Apply Statistics Into Storytelling

You just successfully transited from static storytelling to visual storytelling.

Introducing Types of Storytelling(I am only focusing on the first two types of storytelling for the purpose of this article.

)Static Storytelling (e.


fiscal reports, articles, books, slogans, letters, etc.

)Visual Storytelling (e.


design, films, TV series, paintings, photographs, etc.

)Interactive Storytelling (e.


prototyping, video games, presentations, teaching, tour guiding, etc)Performative Storytelling (e.


concert, street performers, circus, stage shows, dance, etc)Oral Storytelling (e.


parenting, folktales, monologues, phone calls, podcasts, etc.

)The similarities between statistical storytelling and UX storytelling are perceptible when you are putting both into perspectives.

To complete a general but not definitive regression analysis, one has toFind a problem or severalAcquire a large datasetStudy preliminary researchesCome up with a hypothesisTrim the dataset and create variablesApply data into regression modelsRun regressions and find significanceConclude or repeat from Step 4To complete a general but not definitive UX research, one has toFind and study the demographicsFind a pain point or severalCompetitor study and parallel analysisCome up with a hypothesisIterate idea sketches and storyboardsPick a winner solution (Read my article about how to pick a winner)Design and make prototypesCreate a questionnaire and select 5–7 users (Why 5–7 you ask? Read this article by Mitchel Seaman)Do user interview or testingDespite the similarities, statistics is fundamentally just static storytelling.

On this notion, you don’t have a target audience and you don’t need to know what they want to hear.

It is what the numbers tell you, people either read it and resonate with it or they don’t; however, visual storytelling like UI/UX design always has a desirable demographic, you make sure the visuals are designed specifically for them and well delineated what you want them to understand.

Furthermore, as mentioned previously, by shifting your perspective in viewing the dataset, you are able to change the nuance in statistics.

It gets criticised really quickly when people find flaws in your statement, instead of interacting with your audience, it feels like as if you were basically shouting out your opinions loud and forcing people to believe you.

Sure, your statistical analyses can back you up, but they only make your opinions (hypotheses) sound less ignorant, they don’t turn them into facts.

So if this way of storytelling isn’t practical in the case of persuading the audience, what have I actually learnt from statistics then?Introducing Data Visualisation(I don’t know why but it sounded so badass in my head, like a superpower.

)I once told my coworker,Hey, did you know that most people who visited our website also like to go to porn sites?He cringed a bit and looked at me like I was crazy.

I showed him this,Demographic search result of my current employer’s user base.

Provided by SimilarwebHe then believed me and forwarded this to the others.

It was a big laugh in my office that afternoon.

This is a simplified example of the usage of data visualisation in storytelling.

In a much bigger scale, you will have to address your intention of this storytelling first — who is this data analysis for?.Why did you choose to show these parts of the data?.What can your audience learn from it?.Then, you put your outcomes together and give them a makeover.

Visual storytelling is just fun.

I mean, who prefers to read a bunch of text and numbers when s/he can read a well-illustrated pie-chart that contains the same information?Typical data entry table.

Hard to get a general idea of what this table tries to tell.

A visualised version of the table above.

Now we can clearly see a trend in both selected categories.

But visual storytelling requires a targeted audience.

This is why user research is such an essential part of doing UX design.

Data visualisation helps me and my team to understand your users’ behaviours, this visual presentation stables communication with any party since there is no esoteric terminology involved, everything displays as how we perceive it.

And by utilising statistical analysis, I am able to finish my storytelling with a logical conclusion that already exists in the visual patterns, I simply have to point it out.

Thankfully, now there are many online services that allow anyone to do data visualisation with just a few clicks and types.

Here are some of my top recommendations:Google Analytics: good for in-depth website traffic analysisGoogle Trends: good for quick keyword researchSimilarWeb: good for competitor parallel researchStatCounter Global: good for international screen size, OS, SNS analysisAppFlow: good for mobile app user researchWhenever people scorn at my insights, I just show them I have well prepared and I have a visually comprehensive keynote to support my statements.

They don’t have to agree with me, but they cannot deny the numbers, because numbers never lie.

Introducing Statistical Visual StorytellingNow we know that statistics don’t always tell us the whole story because of selective intentions, bad static storytelling is just dull and difficult to understand and pure visual storytelling doesn’t have reliable inception.

In my long-run design approach, whenever I conduct user interview and storyboarding, I always utilise my data science background and give my audience a presentation that is both visually pleasant and statistically convincing.

STEP 1Identify and write down everything you know about your audience.

STEP 2Identify existing problems and conflictions.

STEP 3Do research on competitors and data-visualise what you find, list cons, pros and unbiased observations respectively.

For websites, you can use the online services I list above; for apps, app store reviews and Twitter are good places to go.

STEP 4Absorb the cons of the competitors and explain to the audience why they make sense (you can show positive reviews from real users to further support them).

STEP 5Provide conceptive solutions and proposals.

Do not steal ideas from your competitors.

My statistical mindset never undermines my UX design approach.

It has prepared me for the designer I am now because it taught me the importance of doing preliminary researches as well as the techniques in data interpretation, but most of all, it made me realise that by conducting statistical analyses along doesn’t solve real-world problems, it only magnifies them, further frightens me that those problems are legitimately true.

Upon graduation, I made a decision that I am going to change this narrative, that I am going to use my storytelling skill to raise the awareness of certain problems, to minimise damage, to create probable solutions.

With the rapid increase of digital products overflows the internet, users tend to seek solutions to problems they don’t have and enterprises tend to create problems that real users cannot solve by themselves.

Accessibility and functionality are both lost in this miscommunication.

As an IA/UI/UX designer, I am inclined to fix this askew experience for both parties.

I’m Carlos, an IA/UX/UI Designer based in Tokyo.

You can also find me on Twitter, Dribbble and my website.

Happy design ✍️References:Learning From the Feynman TechniqueThey called Feynman the “Great Explainer.


comDesigning for Data VisualizationIBM designers share the unique challenges and opportunities of designing for data visualizationmedium.

comData Visualization — Best Practices and Foundations“Clutter and confusion are not attributes of data — they are shortcomings of design.

” — Edward Tufteuxplanet.

orgStorytelling in DesignWhat it actually means to be a designer-storyteller?medium.



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