When it comes to telling stories with data, most people make the same mistake:
They think the data speaks for itself.
Unfortunately, data isn’t a story.
This creates a problem, because if you think your data is going to convince people to take a certain action all by itself, you’re wrong. Before your audience can make data-informed decisions, they have to understand what your data means.
Here’s an example.
I recently wrote about the surprising success of Riverdale‘s season two ratings. Its season premiere attracted 2.34 million viewers, a number that turned heads around the industry. But why?
Are 2.34 million viewers more than other shows see in this time slot? Is it more than other shows on the CW? Is it more than other teen dramas? The number alone doesn’t explain itself. Without additional data, you have no way of knowing why that particular number was so newsworthy.
Likewise, if I tell you “40% of our customers buy Product X,” does that mean Product X is popular? Or are you wondering why 60% of our customers don’t buy Product X?
Without context, data can mean anything, which means numbers by themselves don’t mean anything at all.
Data Isn’t Your End Result; It’s Just the Beginning
Unfortunately, copywriters make this mistake all the time when telling stories with data: they present numbers without context and hope that their audience will all come to the same conclusion.
(Spoiler alert: they won’t.)
I see this happen in sales pitches, case studies, email marketing, nonprofit websites, donor requests, annual reports, Power Point presentations, webinars… you name it. And then there’s the news, where an endless supply of context-free data has spurred the need for 24-7 fact-checking.
Data-informed decision-making is a critical function of business, marketing, and politics. But burying good data in bad stories (or wasting it with no story at all) doesn’t help anyone.
If you’re reading this, I presume you want your data to be used constructively. You want to be trusted. You want to be heard, and believed.
To help your audience make good data-informed decisions, you need to incorporate your data into a larger story. When you get better at telling stories with data, your audience will have the critical context they need to form opinions and take action.
Here are 6 methods you can use to build compelling stories around your data.
Writing Prompts for Telling Stories with Data
How does your data compare to numbers from similar historic or cultural benchmarks?
Quick: what’s the all-time biggest R-rated horror movie?
In a rush, you might guess one of the Halloween, Friday the 13th, or Nightmare on Elm Street films… but you’d be wrong.
Scream? No. Saw? Nope. The Conjuring? Close… but not quite.
No, the biggest R-rated horror film of all-time at the U.S. box office is Stephen King’s It. The film debuted in September of this year with a record-breaking $123M opening. Since then, It earned over $320 million USD in just its first 50 days, surpassing the previous record-holder by nearly $100M.
That’s impressive, right? Well, sure. It’s the most money an R-rated film has ever made, and the media have justifiably been celebrating It‘s stunning success. After all, the film’s performance offers some sorely-needed good news after the worst summer box office in 11 years.
And yet, there’s a catch.
See, this record, like all modern box office records, comes with an asterisk:
In fact, if you adjust for inflation — or if you look at the estimated number of tickets sold, which is really an estimated audience head count — you’ll see that The Exorcist (1973) is the all-time R-rated box office champ, by far.
So, when it comes to reporting something like It‘s meteoric rise to the top of the charts, you can use this data in a few ways:
- Report it straight up
- Compare it to recent industry trends
- Compare it to past years
- Compare it to films from the same genre
- Compare it to films with the same rating
Or, if you want to dial down the buzz, you can use modifiers and qualifiers like inflation and head count to put It‘s breakout success into historical perspective.
The takeaway? Numbers don’t exist in a vaccum; data is only interesting in comparison to other data.
How does your data compare to numbers from similar fields or industries?
Is It‘s sudden dominance of the U.S. box office unusual, or does the same thing happen in other industries?
To find out, let’s look at the numbers from a similar field: video game sales.
According to Forbes, 2017’s best-selling video game (so far) is Destiny 2… which was only released in September. And the second-best seller for the year is NBA 2K18… which was also released in September. (Man, it sounds like September is suddenly a hot month to release chart-topping media. Why? I don’t know. Maybe sSomeone should… you know… crunch that data. )
If you were writing about any of this data, you could easily draw comparisons across industries. For example, you could compare year-to-year numbers, which might lead you to wonder why video game sales are up 7% year-over-year while the U.S. box office is down 4.6% in the same time frame. Or you could look at international trends, where you’d see that video games outsell films and music in the U.K.
The takeaway? Data in one context can reveal trends, but telling stories with data across multiple contexts can reveal cultural shifts.
Does your data suggest a rising or falling trend?
Speaking of music, let’s talk about the surprising world of digital music sales.
