How effective Data Storytelling can allow you to design meaningful changes
The psychology of why facts often don’t work, while stories do
“We hear statistics, but we feel stories”
When I read those words, in Effective Data Storytelling by Brent Dykes, I found the solution to my design recommendations getting rejected.
At the time, I, like many Designers, felt like I couldn’t make a real difference. I’d get tons of great user feedback and quotes about designs I’d created, highlighting several critical issues to fix.
But my team wasn’t so eager to listen. I could change a button’s color or text, but any large design changes seemed off-limits, even if they were necessary.
I‘d heard quotes like “I’d rather learn a completely new application than deal with these patchwork fixes”, but I simply couldn’t convince my team to do that.
At least, until I learned about Data Storytelling. Learning this skill, along with the psychology of storytelling, helped me understand how to persuade my team.
But to do that, we first need to talk about why I kept failing with facts.
We don’t (always) react to facts logically
Have you ever wondered why people, when presented with facts, still tend to stubbornly hold on to their beliefs?
This is called the backfire effect, and it’s a well-documented effect across everything from politics to culture.
To explain it simply, many people build up a worldview around certain core beliefs. Whether it’s a sports team, a political identity, or (in our case), some core beliefs around users, people have a vested stake in certain subjects and opinions.
When you encounter facts that run contrary to that worldview, your brain can sometimes treat it like a threat.
While it’s unlikely that we’re presenting world-shattering beliefs with our presentations, there may be some bitter truths that your team may not want to believe.
The “Good product that needs a bit of polish” may turn out to be a product that users dislike with a ton of issues.
Or, the feature that Marketing has been calling a game-changer actually isn’t that useful to users.
Or, perhaps users are dreading the direction/strategy that the product is moving towards.
This is not even mentioning that some facts can be tradeoffs. It might be a fact that your users desperately need a feature, but it can also be a fact that it would take Engineering 3 sprints (i.e. ~$60,000) to build that feature.
These are often bitter pills that your team may not want to swallow, and as a result, they may just reject your suggestion outright. The reason may be tied to another point: if your audience has a stake in it, they might have created a narrative (or even mental model) around it.
Replacing it with a fact that doesn’t fit just doesn’t work, because your audience can’t reconcile the pieces.
This is why I kept failing to convince my team. Instead, as it turned out, I needed to take a different approach: I needed to tell a story.
How Data storytelling creates shared empathy for users
Have you ever noticed that charities don’t overwhelm you with statistics, but instead choose to tell you a story about one person in particular?
There’s a reason for this: when a story is shared, the brains of both storyteller and listener synchronize. This is an effect called Neural Coupling, and it’s a powerful tool in engaging and persuading people.
This is because storytelling doesn’t just tap into the areas of the brain for processing language: stories tap into all of our senses.
When we hear how little Malika avoids the market, because the smells of cooking make her hungry for food she doesn’t have, the olfactory cortex, responsible for smell, lights up.
In other words, this is a way for the listener to empathize with who the storyteller is talking about.
So if you tell stories about a user who gnashes his teeth in frustration when forced to use our product, your audience doesn’t just hear about the bad experience: they can often feel their frustration.
This is the power of Data Storytelling, and why it’s a critical skill for Designers to learn and practice. However, getting started with this process may seem daunting: after all, you may feel like you’re not great at telling stories.
However, there are three questions you can ask yourself to get started with this process.
Data: Are you informing your audience, or communicating insights?
This tends to be one of the most common mistakes Designers make when presenting your user findings. Sometimes, you haven’t refined your research enough to turn information into insights, which leads to a number of issues.
To explain this, let’s provide two sample test findings to see the difference:
3/5 users had trouble with Task 2, creating an account
3/5 users ran into difficulties finding their Login/Password information after creating an account
In the first, we’re informing them of facts: you observed X, so that’s what you wrote down. That puts a lot of the burden on your audience: you’re assuming that they know exactly what to do with that information, which they don’t.
Are they supposed to take this to mean that 60% of users will run into this issue? That’s not how statistics work, but there’s no other conclusion they can draw easily from this information.
This is why the second point is slightly more insightful. This provides actionable next steps (such as re-evaluating how to present this information), and likely if they dig deeper into this, they can see exactly what difficulties they ran into.
This also brings up one key difference that we need to keep in mind, between Informing and Communicating.
If we’re informing someone, we’re passively giving them data that we expect the audience to interpret and comprehend themselves. In other words, we’re giving them a list of facts.
Storytelling is about communicating insights, which means that we need to ensure that the information you provide sparks change, action, or a new understanding.
This means understanding the primary conflict your audience has.
Narrative: What is the primary conflict/tension your audience has?
I’ve talked about this before, but it’s important to consider catering your story to your audience, by thinking about the tension you’re audience is trying to resolve.
Part of this process is to spend time thinking about the narrative arc of your presentation, and whether you’re talking about the points that your audience cares about.
If your audience cares a lot about certain metrics, like engagement, being able to tie your data from the findings to these metrics may create an effective narrative.
For example, let’s take our previous data point:
3/5 users ran into difficulties finding their Login/Password information after creating an account
Perhaps we might see that this is a common trend across multiple pages. We might have other similar data points, such as:
2/5 users navigated away from X page to search for something relevant on Google
4/5 users stopped what they were doing and navigated away to our help documentation
etc.
We might combine that into a larger theme and narrative, which might address our audience’s main tension:
Across our product, users aren’t provided with the right information to complete tasks without opening a new tab/navigating away from the site. This is likely affecting our engagement metrics.
Doing this helps to craft a story from these data points, and ties it to the main tension that the audience has. However, there’s one last element to a Data Story: the visuals.
Visuals: Are you effectively visualizing insights for interpretation?
Visuals, usually in the form of Data Visualizations, are the last critical aspect of Data Storytelling, and it’s important for one critical reason: it helps us avoid the backfire effect.
When facts are visualized, it’s harder for us to reject them. Simple things like well-designed bar charts can often reduce misperceptions or information deficits in comparison to stating facts.
Visualizing data won’t correct everyone’s false beliefs, but it can help in making the ‘bitter pill’ easier to swallow, especially if it comes from the audience understanding things on their own rather than having someone tell them something.
Data Storytelling is a critical skill to help designers with uncertainty
As I spoke about last week, now is an uncertain time for Designers.
As Fabricio Teixeira and Caio Braga pointed out in the State of UX in 2024, we’re in a period of Late-stage UX, which is characterized by concepts like market saturization, financial growth, and automation.
https://trends.uxdesign.cc/
UX is no longer a ‘hot new thing’, and there are a lot of additional factors that may make it harder to truly create meaningful change with our designs.
This is why I believe Data Storytelling will become a critical skill to not only distinguish yourself to create more change, but speak to the evolving needs of the business and the market.
Being not only a little bit more familiar working with data, but also being able to understand and craft narratives around it, can help you have a great impact as a designer and stand out as a stronger job candidate.
So if you have any interest in learning to tell stories around user insights, the new year may be a great time to start learning Data Storytelling.
I’m creating a Maven course on Data Storytelling for Designers! If you’re interested in learning (or would like to provide feedback), consider filling out this survey.
Kai Wong is a Senior Product Designer and Data and Design newsletter writer. His free book, The Resilient UX Professional, provides real-world advice to get your first UX job and advance your UX career.