How to perform user research analysis & synthesis

User research analysis is where good research becomes a great product strategy. It's the process of finding patterns in what users said and did, connecting those patterns to business goals, and presenting them in ways that build confidence in every design decision.

In this guide, we look at where user research analysis fits in your workflow. You'll discover how to overcome common challenges and a step-by-step method for organizing, coding, and synthesizing findings.

Data analysis in UX research is how you turn raw research data into structured findings you can trust. It involves cleaning, organizing, and examining what you collected so you can spot patterns, quantify behaviors, and prepare it for reporting.

In a typical study, analysis sits in the middle of the research pipeline:

Collect data → Analyze data → Synthesize data → Report

Once the data is in, you need to sort, compare, and pressure test what you see across methods before you share it as an insight.

Here are some of the most common challenges, plus ways to overcome them.

Keeping these tips in mind enables you to produce more reliable, actionable insights with less effort.

At this stage, you’re turning observations about user behavior into structured, meaningful insights that can improve the user experience. The steps below outline a practical UX research analysis workflow you can use for most research projects, whether you are working with qualitative research, quantitative analysis, or a mix of both.

Start by bringing everything from your research sessions into one place and checking it against your research objectives.

You can focus on three things:

Once these three tasks are done, you have a clean foundation for qualitative or quantitative analysis and any methods you want to use next.

Once your UX research data is organized, the next step in UX research analysis is to code it. Coding means assigning short labels to pieces of qualitative research so you can sort, count, and compare what people said and did.

If you’re using a research platform like Maze, AI can take a lot of the time-consuming work out of this step. Maze’s AI-powered analysis features scan open-ended responses and interview data to surface common topics and sentiment in minutes, so you see patterns from the get-go. AI-supported user interview analysis can summarize sessions, highlight key takeaways, and point you to recurring themes across multiple calls, which makes it easier to refine your codes.

You now have coded data and broad categories, like ‘onboarding friction’ or ‘search and discovery’. Thematic analysis is where you interpret those categories and turn them into a small set of clear user insights.

Go through each category and ask what it really says about user behavior and user needs. Merge duplicates, split anything that is trying to cover multiple problems, and rename vague buckets into concrete themes. For example, shift from ‘onboarding friction’ to ‘people skip onboarding because it feels long and irrelevant to their immediate goal.’

Next, hold every theme up against your research objectives and segments.

From there, write your themes as an insight statement. A good insight briefly states what is happening, who it affects, where it shows up, and the evidence behind it.

For example, “New mobile users frequently abandon checkout at the card details step because they do not trust the payment screen, as seen in seven of 10 user interviews and a 45% drop-off rate on this page.”

These statements become the backbone of your research findings and recommendations. If you work in Maze, Maze AI can group responses into themes and summarize what people said, giving you an initial thematic structure you can review and refine.

Plus, Clips lets you group related highlights, write a short analysis and summary, and share a reel that shows the theme plus supporting moments from sessions, so each insight is backed by real user footage.

You need to show how users behaved, where they struggled, and what should change next, using charts, heatmaps, flows, and short, evidence-backed insights.

A UX report does three things:

In Maze, each study generates a UX report with key usability metrics, paths through your missions, and visualizations like heatmaps and scores. You can layer your own themes, summaries, and recommendations on top, then share the report as a live link or embed it where teams already work.

The end result is a visual record of the research findings that keeps your insights visible long after the initial readout and makes it easier for teams to connect specific UX decisions back to real user evidence.

Both quantitative and qualitative UX research methods provide valuable user insights, but each offers different angles of understanding your user. Mixed methods research combines both types, bridging the gap between hard data and user sentiment.

Here’s an overview of the main differences when analyzing quantitative vs. qualitative data in UX research:

Now, let’s explore different methods to synthesize and evaluate quantitative and qualitative data.

At this point, you should have already conducted UX quantitative research, such as live website usability testing, click tracking, or UX surveys. From here, you’ll have a large dataset of raw results.

There are different approaches to quantitative statistical analysis—descriptive and inferential.

For the purposes of this guide, we’ll focus on inferential statistics within quantitative user research analysis. But before jumping into the methods of evaluating quantitative data, here are key terms you’ll need to know:

Now, let’s look at four types of quantitative user research analysis.

In UX research, a t-test helps you infer if your sample-based assumptions apply to the entire population. For example, you might want to see if there's a significant difference in usability scores between new users (< six months) and experienced users (> six months).

There are three main types of t-tests you’ll typically use:

When to use T-tests: Use t-tests whenever you want to identify statistical differences between two groups.

