Analysing UX research and synthesising results into valuable insights

Analysing UX research and synthesising the data that comes from it can play a huge part in the success of your end product. In this piece, we take a look at how analysing UX research will turn your findings into useful insights.

Analysing UX research and synthesising results into valuable insights

UX research can be a valuable part of the UX design process. However, completing your study is the first step in obtaining the insights you seek. Until you analyse the data you’ve collected into a set of findings and synthesise those findings into insights, you won’t be able to communicate how your research can help improve the user experience to your stakeholders and clients.

In this post, we’ll discuss how to analysing UX research using quantitative and qualitative data will turn your findings into useful insights. But first, let’s take a look at the difference between analysing UX research and synthesising UX research.

The difference between analysing UX research and synthesising UX research

After you’ve completed your study, your next step is to analyse the data. Analysis can be done in a variety of ways. The kind of analysis you choose to perform will depend on whether you’ve collected qualitative or quantitative data and what you were hoping to learn from your study. However, no matter what method you use, the goal of analysis is to identify the factual results of the study. In other words, it’s during analysis that you use the data you collected to arrive at a set of research findings.

Those findings may be interesting but they don’t provide any real insight until you engage in synthesis. Synthesis is the process of bringing all the findings from analysis together to extract insights and conclusions from the data, as well as a set of actionable recommendations for the UX design of the product. While analysis provides a set of facts, synthesis make those facts meaningful.

Analysis and synthesis often happen at the same time. Yet, while we plan our analysis in anticipation of the questions we want to answer, synthesis is an emergent process through which we make connections and come up with possible insights as we go. Below, we’ll talk about analysis and synthesis separately, but in reality these processes are likely to overlap.

Quantitative data analysis

If you’ve conducted a quantitative study, such as a survey with yes/no or multiple choice questions, an A/B test or an eye tracking test, you will be left with a large set of numerical data. Depending on how the data was collected, it will either already be laid out in a spreadsheet or will have to be entered into a spreadsheet manually, where each column corresponds to one question and each row includes one participant’s answers.

The dataset in the spreadsheet will then be analysed statistically. Programmes like R or SPSS can be used to run statistical analysis or formulas can be plugged into a Google or Excel spreadsheet.

Before you start running statistical formulas on your data, go back to the original goals of the study and decide exactly what questions you want to answer.

For example, maybe you’re curious to learn how long it takes a user to sign up for a newsletter. Or perhaps you’re trying to find out if users are satisfied with the various steps of a checkout process. No matter what the goals are, make sure to concentrate your analysis there.

Synthesising quantitative findings

The good thing about quantitative analysis is that once you decide on the variables you want to analyse, it can be extremely quick and efficient to perform. The bad thing about quantitative analysis is that because the data, and consequently the findings, are numerical, it can be harder to extract descriptive, interesting insights from the results. However, that doesn’t mean it’s impossible.

Quantitative findings, such as the average time to complete a task, participants’ satisfaction ratings with parts of a product or information on features they use the most can lead to insights about whether the UX for a certain task should be refined and what features should be redesigned or eliminated from a product entirely.

Quantitative data can also be analysed to compare and contrast the way users from different demographic groups use a product. These findings can provide insight into the different use cases UX designers must keep in mind as they’re creating the product’s user experience..

Qualitative data analysis

If you’ve conducted a qualitative study, such as user interviews, focus groups or ethnographic research, you’ll be left with a large amount of information in the form of words. If your participants didn’t provide their answers in written form, you’ll want to have all of the interviews or responses transcribed so you can easily read what participants said. While it can be expensive, it’s worthwhile to use a service like to transcribe your interviews so your time is freed up to focus on other tasks.

Once the data is transcribed, you can organise it in a number of ways. One is to put it in a spreadsheet where each row represents the answers provided by a single participant. Another option is to upload that data to a qualitative analysis tool like NVivo or Dedoose.

Just like with quantitative data, before you settle on a method for analysing the data qualitatively, you should revisit the original goals of your research and make sure that your analysis focuses on them.

For example, if you’re designing a real estate app where users can find houses for sale, you’ll want to focus on the demographics of potential users, what features they focus on most when searching for a home and what draws their attention to a given listing.

