Turn User Research Data Analysis into Actionable UX Insights in 5 Steps

Getting to Grips with User Research Analysis

So, you’ve just finished conducting your user research methods. You have collected piles of raw data. Now it’s time to start producing some actionable insights to improve your users’ experience and prove the value of your findings.

Synthesising and actioning your research is a critical point in the user experience research process. It is a stage in user research that’s usually rushed or left by the wayside because synthesis isn’t as sexy as conducting research or other aspects of UX design.

However, if you neglect your data analysis and synthesis then you’ve wasted so much of your time and effort researching in the first place. In the end, you’ll produce superficial and unconvincing user research that fails to improve the user experience OR impress key stakeholders.

In this article, we’re going to look at the stages of producing actionable research insights by following these five steps:

1. Focusing on your research objectives to better approach your analysis.

2. Carefully collecting data and organising it efficiently.

3. Boiling down your raw qualitative data through thematic analysis.

4. Separating your findings into insights.

5. Communicating the data to stakeholders and putting your research findings into action.

Step 1 - Get Your Objectives Straight!

The first step is simple. Get clear on your objectives (you can find out more about that here.)

The goal of your research is to answer research questions. Hopefully, you will have started your research project by outlining the objectives for your research project. These objectives will have guided the type of research you will have conducted and the focus areas for your user research. For example, if you are trying to build empathy for your user, you will have applied user-centric qualitative research methods like user shadowing, user interviews, or usability testing.

Before you go any further with your analysis, you need to revisit these objectives and align them with your goals for the analysis stage. Also, remember to keep your eye constantly on the user and keep your personas close to hand. What are their pain points? Their main motivations? Their behaviours and attitudes?

Understanding the objectives and your users will keep you on the right path while sifting through your data and grant you the best research outcomes.

N.B. Be sure that the objectives are there to guide you and guide you only. Don’t fall into the trap of manipulating data to fit your objectives and confirm your own beliefs.

Step 2 - Collecting & Organising Your Findings

We’re two steps in and we haven’t got down to any ‘analysis’ yet, but trust the process.

To be successful with your data, you need to be able to access it and understand it. That’s why the collection and storage of your raw data are crucial. Not only will this make your life easier, but it will also improve your efficiency and streamline your workflow.

Record your findings as you go

According to Zach Naylor, founder of Aurelius, a significant blocker to progress with research analysis and a massive pain point for user researchers is having a mountain of research to write up after a research activity. Not organising that data as you go is a huge contributor to research analysis being rushed and poorly executed.

Make an effort to record your data alongside your research. It will help you capture all the details and nuances of your studies. Unfortunately, going back to document research days and weeks after you conduct it leaves it open to the decay of time. Recording findings as you go will also ensure the data is ready to go for future use.

Create a (DIGITAL) repository for your research findings

Store your research digital and in one place. It will encourage cooperation and coordination between different teams and be an effective tool for searching through your data during analysis and beyond.

Creating a digital repository can help store tables, documents and databases all in one place. Here are two great options.

• Aurelius is a paid, all-in-one solution to store and track your UX research findings. Designed for the very purpose of what this article focuses on - user research data analysis. Aurelius creates a database for raw research, which can then be easily accessed to draw better conclusions from your research findings.

• Notion is an outstanding tool! We use it ourselves at KOMODO to organise vast amounts of procedures, plans and lots of other secrets. Notion does have a paid option, but the free version is a great tool to get started with organising your research and collaborating with your data collection. Here’s a great tutorial for setting up your own research repository, that comes with a free template to get started.

Step 3 - Getting Down With the Data

The wait is over. It’s time to conduct your data analysis; so pull up your pants, grab yourself a coffee and summon a pile of post-it notes.

N.B. In this article, we’re going to focus on analysing qualitative data instead of quantitative. To conduct the best user research, we recommend using a Mixed-Method Approach to User Research. This is because much of the UX user research you engage in will be qualitative, so we’ve chosen to focus our attention here.

Qualitative data in its raw and unrefined form can be terrifying. Unlike quantitative research, it doesn’t always fit into neat boxes. That makes qualitative data quite overwhelming, especially when you are staring at a pile of transcripts from user interviews, usability tests, focus groups, and your stash of notes from the field, too. But don’t worry, thematic data analysis is here to save our skins.

We're going to break it down now, so we're not feeling like Bruce below:

Identifying Key Ideas with Thematic Data Analysis

Thematic analysis is a widely accepted approach to analysing qualitative data. It is a popular methodology due to how easy it is to apply to user research. The purpose of thematic analysis is to identify key themes, ideas and patterns that create an overall picture of what is going on. It can roughly break up into three stages.

