Data Storytelling for Web Analysts: How to Educate & Persuade Using Data

In this comprehensive guide, I’m going to give you a step-by-step framework you can use to present data that educates and persuades. You’ll learn how to take data and turn it into storytelling gold.

If you’ve ever been asked to give a data presentation and thought to yourself “How do I start?”, “What do I present?”, and “Is it too late to call in sick?”, then this article is for you.

Here’s what you’re going to learn:

Why Stories are So Powerful for Educating and Persuading

Think about your most enduring memories. Were they bland moments when you were being presented with statistics? Probably not.

If you’re like most people, you remember moments that involved strong emotions.

And the highly influential role of emotions in memory, decision-making, and behaviour has recently become the subject of some compelling research.

What’s the connection with storytelling? Simply put, stories engage our emotions, and engaged emotions have a strong connection to memory and persuasion. 

Stories are Memorable

Did you know that stories are 22 times more likely to stick in a person’s mind than just data over a long period of time?

Stories are memorable because they engage the emotional parts of our brain.

In one study students were asked to give a class a 1-minute pitch for a product. The presenters used, on average, 2.5 statistics to describe the product. Only one presenter told a story. When the audience was surveyed after the presentations, only 5% remembered any of the statistics, while 63% remembered the story.

Stories Persuade

Recent neuroscience is pointing increasingly to the idea that ALL decisions are made using emotion, not logic.

In fact, it’s frequently heard in marketing that “we buy on emotion and justify with logic”.

And relatedly:

An Example of a “Big Idea” Statement – Signing Up for Employment Insurance

Say you’re a web analyst for a public sector organization that operates the federal government’s employment insurance program. So when people lose their job, they come to your website to apply for unemployment benefits.

One of the content goals for your employment insurance website might be this: 

“Visitors are able to sign up for employment insurance during their first visit.”

In web analytics lingo, every time a person successfully registers for employment insurance on your website, that sign-up is considered a “conversion”.

While analyzing the conversion data for the previous month, you segmented the data (of course!) by visitor device and discovered a huge difference between the conversion rate on desktop computers versus mobile devices.

Here’s what that conversion data might look like:

Employment Insurance Registrations on XYZ.gc.ca (June 2019)

Notice the huge difference in conversion rates between desktop and mobile: 6.98% vs 1.81%.

What’s the Big Idea here? If I was the web analyst presenting this data, this would be my Big Idea statement:

“There’s a huge difference between desktop and mobile conversion rates, which might indicate a problem with the usability of our employment insurance web pages in mobile browsers. We need to do some qualitative research on those pages to find out what’s going on. I propose we set up heatmaps on the pages to see where visitors are clicking, as well as launch a pop-up poll on the pages asking users what obstacles they’re facing.”

So when preparing your presentation, consider these two questions:

Nussbaumer Knaflic talks about a “continuum” of communication mechanisms, from live presentation to written document or email:

Live presentations give you the most amount of control, because you’ll be in front of the people who are receiving the presentation – so you can answer questions and clarify any points that are unclear to the audience.

Written documents and email give you less control, because the audience will be reviewing the presentation without you there to answer questions and ensure your points are understood correctly.

In her book, Nussbaumer Knaflic introduces the concept of the “3-minute story”. As its name suggests, the 3-minute story is this: if you only had three minutes to tell your audience what they need to know to make a decision, what would you say?

Storyboarding (i.e. creating a visual outline of what you plan to present) is crucial to this process. I discuss storyboarding and how to create a story from data in the last section of this article. 

This is an obvious one, but it bears mentioning: the richer the dataset you have to analyze, the greater and more useful insight you’ll have to present to your audience.

Related to point #2 (what do you need your audience to know/do?), there’s another question: what’s your data saying?

Step #2: Choose an Appropriate Visual Display

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Once you’ve figured out the context for your presentation, including what data you have to work with, you need to decide how to display your data.

Why is it important to choose the best visual display for your data? There are a few reasons:

But the key is to know what type of visuals to use for the data you’re presenting.

In the section below I cover the 14 most common types of visuals, along with an explanation of when to use each, and examples.

14 Common Types of Visual Display

Step #3: Eliminate Clutter

The goal of a data story is to get your audience to know or do something (ideally do something).

The best way to sabotage that goal is to clutter your presentation with unnecessary stuff – because clutter increases the “cognitive load” on your audience.

In other words: don’t make your audience think too much!

Related to this, in his classic book The Visual Display of Quantitative Information, Edward Tufte introduced the concept of “data-ink ratio”, saying:

Here’s an example to demonstrate Tufte’s concept:

The graph with low data-ink ratio has unnecessary elements:

All of those elements pull the viewer’s attention away from what matters in the graph - the vertical bars.

