Do you remember that financial report from last week?
Probably not, right? Reports can be overwhelming. Even scary. Most of us avoid any deep dive-in because we don’t have the time. Or perhaps, we don’t understand what do we actually need to conclude from it.
Maybe you were the one who created the report last week? You added all the numbers, charts, and shared it with the team or the client, thinking naively how everyone will understand your data without any further explanation. You might be great with numbers, but how good are you at telling stories?
Stories connect people, build trust, and create meaningful connections. From the earliest days in human history to today’s wide usage in marketing, storytelling is the best-proven method for successful communication.
What makes a story is a “cause and effect” relationship. If you don’t have the “what, why and how”, the narrative will just be a series of events and there is nothing for the listener or the reader to relate to. When you tell a story, you try to visualise it for your listeners/readers. You try to build a picture in someone’s mind and to find the connection between your and the listener’s story.
Now, the same principle applies to data and analytics. The job of a marketer is to tell the stories that build successful campaigns and customer journeys. However, interpreting all the data correctly and turning it into a good story can be an intimidating task that many organizations struggle to accomplish. Many companies are clueless about what to do with all the data and how to proceed with the given information.
Imagine again that report from last week. Would you remember and understand the data better if it was presented by a stand-up comedian who presented the information as a relatable story?
Everyone who has data to analyse has stories to tell, but data alone is just a collection of numbers until you make a story out of it. Showing reports and dashboards can be useless without adding a context. Any great insight explains what happened, why it is important and how you can use it to turn it into something actionable.
When we see great data storytelling, we’re seeing a great data visualisation. It’s interesting. Inspiring. Easy to remember. We’re seeing data that’s been analyzed well and presented in a way that someone who’s never even heard of data science can easily get it. Data visualisation is using data and statistics in creative ways to show patterns and draw conclusions about a hypothesis, or prove theories, that can help drive decisions in the organization. Don’t leave your data trapped in the excel sheets and PowerPoint presentations, listen to the numbers, make notes and reveal the message – that’s their purpose.
Presenting data creatively means turning statistics into stories. By ‘humanizing’ data we can make those numbers – thus the people and companies behind them – more transparent. To be clear, data storytelling is not data visualisation, analytics reporting, or a bunch of colourful pie charts floating in a neat presentation. Data storytelling unites the best of both worlds together: the “boring”, but accurate data and “exciting”, but emotional human communication.
When you start creating a story that will support your next report, try to keep it simple for your busy colleagues. Think of the ways how you can engage your team by explaining your strategies and results as a story. The essential story elements will make your data relevant, relatable and tangible for your audience. As you start to build your storyline ask these questions:
Data storytelling may be intimidating for beginners, especially for analysts that usually let the numbers do all the talking. Ideally, data storyteller has a background in marketing, with a mathematical degree and graphic design affinity. Ideally. But in the real world, you just need to have interest, time and a determination to learn.
Statistics can be overwhelming to analyze if you do not sort out the unnecessary information. Once you decide what is most important to highlight, then you can transform it into a visualisation.
The perfect example of good data visualisation that focuses on the most important data is The New York Citi Bike project. Using live data provided, it’s possible to see how many bikes are checked into each station at any particular moment. By presenting it in a visual and interactive format, The New Yorker made the data widely accessible, relevant and timely.
Great data storytelling needs an interesting story. While data can certainly give a boring subject an interesting spin, make sure it’s something that is relevant or interesting to the people you’re trying to reach. Keep in mind that data storytelling is not a story about numbers; it’s about how those numbers interact and affect people.
A video Inequality in America is a good example of how you create a relatable narrative that leaves the viewer thinking about the information hours after.
Good data alone does not make a good data story. Data storytelling is only effective when it provides value, whether it teaches people something new, gives them a fresh perspective, or engages them to take action.
The way you deliver that story determines whether that message is communicated. Your narrative should guide readers through, provide context, and help them synthesize the data story as effectively as possible.
One of the brilliant examples is the 100,000 Stars site. It’s not easy to grasp the actual size of our solar system, galaxy, or universe. However, this project makes it possible, using data and pictures together to translate the raw scientific data into a more interactive and enjoyable educational scheme.
Less is more. Fewer numbers can give greater insight if they are the right numbers. Less precision can lead to more confidence. The percentage values can almost appear to be too precise, while “two-thirds of…” shows the focus on what the majority did or didn’t do.
In 2015, Eric Roston and Blacki Migliozzi launched a project called “What’s really warming the world?” to showcase how much different factors (natural and industrial) actually contribute to global warming, based on findings from NASA’s Goddard Institute for Space Studies. The highlight here is not on the numbers as much on the comparison and timeline.
One of the best ways to sabotage your data storytelling is with poorly designed data visualisations. Data visualisation is meant to make the data understandable, which is why it’s important to work with a designer who understands best practices. Colour, labelling, copy, hierarchy, comparison – there are many things that can make or break your data visualisation.
A fun and engaging example is “Daily routines of famous people” where colours do all the explaining while keeping the data comprehensible.
As businesses keep growing and the markets get saturated, the ability to differentiate will be crucial to stay ahead of the fierce competition. Data storytelling, and storytelling in general, help your brand be recognizable and trustworthy. What makes boobook different than other market research and data analytics agencies, is our ability to tell great stories. Our team of experts starts with the “what, who, how, why, and so what” principle. We declutter your numbers and deliver personalized advice based on the needs of your business and wants of your customers.
In the recent example of Pernod Ricard, we analyzed the data from over 18,000 people. Together with the Pernod Ricard team, we created a series of typologies based on consumer attitudes and behaviours that transcended nationalities and demographics. The results are now fully embedded in the business and the language they use about the different types of shopper profiles, the different typologies that they target, and the different campaigns that they put in place.
Know your strengths and work on your weaknesses. Stay up to date on best practices in marketing, and data analytics to improve your data storytelling.
If you need help with your getting insights and creating a story around your data, feel free to get in touch!