Data storytelling from the NFL: How marketers can make data sell

Data storytelling is an academic subject as much as it is a practical one. For example, a common question is whether one should use an infographic or another type of data visualisation. 

For Dykes, a customer-analytics evangelist at Adobe, it is important not to “confuse the tool with the task” and to keep in mind the goal of data storytelling: to make data easy to “understand, remember and act upon”.

“The major pitfall is to design a data story without knowing your audience,” said Dykes. “The other is to overload your audience with data. The working memory is finite. Think about Miller’s Law, which shows that the brain can only consume up to seven items at once.”

Understand audience

Citing cognitive-overload theory, the problem is two-fold for marketers: Find the “right data to analyse” and then present it in the right way to tell a story that is meaningful to users and stakeholders.  

Drawing on classical narrative theory, Dykes takes inspiration from the “storytelling arc” as a means to frame data that can lead to a potential story.

Data within a narrative arc

Using “content staging” to reveal insights in a progressive fashion that “builds on understanding” also assists with minimising the impact of cognitive overload.

“Making data relatable is also crucial,” said Dykes. “The New York Times told the story of a long jumper’s world record. Rather than just tell the distance he jumped in feet they compared it visually to a basketball court, illustrating the jumper had leapt from the baseline to somewhere beyond the three-point line.”

Finding a character in the data

On the business end of data storytelling, Theresa Locklear, director at the NFL (National Football League), discussed the impact data storytelling has had on the league's business and introduced Rob Dearborn, analyst at the business intelligence and optimisation division of NFL, who dove into the organisation’s data challenges and best practises.

Dearborn highlighted that a central part of the NFL's data storytelling needed to be done for internal purposes. “With all our stakeholders wanting to know key pieces of data on our broadcasting and content we essentially had a Q&A desk,” said Dearborn, reflecting on how this was an unsustainable practice. “It was a like constant bean counting. And there was no time to answer 200 countries across the world.”

While “dashboarding” was a possible solution, it was too complicated and confusing even if it got people thinking in “real-time”. Instead Dearborn embarked on a plan to turn the NFL’s data into a story that could start conversations and get stakeholders to ask the “why questions rather than what”.

This internal change of approach toward making sense of data went on to affect the NFL’s commercial side as well. Drew Norton, manager of business intelligence and optimisation at NFL discussed the company’s video analytics.

“There were flaws and it was difficult to get actionable insights, and adding metrics only created more noise,” said Norton. “Whenever possible, try to boil it all down to one number.”

Boiling things down to one number

Longer video for example, had lower completion rates but by finding “the line of best fit”, the team could understand why certain videos were viewed the most, see how content performed on a given week, at different lengths, and all at “one scale”.

“This enabled us to tell a more actionable story to our content teams in order to optimise content production,” said Norton. “In other words, produce more of what is going to be successful and tweak things that clearly had potential but maybe needed more of a push or needed to be formatted differently. Having this information gives us an edge with advertising as well.”

Data storytelling best practises from Dykes:

• Ask six critical audience questions: key business goals and priorities, specific needs and questions, how familiar are they with the topic, how data-savvy, seniority level, and the delivery method (direct/indirect)
• Understand the five essential components of a data story: Main point, explanatory focus, linear sequence, narrative elements and visuals
• Structure your data with a story: data storytelling arc
• Prioritise what data to show in your story
• Insert characters into your data story: search for appropriate character imagery and create a persona for your hero
• Consider the cognitive load theory: manage the intrinsic complexity of the data, minimise the extraneous and maximise the germane to make the data relatable and tangible to your audience
• Choose the right visualisation
• Go from being a data explorer to a data storyteller