Learning session 23: Analyzing UX Research

🚁 Topic

UX research investigates how users interact with a product, system, or service to identify areas that can be improved. It is crucial to analyze UX research to identify improvement areas and ensure effective changes.

🤓 What I learned

Whatever method you use to conduct user research, the next step should be analysis. You cannot simply apply raw data to your product.
Whatever data you analyze — web analytics, interview transcripts, field notes — your primary focus must be on your research objectives. It would be best to keep your product’s target audience in mind.

Importance of data analyzing

The main goal is to gain valuable findings, sort them out, analyze them, and generate insights that can be applied to the development process.

One of the most common errors UX practitioners make is jumping to negative conclusions based on unstructured statistical data and without proper research.

Data analysis gives you informed decisions and saves money and time. Instead of focusing on random numbers or user quotes, UX researchers should determine why users behave or think the way they do.

For example, if you find out that numerous users in your product don’t use the save button before you decide to redesign the button, you have to consider things like, what if I changed the button place? What if the users don’t know how to use it?

Attitudinal data

Analysis of user research data provides qualitative data. Qualitative data represents the users’ thoughts and feelings and allows us to acknowledge users’ assumptions.

Quotes and user stories gathered during user interviews, focus groups, card sorting, or diary studies are typical examples of this data type.

One disadvantage of attitudinal data is that users do not always follow through on their promises. As a result, attitudinal data can be confusing and sometimes lead to false assumptions.

Behavioral data

Behavioral data is the most valuable data collected during user research. It represents user behavior during contextual investigations, observational studies, and other ethnographic approaches.

Unfortunately, UX practitioners cannot always interview participants to understand why they behave in a specific way and why they choose another path over the other one.

Think about analysis early

You have to think about the analysis phase early in the process before the research has started. Research objectives and hypotheses help you stay focused and find accurate responses during your primary analysis.

For example, your goal research is to discover why your user isn’t interacting with the save for later button. In this case, you would have certain assumptions about the explanation for this behavior and look for specific factors when analyzing research data.

The next step is to create tags or codes that help you categorize and identify data in the final stages of your research.

Analysis in the discovery phase

Even during the discovery phase of your research, analysis is required. UX practitioners are human; some things can slip through their fingers if not documented.

Taking notes, reviewing records, and writing down first experiences while their memory is still fresh assist researchers in remembering and retaining essential thoughts or ideas.

It is a well-thought step to write down the words that participants use, their facial expressions, body language, and overall behavior; everything is necessary to understand their motivations, feelings, and needs.

Setting priorities and objectives

Analyzing numerous amounts of data gathered during the user study can be daunting, and you may be uncertain where to begin. Prioritizing and understanding your goals allows you to concentrate on relevant data when analyzing results.

Before you begin, remember why you decided to conduct UX research.

For example, you may find that your users are interested in meditation in addition to yoga. These findings are “nice-to-haves” and should be filed under “non-urgent.” When analyzing, however, “must-haves” should be given top priority.

Analyzing quantitative data

Here are some methods that assist you in getting insights from quantitative data:

• Cross tabulation enables UX practitioners to examine the relationship between various variables and recognize patterns, trends, and statistics within data sets.
• Conjoint analysis assistance through determining the best combination of features in a product or service by ranking them from most to least desirable.
• Gap analysis reveals the difference between the desired and current states.
• Trend analysis demonstrates how a value changes over time and what factors impact this change.

Questions when analyzing quantitative data

Working with numbers is required while analyzing quantitative data. While it might appear to be a repetitive and overwhelming task, quantitative UX analysis allows researchers to see patterns and tendencies and examine how users interact with a product. Based on the information gathered, we can determine what can be improved to help users achieve their tasks quicker and more efficiently.

While analyzing quantitative data, you can define the success rate of specific tasks, and the time users spend interacting with a button, what features users like most, their satisfaction with the product, etc.

Analyzing qualitative data

Human behavior is dealt with in qualitative research data, which may be more challenging to analyze than numerical quantitative research data. It may take some time to read through long documents and field research notes and decide which details are critical and can be ignored.

Furthermore, participant feedback can be contradictory, so researchers should remain objective and avoid dismissing viewpoints that contradict their beliefs. Thematic analysis is one of the most effective methods for breaking down and organizing large amounts of data.

Thematic analysis categorizes the collected data by assigning appropriate codes to individual observations and quotations.

While coding is not the same as writing code in a programming language, the researcher analyzes each text section and gives it a label that relates to the information. Some codes can be crashed or increased as they search for themes.

Questions when analyzing qualitative data

Qualitative research data analysis provides insights into why users behave a certain way. Also, it uncovers insightful information that helps to improve the user and customer experience.

While analyzing data, you have to note some questions:

• Why do your users like this product?
• Which features are helpful?
• Which functions go unnoticed?
• Do they react emotionally to certain features?
• Are they satisfied with the product?
• How does the product accomplish its goal?

Synthesize your findings

After analyzing qualitative and quantitative data, it’s time to generate your findings and draw conclusions. Synthesis assists researchers in transforming identified themes into something valuable.

Don’t mix up findings and insights. Discovery is a fact or statement derived from user research. A finding, for example, is that 70% of users abandon their shopping carts at the checkout page.

This statement shows insight is an outcome of human behavior or user determination. This example can teach us that the checkout page is too complicated or contains distractions that prevent users from making a purchase.

Contradictory results

Different research methods can sometimes produce inconsistent or contradictory results. For example, even if the task success rate among users is 100%, they may still report that they are dissatisfied with the app and would transfer to another if a reasonable alternative becomes available.

To avoid these types of inconsistent findings, begin to review the methodology:

Respondents: Different people may respond differently, resulting in contradictory results.

Tasks: Did everyone get the same amount of time for each task?

Environment: Have users been completing a task within the same environment, or has something affected their responses?

Data analysis: Is it possible that the data was overcorrected?

Make recommendations

The final analysis step entails making actual recommendations based on the most critical insights and supporting data. Incorporating recommendations into your research report is a valuable takeaway for stakeholders, motivating them to act quickly to solve users’ problems.

You can also distribute a document containing a list of the acquired insights and hold an open team meeting in person or remotely. Instead of providing ready-made solutions, you can collaborate and turn your insights into “how would we” questions.

🤺 What challenged me

One of the most challenging things I encountered while learning to analyze UX research was figuring out how to correctly weigh the different types of data I was collecting. For example, if I was surveying quantitative and qualitative data, I wasn’t sure how to give the qualitative data more weight than the quantitative data. I also struggled with understanding how to properly synthesize all of the data I collected to create actionable insights.

Thank you for coming this far. Any feedback or critique is appreciated. ❤️