The Art Of Visualising Data For Your Target Audience

Data visualization has now entered the world of branding and marketing towards a powerful
storytelling medium. Businesses use these visual graphics to communicate their key insights
and drive engagement, in turn influencing decision making after converting complex data into
clear, visually appealing images. It’s pertinent that this data is visualized in accordance with
your target audience.

How to Visualize Data for Your Brand and Its Importance

Data visualization is what makes it easier to represent complex information in a simple way, as
mentioned above: turning raw data into visual strategies. Especially from a Branding and
Marketing perspective, where clarity in messaging can make many other hours go to earth land
or the need to revert missing but visuals will always work since humans are visual creatures that
like images.

Here are a few reasons why visuals work so well in marketing

Improves Retention: The brain processes and retains visual information faster than text.

Higher Engagement: Graphs, pie charts and infographics appeal more to human senses
resulting in higher engagement with these types of content.

Better Decision Making: Dashboards allow viewers to easily see trends and patterns so they
can make rapid, insightful decisions.

But not all data visualizations are great at providing insight. But the success of a visual largely
depends on how well it resonates with what consumers need, want and do.

Know Your Audience

When you start designing a data visualization, it’s important to know who is going to be using
this visual. This involves answering the following questions:

Who is the audience? Detail demographics such as age, sex, job and character of education
How well do they understand data? Think about whether your audience is used to looking at
data or if they need graphing that is straightforward and more visual in nature.
What are their goals? Know your audience: what are they hoping to get out of the visualization?
How do they consume content? Think about where your target market is hanging out. Are they
mobile-first users? Whether or not they like interactive content

If you consider the above questions before creating your initial data visualizations, then
hopefully all of the information will be fundamental to how and why each data visualization is
developed which in turn should make for clear takeaways that land making them easy to digest
both at face value but crucially they excite or disrupt too.

How to Design Great Data Visualizations

Now that you know what your audience wants, the next step is to create visualizations that help
communicate it. Key Principles to Take Home

1. Simplicity is Key

Less can be more when it comes to data visualization. Making your visuals too complicated can
leave your audience more confused than enlightened, thus diluting the message. Focus on
simplicity by:

Chart type: Pick the right chart that lists data expressions. Bar charts, line graphs and pie
charts are suitable to compare values against each other in a simple way while scatter plots &
heat maps correlations or actual distribution.

Keep it clean: Remove any extra material (Gridlines for example) to avoid adding too much
noise, so colors used are only those that present your data meaningfully.

Utilization of white space: Using more white space can help separate elements and make that
visual easier to read.

2. Focus on the Story

All information in your data visualization should tell a story. Extract your principle conclusion
from the data provided to you, and use it to make a story by implementing the right visualization.

This can be achieved by:

Colour Coding: For the key information, we highlight and put in them big or different colour.
One of the ways this can be done is by drawing in a strong color, to show an important trend.

Logical flow: Your visualization should take the viewer by hand through your data in a
structured path from giving out the information to reaching a conclusion.

Annotations: Add text to add context or comments where needed, tooltips allow your clients
grasp the import of what you have produced.

3. Customize for Your Audience

Design your visualizations catered to the needs and interests of people you are trying to reach.

Languages/ terminology: Speak in a language that your audience speaks. Do not use jargon if
the technical terms are for an unfamiliar audience, use humour, hyperlocalism and caricatures
to make your communication and visualization more relatable.

Interactivity: Make sure, for your data savvy audience to include some interactive elements
where users would be able to explore their own information.

4. Make Data Accessible

Data visualization is nothing without accessibility, make sure that your visuals include:
Alternative Alt Text: In order for visually impaired users to understand this, use an example of
a scent description or provide alternative text descriptions of visualization If Needed…

Color contrasts: Add complaint colors on the web page to have color contrast between text
and background.

Streamlined Data Formats: This simply means that you make the data available to everyone
so besides having a PDF version, have an accessible HTML page of your content.

5. Test and Iterate

You should test this design with a small group of your target audience and collect feedback.
Taking this feedback, hone in your designs to better convey what you are trying to say.

Case Studies: Examples of Audience-Centric Data Visualizations that Work

Source: The New York Times Election Visuals

The New York Times The NYTimes is someone who during election seasons in particular has
always kicked ass when it comes to creating super delightful, easy-to-understand data
visualizations that update in real time. They said that a well-designed site with simple, user-
friendly and general interest maps (and charts) for the casual voter who can’t be bothered to
wrap their head around complex electoral process results.

Being able to communicate complex information clearly and effectively is one of the most
important things in branding and marketing. Modeling approaches in different domains can
explain the use of large scale text, including how data visualization when done right is a game-
changer. After learning more about your target audience and following the best practices in
designing, you can create graphics that help not just inform but also engage, persuade and
encourage action.