And today I would like to talk about why designers should turn to data and how analytics can help improve UX/UI. Very often in companies, design teams, and analytics teams can only intersect at corporate parties or all-hands meetings. After all, the areas of responsibility of these teams are not very much in contact.
Designers in a ui/ux design and development agency in San Francisco create beautiful interfaces, offer user-friendly solutions, and apply modern trends. Analysts, on the other hand, build graphs, calculate income, and report the results of A/B experiments. However, there are some very good reasons that should make a designer want to be in constant contact with analysts.
Table of Contents
Understanding the product audience
Product design is very dependent on the audience that will continue to use this same product. From the experience of Dworkz, they say that different segments of users will react differently to similar changes. Moreover, often designers work with products where the audience is very technically advanced and understands the latest IT trends. So, they have to make every change in the product very carefully and carefully without hurting users. Somewhere slightly ambiguous wording or inaccuracy can cause a negative outburst.
Other design specialists can tell about the opposite situation when the audience of another product appears to be unassuming to the latest technology trends. So, such an audience usually reacts much better to new solutions such as huge sudden pop-ups, large fonts, and bright banners.
Here, it seems that aggressive design decisions, on the contrary, would scare people away. Yet, in truth, it would be much easier for the audience to do what was offered and have only one clear focus created for them in advance.
Finding the right solution
Often designers help to solve some explicit or implicit problem of the user. Yet, how do you know if a user has this problem? Some things may seem super logical for a designer or developer, but it turns out that real users did not have a need for what we offer them. Therefore, before diving into solving far-fetched problems, you can save time simply by looking at the data.
For example, everyone is well aware that additional funnel steps can scare the user away and it is likely that the more steps, the more likely we are to lose a user. In one of the teams, we began to develop a new registration flow, since the old one contained as many as 5 stages. It seemed to us that by combining the first three stages into one, we would significantly increase the number of registered users.
However, after looking at the analytics, it became clear that at these stages we lose only 10% of all users who started the process. And the main problem in registration was at the last step, where it was necessary to enter an unreasonably complex password and the user fell off after 2-3 attempts. The problem existed, but absolutely not where it seemed to us.
Design backlog prioritization
How to understand which task to do first, which second, and so on? You can, of course, follow the good old way: whoever shouts or writes a personal message more than anyone else has the highest priority. Let’s say there are three redesigns of subscription forms in the backlog.
Most likely, you can very quickly find in the analytics how many registrations we have from each such form and start working with exactly the form that most users use. If the data for prioritization is not so simple, it will not be superfluous to contact the analytics team and clarify with them the approximate audience volumes that will be affected by the change. To find out more, better contact the Dworkz team.
Professional growth of the designer
The market is now highly valued by designers who know how to work with data. It was 10 years ago that it was normal for a designer to live in a vacuum and not think about what happens to his great work after its implementation. Now business is asking the designer for a more proactive and broad approach.
A senior designer should be able to operate well with the main product indicators and know what affects User Retention, Adoption, or Stickiness. Find out where in the aha-moment product, track how the Bounce Rate, and Session Duration change, at what stages of the funnel the user is lost, and where it is abnormal. Without a clear understanding of the product, the growth of a designer is impossible, and such an understanding cannot be gained without analytics.
What should be done to start working with data? If you work as a designer in a product company and are interested in data access, start learning what analytics systems you use.
These could be solutions such as:
- Google Analytics;
- MixPanel Amplitude;
- Google Analytics;
- Tableau;
- Power BI;
- DataStudio.
If you were told that at least something from this list was implemented in the company, do not be afraid and ask for access. All these systems require minimal time to adapt to you as a user, not a developer. After spending even a week, you will easily get used to it and will know where and what you can peep useful.
Do not be afraid of a large amount of data and analytics. This can be very scary at the very beginning, just give yourself time. After some period, the designer will be able to read graphs and tables as well as any other specialist. To shorten the adaptation period, do not be afraid to ask for help from the analytics team. Data scientists will be very pleased that you have a request to access and understand the data. It will not be difficult for them to spend time on the intro.
Final thoughts
Well, in conclusion, I would like to say that in my practice there were very productive collaborations with designers. An analyst with his knowledge of numbers plus a designer with empathy for the user can generate very cool product solutions that would be impossible with the participation of only one of these areas.
Swati 
mindmingles