When I started my career in digital design more than a decade ago, I thought my work would revolve around colors, shapes, layouts, layers, imagination, and creativity.
Data and analytics? Pfftt, never even crossed my mind.
But times have changed. Designers can no longer rely only on their personal tastes and preferences, especially UX designers. After all, whom are we designing for, ourselves or the users? Of course, that’s a rhetorical question. This is why — as much as the creative-hardcore part of me hates to admit it — data is so important in UX design.
How Data Can Help UX Designers
Imagine inviting some people over to your house. You order ramen, sushi, and sashimi from your favorite restaurant because you just love Japanese food. As it turns out, the majority of your guests either don’t like raw fish, are allergic to gluten, or can’t stand wasabi. What happens next? Your guests can’t wait to leave or might even be sent to a hospital to get an Epipen shot. Disaster.
One way to avoid such a nightmare is to have a simple survey in your RSVP process. If someone confirms their attendance, have them fill out a form regarding their food preferences, diet, allergies, and other important notes. In other words, collect data.
This is similar to designing a user experience. By collecting and analyzing data, you get to know your users better — what they like and dislike, need and don’t need, and how to make the whole experience enjoyable for them, as well as fulfilling your business KPIs.
What Data Do You Need for UX Design?
The amount of data you get from various tools and analytics can be overwhelming. But hey, you don’t need to cram everything into your design! For starters, you can categorize your data into the following:
The raw numbers (Quantitative data)
In your analytics, you can easily find numbers like the ones on the following list. They’re also commonly called quantitative data.
Useful Quantitative Data for UX Design
- Web visits
- Most visited pages
- Most clicked buttons
- Least converting landing pages
- Highest exit rates
- And many more
The reason behind the numbers (QuaLitative data)
Why do so many visitors leave without doing anything after visiting this page? Why is that button not clicked as much? This is what your analytics don't tell you.
To be able to design a data-driven user experience, you need to figure out the reason behind the numbers. And, yes, these reasons still count as data even though they don’t contain any numbers. We call them qualitative data.
How to Analyze Data for Design Purposes
Though you can easily export quantitative data from analytics, you might need to have a team of data analysts or data researchers to really understand how and why you got those numbers and what you should do with them.
If you're short on resources, however, you can try some methods together with your UX designers to analyze your numbers. They’re pretty simple, so even designers who don’t have a background in data and research can try them.
3 Data Analysis Methods for UX Design
- A/B testing
- Usability testing
Conducting internal surveys among your team members can be the first step to determining three to five best options or alternatives for a product or service. These surveys can provide valuable insights into team members’ preferences, opinions, and experiences with various design options, allowing for a more informed decision-making process.
Next, you can conduct user surveys or interviews to narrow down your options, as well as collect data about their preferences, behaviors, challenges, and hopes. Keep the user survey results anonymous if necessary to keep things objective and unbiased.
Now that you have narrowed down your options and have two to three alternatives to try, it’s time to conduct A/B testing on different design elements such as:
- Image A vs. image B for the hero banner
- All-caps vs. sentence-case headline
- Floating vs. static CTA button
- And many more
After a certain period, you can see which alternative performs better. Keep that one or have another A/B test if you want more granular data.
Conduct usability testing alongside or after A/B testing. Have a group of participants — from different segments if possible — use your website or app. Collect their feedback and have another survey or interview if you can.
At a glance, all these processes to acquire data might seem lengthy and costly. But remember that they are investments for your business or project.
Why Data Matters to UX
Without all this data, your team would only be making guesses in their design decisions. And guesses are expensive. Imagine skipping A/B testing and sticking to a floating CTA button for months with super low conversion rates because, apparently, your users find the button intrusive. You end up spending more budget and resources than if you’d just conducted the test.
That’s why it’s important to allocate budget, time, and resources to get and analyze data so your design team can work more effectively and even come up with innovations to help solve your users’ problems.