Given how ubiquitous digital downloads and streaming services are, you’d think they’d have surpassed the sale of CDs long ago, right?
Wrong. It might shock you to realize that digital music sales didn’t top physical music sales until 2015.
But there’s another wrinkle here: the battle between digital sales and streaming subscriptions. In 2016, U.S. streaming revenue surpassed digital downloads for the first time, thanks to a massive boom from Drake and Rihanna. That trend is expected to continue in 2017, with streaming projected to become the only distribution growth channel in music worldwide by 2021.
Now, if you were telling stories about data from the music industry, you have a wealth of numbers to pull from… but you also have competing narratives.
Do you highlight the seemingly unstoppable rise of streaming? Do you drill into the factors that drive digital downloads? Or do you center on the statistically surprising quirk that physical album sales were up in 2016, including the most vinyl album sales since 1991?
The takeaway? A lone data point can’t reveal a trend, but data across time equals headlines.
Does your data confirm or contradict predictions?
For a perfect example of data that’s meaningless without context, look no further than the sports page.
Let’s take the saga of Los Angeles Lakers rookie point guard Lonzo Ball.
Though his career has barely begun, Ball is already one of the most talked-about players in the NBA. This is mostly because his father LaVar Ball is a big-talking empire-builder who insists that his sons will be Hall-of-Famers, and whose headline-dominating style makes you think “ohhhh, so this is what would happen if Donald Trump and Serena Williams’s dad Richard ever had a love child.”
So when Lonzo made his NBA debut, all eyes were on his statistics… and they weren’t pretty.
In the Lakers’ 2017-2018 season opener, Lonzo scored only three points and earned just four assists during a 16-point loss to the Clippers. Obviously, these are not the numbers one expects from an alleged rookie phenomenon. Sportwriters were quick to pounce and declare Lonzo Ball a bust.
But then two things happened.
First, Ball bounced back. In his second game, he scored 29 points, 11 rebounds, and nine assists against the Suns. In his third game, against the Pelicans, he made 13 assists.
And then, Kevin O’Connor at The Ringer went back and watched the tape of Ball’s debut game against all-NBA defender Patrick Beverley. His findings? It turns out Ball was more effective than his stats alone would indicate. His low tally of assists wasn’t the result of his own bad choices, but mostly bad execution by his teammates who kept missing.
Now, sports narratives can change on a minute-by-minute, play-by-play basis. Today’s hero is tomorrow’s goat, and headlines are sold on streaks and surprises. But none of it would be newsworthy if fans didn’t have a pre-existing expectation of what those numbers should look like.
The takeway? Telling stories with data is easier when you know your audience’s expectations, because when your data conforms to or diverges from predictions, your story has a hook.
Is your data particular to a certain group, or is it generalized?
Is online course enrollment up or down in the U.S.?
What if I told you the answer was “both,” and that the correct answer depends on which students you’re talking about?
See, each year, Babson College reports on the state of online education in America. But the stories their report reveals only make sense if you compare each year’s numbers to past years, and if you look for correlations among the data.
According to the 2017 Distance Education Enrollment Report, total online enrollment is up 3.9% as of 2015. But if you look deeper, you’ll see two very different stories. It turns out that online enrollment is way up at private non-profit institutions (11%), but it’s down at for-profit schools (-9.4%).
The takeaway? Generalities can be true and misleading at the same time, so make sure your data tells the whole story of how your numbers add up.
Does your data reveal a behavior that should be encouraged or curbed?
One of the most-read articles on The Atlantic this year is about the negative mental effects of smartphones.
In this case, researchers found that smartphone use correlates with alarmingly high levels of antisocial behavior. While this may be true, the article doesn’t cover the flip side of this equation: are there also positive effects of increased smartphone use? If so, shouldn’t they get equal consideration?
But that’s not the point of the article. Instead, it connects the worrisome data about digital behavior to other downward trends in economic, social, and sexual activity to imply that “the smartphone generation” is heading for disaster.
Now, could you flip these perspectives and find a completely positive way to spin these numbers? Possibly. And I’m sure smartphone manufacturers would see some of this user data as a good sign. But the author and editors of this article leaned toward the downside of these trends, and the result is a story that’s been among The Atlantic‘s top articles for months.
The takeaway? Data is neutral, but how you interpret it isn’t.
If You Like This Post
… then you may also like these posts about 10 tips to improve your writing for online audiences or 4 ways to improve your sales pitch.
Image: “Research Data Management” by Janneke Staaks via Flickr Creative Commons License