Data-Driven Design

Keep in mind that data should not be your only source of truth. It helps you make more informed decisions, but data alone is not enough. Also add user insights through research to your decision making.

Why is it important to apply data-driven design?

Designing good-looking products is not enough anymore. In addition to looks, usability and solving a user problem are just as important to the success of a business and its products.

Data-driven design is very helpful here because it helps design teams go beyond common best practices in an accessible way. That's because data-driven design is easier to get by than actual user research.

Sadly, not every project has the budget for a full-on user research phase. Data-driven design can be an acceptable alternative.

Misconceptions about data-driven design

Like any well-known way of working, data-driven design has its own misconceptions. Let's take a look at a few common ones and what they mean.

Data is equal to numbers

Your first association with data-driven design could be about quantitative data. And we get why. Data can be about numbers and statistics, but that's only one side of the story.

For example, if you notice a high bounce rate (many visitors leave the site after seeing only one page) and stop there, it would be useless data.

If you ask yourself why users behave this way, you can improve the website and perhaps lower the bounce rate. You do this by conducting user interviews.

It could result in quotes, emotions, and feelings from the user. That's more than just a number, but just as useful.

More data is better

As a UX designer within a data-driven project, you might be inclined to collect as much data as you possibly can.

This isn't always the best solution. At some point, you have enough data to confidently make design decisions.

For example, knowing the bounce rate within a period of time could be enough to get going. But then again, knowing the time-on-page within that period would be better.

Now you know two metrics. But should you know five? Or ten? Probably not! You can make a decision based on those two metrics.

Two different approaches to data-driven design

No methodology is more valid than the other. Choosing one depends on many things: on your objectives and available resources, for example.

Here's a list of data-driven design approaches and when to choose them.

Data-driven design

This approach is based exclusively on decisions made by collecting and analyzing quantitative data.

If the goal is to optimize the performance of a service or product for a specific area, this is the right approach. This approach is commonly used in conversion rate optimization (CRO) projects.

Data-informed design

When using data-informed design, you also involve qualitative data in addition to quantitative data for your decision-making. That means you not only look at data but also at the emotions and feelings of your target user.

How to decide what method to use

You can collect the data for data-driven design in many different ways. To make the right decision, always ask yourself the following.

• What is the project goal?

• How much time and budget do I have available for research?

• How easily can I reach my target user group?

There is no right or wrong answer because the choice depends on your skills, your project, and its objectives.

In case there's not a lot of time or budget, going for a quantitative data-only approach could be your way of doing data-driven design. The same applies when the users are hard to reach. Doing qualitative research becomes very difficult in that case.

Useful resources

Data-driven design, by design - UX Collective

Data-Driven Design: An Integral Part of UX Design - UX Matters