Although participants might have brought up other topics during your study, don’t include them in your analysis if they don’t pertain to your research goals.

There are several ways you can analyse qualitative data. Two popular options are content analysis and affinity mapping.

Content analysis

Content analysis involves looking for patterns in the data and then coding them. It can be especially useful for evaluating long text data such as interview transcripts. Codes are essentially labels that you can apply to each chunk of text that brings up a particular topic.

For example, for the real estate app mentioned above, you might use codes such as budget, location and number of bedrooms. As you go through the text data, you will then label each chunk of data where these subjects are discussed.

Here are the steps to perform a content analysis:

  1. Decide on codes. There are two ways to do this. On the one hand, you can come up with codes based on the topics you’re hoping to find. If you’re interested in seeing what people have to say on a house with a pool, you’ll include the code “pool” in your analysis. On the other hand, you can see what topics emerge organically. If you review the data and notice that decks comes up frequently, you’ll include the code “deck” in your analysis.
  2. Assign codes as you work. This can be done manually or with a qualitative analysis program like those mentioned above.
  3. Organise related codes into themes. Once you’ve coded all the data, look for codes that speak to the same general topic and place these under larger umbrella categories called themes. For example, codes like “gated community,” “good schools” and “safe location” can be grouped into a theme called “neighbourhood.”

If more than one member of your team is coding the data, you need to make sure everyone understands the codes the same way. To do this, before coding the entire dataset, each coder should code the same small part of the data and compare their work. If there is disagreement in the way the codes are applied, coders need to discuss the discrepancies until they’ve agreed on how to apply the codes consistently.

Affinity mapping

Another useful way to analyse the data is through affinity mapping. Affinity mapping is a visual way of organising the data but, like content analysis, the overall goal is to identify patterns and themes.

Affinity mapping

Although this is not the only way to conduct affinity mapping, User Interviews recommends taking these four steps:

  1. Write all the text data points on post-it notes. While this will likely lead to a lot of post-its, make sure to use as many as your project requires.
  2. Stick all the post-its to a wall, whiteboard, or any other large surface that people can gather around.
  3. Move the post-its into groups based on common themes that pertain to your research objectives. You may want to limit this process to about 20-30 minutes depending on how many post-its you have to organise and to prevent the affinity mapping session from sprawling.
  4. Continue to re-organise the groups until time runs out or the team has come to a consensus. If time runs out and team members still disagree on the groups, have a discussion until everyone’s on the same page.

Once you complete this process, you may want to label each group with an overarching theme that sums up the content.

Synthesising qualitative findings

Qualitative analysis can be incredibly time consuming, depending on the method you use and the amount of data you have to analyse. The upside is that the findings that you uncover – and the insights that can be gleaned from them –  are usually rich and detailed. Once you’ve completed your analysis, you should synthesise your data based on the common themes and concepts that arose.

Either as a team or on your own, evaluate each theme to uncover the insight behind it, including what makes it meaningful to the product’s UX design. Then articulate that meaning into a single insight statement. Your insight statements can answer questions like: Do the themes point to a particular pain point? Is there a need that isn’t being met by the product? What features do users want the most?

For example, in our real estate app, if the theme is “large backyard,” based on a finding that showed that users had difficulty finding houses with yards that would be big enough for kids and pets, the insight might be that the app needs to let users search for houses by yard size.

You’ll then organise your insights so you can communicate them to stakeholders and clients. Prioritise your insights by importance based on your research goals. For each insight, include a concise explanation of the finding that inspired it and a recommendation for how the UX team can take action based on the insight.

In addition, include quotes, stories and other examples from your research participants that help further demonstrate each of the insights and helps bring the product’s users to life.

The takeaway

Regardless of whether you’ve conducted a quantitative or qualitative study, you haven’t completed your job until you’ve analysed and synthesised your data. Once you’ve organised your data into a manageable format, your first step is to analyse the data using a method that ensures you will arrive at results that pertain to your research goals. This will be followed by synthesis where you will explore your findings for meaningful insights.

It’s these insights that are ultimately the biggest benefit of analysing UX research. Insights enable us to truly understand what users want or don’t want from the product we’re designing. When we conduct research that enables us to arrive at these insights, it increases our chances of creating a product users will embrace.


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