. There is almost always someonewho will ask the nitpicky questions about your sample size or data analysis methods; if you’re producing a piece of long-form content, anticipate those questions with a methodology section, and if it’s a piece of short-form content, have an answer ready when you get the inevitable question.
As with any story, your audience should also inform how you shape your key message. Even if you have a very specific idea you want to relay (“our product lasts twice as long as the product made by our competitors”), your knowledge of your customer, rather than your dataset, should drive how you tell that story with data. If your product’s durability matters to your customers because it saves them money, you’ll want charts that compare the total cost of ownership of your product versus your competitors’ product; if it matters because they don’t want the inconvenience of down time or service calls, give them a chart that compares the total number of work days lost over the life of each product. You don’t need a lot of math skills to make that distinction.
But data is often most helpful when you don’t have a specific message to deliver, just a need to boost brand awareness and communicate your expertise. In that case, you just need a data story that will surprise and intrigue your audience. The message that comes out of that story should be clear and relevant to your audience—but you may be quite agnostic about the message itself. If you’re just trying to capture the attention of HR professionals, a message like “new data shows on-site daycare boosts employee productivity” is just as useful as “new data shows acupuncture and massage are the most popular extended health benefits.” Use your communications savvy to figure out the potential headlines that would make your audience take notice—then look for data that could tell that story, discarding headline ideas if the data turns out to be boring or unavailable.
It’s when we get to execution that we might really feel that resurgence of adolescent math anxiety. It can be tempting to just hand the data analysis off to a data geek, and let them come back to you with a couple of charts’ worth of interesting findings. If you have that resource available, you could take that approach—and you certainly should ask your data team to give you a set of tidy spreadsheets, so you’re not trying to pull data from a complicated analytics interface. (Though if you’re going to be driving a lot of content off one data source, it’s great to learn how to use it well enough to produce the kind of data tables you need.)
As far as actual analysis goes, however, you’re better off digging into the data yourself, because it’s your knowledge of audience, messaging and story that will let you identify an interesting finding. Start by jotting down some hypotheses: if you’re looking at data on how particular product lines sell in different regions, for example, write down your hunch about black clothes selling better in the Northeast, and lighter hues prevailing in the South. Then print out your spreadsheets (at least for the first few times you do this kind of work) so you can physically highlight anything that looks interesting. When I’m dealing with a large volume of data, I put my printouts in a binder and flag key pages with post-its. Go through your data in order, paying particular attention to anything that speaks to your hypotheses—but be open to spotting anything that’s surprising or interesting. If it’s interesting to you, it’s a lot more likely to interest your audience.
Much of the time—for example, when producing a blog post or report—you’ll combine text and charts to tell your story. Make sure they fit together! If your corporate voice is playful and creative, get an infographic designer to create charts that are as visually interesting as your text; if you only have the resources to create basic charts, make sure the voice of your text matches the visual tone of your graphics.
If you’re a professional communicator, you can make powerful use of data storytelling in your content marketing efforts—even if you don’t think of yourself as a numbers person. Yes, it’s great to have those skills available, and if you’re on a team that includes people with analytics training, it’s very helpful to collaborate with them. But don’t let the spreadsheets distract you from the main story—the story that you, as a communications professional, are uniquely able to tell.