Using data-driven insights for better design with Adobe Express.

What do we mean by data-driven design?

Designers aren’t users (or at least can’t replicate the experience of every user) and so without insider knowledge, products and websites can sometimes be difficult to use.

Imagine this – you design a door. It’s a door that says PUSH. In clear letters embossed on the door it says push – of course you’d push it! But for the sake of design consistency there’s a handle on both sides.

Oh no, someone has now pulled on the handle, and everyone is looking. That’s not a problem you picked up in testing because you designed it to push. But your data shows that 20% of people pulled it. That data demonstrates an issue you could correct with data-driven design.

Look at it another way, data-driven design can help you fix problems you may not have known existed. You can identify behaviours from users and implement data-driven design solutions to make things even better (before you’re told.) You could even use data-driven insights to better tailor your product to your target audience.

What different types of data can you use to inform your designs?

There are two distinct categories of data you can use to inform your design decisions. They often focus on the what and whys of user behaviour.

Quantitative data.

Quantitative data focuses on measurable and numerical datasets for insights into user behaviours and trends.

An example of this could be counting clicks onto specific links or portions of the page. This could guide future layout decisions. Quantitative data is good for getting a sense of the what, when and how of a behaviour such as, what number of people pulled on the push door? from the data.

Quantitative testing methods could include:

• Analytics tools
• Data samples
• Tracking software
• Closed-questionnaires or surveys.

Qualitative data.

Where quantitative data works in the realm of the measurable and numerical, qualitative data focuses on the experiential. This observation of subjective user opinions, feelings or motivations gives a solid answer to the why’s of user behaviour. For example, why did you pull the push door?

Examples of qualitative testing methods include:

• Interviews, survey and questionnaires.
• A/B testing.
• Results.
• Usability tests.

Why is data-driven design so important for user experience (UX)?

Data-driven design thinking lets you create your product, service, webpage or advertisement from the ground up with your users in mind – even if they’re not fully aware it’s an issue. With behavioural data points and rigorous testing, you can inform designs both current and future.

• Get insights into what’s working. Data-driven design thinking doesn’t just tell you what needs to be changed – it lets you know what’s working. Then you can figure out why it’s working to learn from those principles.
• Spot quick wins that could help improve your design. Is your data pointing out some obvious flaws? Is the click rate on a certain section too low? Maybe people aren’t interacting with your product or service in ways you thought. Spot issues quickly with data insights to make swift changes to your design.
• Remove guesswork. Take the mystery out of issues with readable results that tell you where and what the problems are. This can help you build a business case for changes.
• Improve decision-making. Make decisions with confidence and by the numbers. With data-driven insights you can improve your designs to get the results you want.
• Boost efficiency. With data-driven insights, you can boost your team’s efficiency. Ideation can focus on solutions rather than guesswork on the problems.