Coding the Data.

Nope. Not that kind of coding (though at KOMODO, we’re good at that too). A code, in thematic analysis, is simply a way to describe details of interest uncovered in the research. As you read through your research, you can start adding to the codes as and when you find them. They should be closely related to your research objectives and be snippets of the answer to your problem.

For example, look at this transcript snippet from a user interview for a new eCommerce website:

“Overall, I thought the website was interesting. I really liked the colours and the interactivity. But, the information on the website was sometimes difficult to read and understand. This meant that I found it difficult to find the product I was looking for. It was sometimes annoying that I had to wait for such a long time for the search feature to refine the result I was looking for and it frustrated me as I'm used to faster loading times”

Some points have been underlined above that could be translated into codes such as:

• Positive emotion

• Good branding

• Negative page experience

• Website navigation

• Speed expectations

By codifying the data, it makes a transcript less dense and more scannable for user research. The codes don’t need to be complicated and you can have many different codes; the main objective here is to start pulling the data apart. As you code more of the data, patterns will begin to emerge and key themes will start to become clear.

Identifying Key Themes & Interpreting the Data

As your research becomes covered in codes and annotations, key themes will inevitably start jumping out at you! Remember those post-its you summoned earlier… Now’s the time to start using them.

Like many UX design activities, organising your codes into key themes is a highly iterative process. Use your post-it notes and a big blank wall to sort UX findings into helpful categories. Interactive activities like affinity mapping can help you understand what is going on by creating clusters of codes to create your overarching key themes.

Going back to our eCommerce example. If many users highlighted website navigation as an issue, you might interpret ‘poor website structure’ as a potential key theme emerging from your results. Connecting all the dots can be a time-consuming process, but once the data is laid bare in front of you, the research will start to tell you a story.

Step 4 - Data Synthesis: Turn Your Findings Into Insights

The next step is to turn your ‘findings’ into ‘insights’. The two terms are often mixed up and are two separate ideas. Teri Slavik-Tsuyuki puts this nicely into words:

“Findings are based on hard and fast observations, and things we observe, that help us put all the data in buckets, and people in categories. But findings alone don’t lead to the Why. They just call out and help us categorize the What. Insights are penetrating, discerning understandings that unlock an opportunity. They cut to the chase and get to the Why.”

So, after going through the monumental task of uncovering your findings and figuring out the inner working of your user’s mind - now you’ve got to turn it into insights. Insights can then turn into a solution to a specific user problem. It’s the pot of gold at the end of the user research rainbow.

The real trick, however, is how to come up with valuable insight.

Remember those codes, key themes and affinity maps we asked you to spend time on? Those clusters of information, user quotes you highlighted and everything in between will add up to your insights. It should all be right in front of you. An insight should feel like a eureka moment. A hint at a solution to the problem your research was trying to solve.

Once you’ve discovered your insights, create a fresh page in your repository and write down any insights you can pull from your research. From there, you can then prioritise which tasks are better aligned to your research objectives - the ones that lead to the most profitable results.

Step 5 - Communicating Your Insights

A common problem user researchers face is getting stakeholder buy-in and demonstrating user research’s value. In the fifth step, we’re tackling just that. User research presents the foundations for the next steps in your design process and frames the research in terms of creating an actual answer to the problem you are trying to solve.

Sharing your insights with others can be extremely useful to uncover other perspectives. There are techniques you can use to convey the research findings in a more digestible way.

Problem Statements

The insights from your research will often translate into a problem statement. In design thinking, a problem statement helps us clarify the problem we are trying to solve. It is all about the people you’re trying to help, and the needs discovered in the empathy stage (user research) are what we use to frame it; it provides a focus to keep the statement user-centric.

You can format a problem statement using this formula:

[User] needs [Need] because [Insight].

How Might We (HMW) Questions

HMW questions can help you frame your user research insights or problem statement better. They will also be beneficial in idea generation in the next stage of Design Thinking. HMW questions are created off the back of the user’s POV. They are short questions that help break the POV into smaller pieces to tackle and make your research a lot more digestible.

Host a Workshop

At KOMODO, we love to get out clients into a workshop. A workshop is interactive, collaborative and can spark innovations. It’s an opportunity to put the research insights in front of people to find a unique design solution. In workshops you can cover:

• Empathy Mapping

• User Journey Mapping

• User Persona Creation

• Asking the ‘5-Whys’

Find out more about how to run a workshop in this article.