Atomic UX Research is an efficient research framework to organize and analyze research data. We show you how to make the best of your user research data with the help of this powerful approach in 4 easy steps.

What is Atomic UX Research? A short recap

Basically, Atomic Research means splitting up gathered information into small units which you can tag. By adding tags to your data, you make it possible to find connections between the single information units.
The approach of Atomic UX Research consists of four key parts: experiments, facts, insights, and opportunities.

• With “experiments“ you start the research process with studies that have been carried out already and anything else that creates facts.
• “Facts” are unbiased pieces of information that you’ve found out with your experiments.
• “Insights” are facts considered in context. They describe what you’ve learned.
• “Opportunities” result from insights. They are basically new hypotheses that can be tested again.

Use case – Atomic UX Research applied

Doing research doesn’t have to be limited to test websites and software. So, in our use case, we explored how people cope with remote work. Since working remotely has become the new normal for many companies, it pays off to improve working conditions. And we want to show you an easy way to do that with Atomic UX Research using our tool, consider.ly.

Introduction

In the following, we describe how we (fictionally) conducted qualitative UX research to improve our company’s all-over remote work situation. Our use case is completely made up. Nevertheless, it features different aspects of working remotely that we encountered ourselves.

What we wanted to learn about:

• challenges (my cat is always meowing during Zoom calls)
• advantages (having my cat sit on my lap during work improves my mental health)
• possible improvements (having my manager provide a cat sitter during important meetings)
• and creative ideas regarding remote work (meetings with other colleagues so everyone can hold their pet to the camera)

…within a considerable amount of qualitative research data. With the help of Atomic Research and a UX research repository, this was not that hard.

Step 1 of Atomic UX Research: Experiments

In the first step, we’ve gathered knowledge about remote work. This means that on the one hand side, we did a so-called experiment. On the other hand side, we looked for useful information from older experiments.

Creating data

The experiment we chose was an interview series with our colleagues. We asked them open questions about how they feel when working remotely and recorded what they said. For example “Working remotely makes me run out of coffee”.

Collecting data

Next, we collected further information about working remotely. The sources we used included the following:

• Slack messages from official channels
  (“I really miss our coffee breaks together 😔”)
• External blog articles about remote work
  (“10 Powerful ways to stay productive during home office – and yes, this includes a fancy yoga pose to impress your Instagram followers”)
• An older qualitative survey on remote work that has once been carried out within our company
  (“Now I don’t have to get up to print something anymore because the printer is right next to my desk – great!”)
• A protocol of a Q&A session about remote work
  (“I think we all could use a little more social interaction.”)

Finally, we stored everything in our UX research repository. After the end of Step 1, our knowledge collection looked like this:

Step 2 of Atomic UX Research: Facts

In the second step, we wanted to make sense of our research data.

Defining a tag taxonomy

Disclaimer: this is mainly needed if you do it for the first time. In later use of the atomic research model you most likely already have a mature taxonomy and just add project-specific tags if there is no useful one in the existing taxonomy.

Atomic UX Research thrives on tagging. Therefore, we defined a tag taxonomy. This includes setting up tag categories related to working remotely. Since our study is quite small, we only defined one tag category.

Under our category, “Key factors of remote work”, we summarized everything that has an impact on remote work.

Tagging data

After we’ve defined a structure by setting up our tag category, it was time to practically apply Atomic UX Research 🚀!

First, we applied tags to single sentences. For example, when a person said that they miss person-to-person communication, we applied the tag “communication” to the matching sentence. This way, we also avoided personal bias.

Conducting sentiment analysis

We wanted to go even further with analyzing our data. As a result, our notes contained sentiment tags dividing the statements into ones with positive and ones with negative connotations. Accordingly, consider.ly tagged sentences containing statements like “I love home office” with “positive”.

The sentiment-related tags “positive” and “negative” belong to the new tag category “Sentiments” which was automatically added to our tag taxonomy.

A fully tagged note looked like this:

Step 3 of Atomic UX Research: Insights

During tagging, we saw that there are many things to explore: How do people manage their work-life balance? Where do they see challenges? Do pets improve the mood while working remotely?

For our Atomic UX Research use case, we picked three things we felt had big importance to our company’s employees. We made them our insights:

1. Getting or giving people feedback is hard
2. The workplace is too noisy
3. There is too little social interaction

Consequently, we searched through all of our notes to find related information. Then, we attached the matching tagged text passages to the relating insights. This way, we supported them with original quotes from our sources. 

That means that when several people mentioned loud workplaces, we came up with the hypothesis that “workplaces are often too loud”. Then, we looked for matching quotes and found e.g. Jessy saying “my cat is always meowing during Zoom calls”.

But it’s not only Jessy’s cat. It could also be Mike’s annoyingly singing neighbour or Tom’s flatmate renovating her room. All these bits of information support our observation and make it a useful insight.

Step 4 of Atomic UX Research: Opportunities

The fourth step of Atomic UX Research is to identify opportunities. In our case, the opportunities were dedicated to the idea of creating better working conditions for our employees.

In the previous step, we wrote down our observations and the relating quotes. After that, we derived the following opportunities:

1. Getting or giving people feedback is hard
   ➡️ We should exchange feedback regularly, e.g. in dedicated meetings.
2. The workplace is too noisy
   ➡️ We should provide noise-canceling headphones.
3. There is too little social interaction
   ➡️ We should offer possibilities to socially interact, e.g. a virtual coffee break.

Use Case Summary

That’s it! With those four steps, we carried out internal Atomic Research on remote work.

To sum it up: First, we’ve carried out experiments and collected research data. Second, we applied tags to structure our information.

Third, we created insights which we supported with original quotes from our notes. And fourth, we found opportunities related to the issues identified in the insights. These can be tested in the future.

When it comes to the topic of noisy workplaces, the following picture sums up what we did:

Thanks to Atomic UX research, we managed to build a complete picture of the aspects that affect our employees while working remotely.

We could now go on with telling our colleagues about what we found out and brainstorm on more solutions together.

Why Atomic UX Research is so powerful

Imagine you are Jessy from our example and you want to convince your manager to provide you with noise-cancelling headphones. Walking up to him saying “My cat always meows while I’m in a meeting” would sound quite funny. It sounds like a very personal problem and a solely self-centred and based solution. 

By using the Atomic UX Research approach the insight  “The workplace is too noisy” can be shown along with data. With real evidence from others. And thus separating the insight from gut feelings and personal opinion. Using the model is connected to those supporting facts but can be traced and dug into. A great way to underline your findings. A great way for your manager to see the quality of your work and the transparency and structure behind it. insight.

Do you get the point? Whereas in our example, Atomic UX Research is used to solve internal issues, it serves as well for all other kinds of user research.

Why? Because it allows you to…

✔️ Include different types of sources

✔️ Connect old and new data

✔️ Quickly come to conclusions

✔️ Easily visualize results

✔️ Derive new research questions

By establishing Atomic UX Research over a longer period of time you can

✔️ Additionally link existing more facts new insights (more evidence)

✔️ Work more sustainably by re-using and re-cycling prior research data and insights

✔️ Share your research and collaborate with other researchers on a familiar data structure and logic

We hope our use case encouraged you to try Atomic Research. If so, we promise it’s worth it!