ANOVA (Analysis of Variance) is used to test whether there are statistically significant differences between the means of three or more groups. It’s similar to a t-test, but instead of comparing A vs. B, you can compare A vs. B vs. C in one go. In UX research, you might use ANOVA to see whether customer metrics like NPS or task success rates differ meaningfully across multiple user personas or regions.

When to use ANOVA: Use ANOVA analysis when you need to compare the statistical relationships between more than one group.

Correlation analysis measures how strongly two variables move together and in which direction. In UX research, you might ask: “When CSAT scores go down, does retention also drop?’ or ‘As task success improves, do support tickets decrease?”

The result is a correlation coefficient between -1 and +1. Positive values mean the variables tend to move in the same direction, negative values mean they move in opposite directions, and values near zero suggest little or no linear relationship.

When to use correlation analysis: Opt for correlation analysis if you’re looking to assess the relationship between two metrics:

Regression analysis builds on correlation by asking, “Does a change in one variable help explain or predict a change in another?” Instead of just saying two metrics move together, regression estimates how much one variable (for example, page load time) affects another (for example, conversion rate).

In UX research, you might use regression to see how task time, number of errors, or device type relate to outcomes like satisfaction, retention, or revenue. For example, you could model how often users convert when a page loads in under 1.5 seconds versus when it loads slower, or how changes to a checkout flow impact completion rate.

When to use regression analysis: Go with regression analysis if you’re looking to identify the specifics of cause-and-effect relationships between variables.

Qualitative UX research methods provide a lot of data, so whether you’ve conducted user interviews, surveys with open-ended questions, focus groups, or contextual inquiries, step one is compiling the data into a central location.

Your results will likely look like interview transcripts, long-form text-based answers, or time-stamped user behaviors, so you’ll need a more robust location than a spreadsheet—here’s when your UX research repository can really shine!

From here, you can then begin to synthesize and analyze the data using one of these techniques.

Thematic analysis involves identifying recurring themes and patterns within your qualitative data and assigning codes (tags) to organize the research results. This allows you to summarize huge text-based databases into a few key shared insights and results.

You can make assumptions, see clear patterns, and determine the why behind your sample’s behavior. You can also turn this analysis into affinity diagrams to manage information groups and their relationships.

When to use thematic analysis: Use thematic analysis as a starting point for broadly grouping and organizing large amounts of qualitative data.

A narrative analysis is a qualitative research method that captures the complexity and richness of human experiences and provides deep insights into users' motivations, emotions, and thought processes. This helps you identify themes and patterns that may not be clear in quantitative results.

For example, you might spot that only a handful of users followed the intended sign-up path during usability testing. This is a fact, but it lacks an explanation. However, by analyzing the text-based data, you notice that users express confusion aloud, saying the red color of the registration button threw them off.

When to use narrative analysis: Go with this method for exploring user experiences and stories, understanding the context of their behaviors, and developing personas and journey maps.

Grounded theory analysis invites you to generate new theories based on data collected in the field. These new theories and frameworks need to be grounded in empirical data.

In UX research, you can use this method to develop new theories about user interactions and behaviors. For example, while coding users’ interview data, you might observe a correlation between positive CSAT and a highly-responsive support team.

When to use grounded theory analysis: Grounded theory analysis is best for exploring new or under-researched areas and continuously developing theoretical models that explain user behavior.

This method allows you to synthesize the text and all qualitative data to make generalizations, find patterns, and draw conclusions.

Here, you should also turn content into codes, like in thematic analysis. In content analysis, however, you want to identify the frequency and repetition of words and topics, getting to more granular detail, rather than just grouping recurring themes.

For example, if you’re analyzing unmoderated usability tests, you can code every comment that references the sign-up page as ‘SU’. Whereas, in thematic analysis, you’d group it into a bigger theme, such as ‘conversions’.

When to use content analysis: Content analysis is ideal for viewing data in snippets and digging into the weeds of specific insights.

UX research analysis will always need human judgment. You still define the research goals, choose the right methodologies, and decide which user stories matter most.

Where you don’t need to spend your time is on repetitive admin work like transcribing user interviews, tagging every quote, or rebuilding reports from scratch after every study.

Maze helps you streamline that research process end-to-end. It gathers results from tests, surveys, and prototypes in one place, uses AI to identify themes and summarize what users said, and turns everything into visual reports your team can act on.

From testing to analyzing, Maze supports teams looking to prioritize and streamline their user research processes.