This graph strips out the unnecessary elements, so the viewer’s attention is focused on the data (i.e. the vertical bars)

In her book Storytelling with Data, Cole Nussbaumer Knaflic introduces the Gestalt principles, which are based on the Gestalt psychology idea that humans naturally perceive visual elements as organized patterns and objects.

Below is a great graphic from Fusion Charts that illustrates the Gestalt Principles:

Gestalt Principles of Visual Design

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So when designing your graphics (and presentations), use the Gestalt principles above to influence what your audience pays attention to. And when reviewing every component of a graphic or presentation, always be thinking: is it necessary to include this?

By doing so, you’ll have more success getting your audience to pay attention to what you want them to.

Which brings me to the next step…

Step #4: Focus Your Audience’s Attention

In the last section I discussed how to remove clutter from a presentation, in order to highlight its most important components.

But eliminating distractions is only the first step – now you want to purposefully focus your audience’s attention.

Here’s the key to this part: you can focus your audience’s attention without their effort, by using something called “preattentive attributes”.

(Irony alert! Doesn’t the term “preattentive attributes” sound complicated and like it requires a ton of effort?)

Anyway, it simply means getting your audience to notice specific things in your presentations without effort by using a handful of visual elements:

Let’s look at each one…

Focus Attention Using Colour

The key here is to use contrasting colors in order to focus the audience’s attention.

Here’s a simple example from Storytelling with Data. Look at this group of numbers:

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Now look at the same set of numbers, but with a slight change:

How effortless is it for your eye to pick out the number 3 in the second set?

This is an example of using colour (specifically, contrasting colours) to automatically focus attention. 

Focus Attention Using Size

Another one that’s straightforward – to make something instantly noticeable, make it visibly larger than everything else:

Here’s a subtle example of slightly shifting the position of something on a page. See how it still stands out right away:

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And here’s a nice collection of preattentive attributes from a legend in data visualization, Stephen Few:

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Step #5: Think Like a Designer

The essence of this step is “form follows function”.

In other words, think about what you want your audience to do with the data, then figure out how to create a visual that can facilitate this.

In Storytelling with Data, the author makes a connection to these physical design principles:

Step #6: Tell a Good Story

What kind of data visuals would you create for this story?

At the beginning of this article I described what data storytelling is, and why it’s so powerful…

But what are the tricks to creating a compelling story?

There are tons of resources on the web that cover storytelling generally, but hardly any that describe a step-by-step process for telling a good story with data.

The best process I’ve seen was created by web analytics guru Brent Dykes. His process makes use of a classic storyline structure while integrating data (and data visuals). In the sections that follow, I provide a quick outline of his process. 

The Structure of a Good Story

Just about any movie you’ve seen has what is called “Freytag’s Pyramid”, which breaks down any dramatic structure into five acts:

Here’s an illustration of Brent Dykes’ story structure that integrates data (as you can see, he’s removed the “Falling action” stage of the pyramid):

Let’s go through each step:

Step #1: Introduction (the “Big Idea” & Hook)

At the beginning of your presentation you want to do two important things:

The “Big Idea” is the main reason you’re presenting to your audience. It’s the one important thing you want them to know and take action on.

Put another way, the “Big Idea” is an important discovery you’ve made. It addresses the “so what?” question that might be lingering in your audience’s mind (as in, “Why does this presentation matter?”).

The “Big Idea” you come up with will depend on three important factors:

The “Big Idea” mentioned in #1 should be important enough to grab your audience (at least the decision-makers) but embedding that idea in a hook is even more effective.

Here are some examples of excellent hook mechanisms:

Step #2: Rising Action (Analysis & Supporting Facts)

Once you’ve established the “Big Idea” and reeled in your audience’s attention with a “hook”, the next step is to increase the “action” of the presentation by telling your audience how you arrived at the Big Idea, by describing the analysis you’ve performed and supporting facts you’ve discovered.

By describing how you did the analysis and presenting some supporting facts, you’re building the narrative of the presentation.

At this point you want to display your facts using the data visuals described earlier in this article, because visuals + narrative are a powerful data storytelling combo.

Step #3: Climax (the “Aha” Moment)

The climax is the “aha” moment of the presentation, where you explain your hypothesis for the cause underlying the “Big Idea” statement.

To continue my example above, your analysis might find that in the last analysis period there was a big spike in mobile users on the site. When looking at the employment insurance registration page in the most popular mobile browsers, you found the page didn’t render properly, which made it very difficult to navigate.

So your “aha” insight might be that a poor mobile browsing experience might be the cause of the decline in conversion rate (i.e. percentage of employment insurance registrations on first visit). 

Step #4: Resolution (Recommended Next Steps)

In the resolution stage of your presentation, you clearly define the recommended next steps, based on your hypothesis from step #3.

Wrapping Up

In this article I covered the power of data storytelling – what it is, why it’s effective, and how to use it in your web analysis presentations.

How has storytelling benefited web analytics in your organization?