Maze study analysis and results

You’ve launched your study, recruited participants, and collected responses. Now comes the most important step: turning raw feedback into actionable insights you can trust and use to make product decisions.

Maze is designed to support analysis across quantitative signals, behavioral data, and qualitative feedback—whether you’re reviewing a prototype test, survey, card sorting, tree testing, or moderated interview.

In this chapter, we'll cover:

By the end of this chapter, you'll be able to confidently analyze research results, uncover key insights, and share evidence with stakeholders.

Once responses start coming in, your results are available directly on your live study in the Results tab.

Maze gives you two primary ways to look at your user research data:

You can use both views. Block‑level results help you quickly spot patterns in user behavior and usability issues, while participant‑level sessions add the context you need to explain those patterns to stakeholders and decide what to change next.

Unmoderated studies combine scale with behavioral and attitudinal data. Maze structures the results from these studies around the building blocks of your Maze (which you set up in chapter 3 of this guide). So you can move easily between high-level usability trends and underlying behaviors that drive them.

For unmoderated studies like prototype tests and live website tests, goal-based tasks are the backbone of your usability analysis. Each task shows you how participants finished a flow, how long it took, and how much effort it required.

In goal-based tasks, paths are grouped into three categories:

Don’t stop at success rate. A task can show a high percentage of completed tasks and still feel hard to use if the average duration is long or the misclick rate is high. Both are strong signals of friction in the flow.

To understand what’s causing that friction, look at your goal-based task paths and heatmaps together.

Goal-based task paths show which routes testers took through the flow and how often they followed or deviated from your expected paths. Heatmaps show where people clicked, what they missed, and which elements drew unexpected attention, so you can pinpoint where the flow is breaking down.

Maze also provides heatmaps at multiple levels to help diagnose why outcomes occurred:

Closed-ended questions like multiple choice, opinion scale, yes/no, and matrix give you quick, structured data you can slice and compare. When you select one of these blocks in the Results tab, you’ll see an aggregated view at the top (counts and percentages for each option), with individual responses listed below.

Use this view to:

Open-ended questions help you capture how testers describe their experience in their own words—what they noticed, where they struggled, and what they expected to happen. When you select an Open Question block in the Results tab, you’ll see AI summaries and sentiment at the top, with individual responses listed below.

Use this view to:

Card sorting and tree testing help you see whether your structure matches how people think about your content and where navigation breaks down. The results focus on agreement and paths, so you can refine labels, groupings, and hierarchy.

For card sorting (open and closed), use the results view to:

For tree testing, focus on whether people can find things in your structure:

Maze supports two types of moderated studies:

Both generate rich qualitative data. The key difference is how much synthesis Maze provides upfront versus how much analysis the researcher performs manually.

First, we’ll focus on analyzing results from human‑moderated interview studies, then look at AI‑moderated results in the next section.

Regardless of the moderation type, every study gives you:

Human-moderated studies rely primarily on manual analysis of:

A typical review process includes:

AI-moderated studies are designed to accelerate analysis by surfacing insights automatically. After the AI moderator runs its first session, the Analyze page gives you an aggregated view of highlights clustered into themes, plus tools to refine and share those findings.

Clips let you turn moments from sessions into short, shareable evidence. Use clips to:

When reviewing clips, you can create highlights and tag themes. Reels are automatically generated from themes, making it easier to build a cohesive research narrative and share it with stakeholders.

Block-level results show patterns across your data. When you need to understand what happened for a specific person, use the views that show full sessions for each participant.

Maze gives you two main ways to review results by tester:

1. Responses (for unmoderated studies): In unmoderated studies like prototype tests, website tests, or surveys, the Responses table shows individual results for each participant. For every row, you’ll typically see:

2. Sessions (for moderated and AI-moderated studies): In moderated and AI-moderated studies, the Sessions view lists each participant session. For every session, you’ll see:

This view is especially useful when:

Pair session reviews with aggregated data to avoid over-weighting edge cases.

To go deeper, Maze lets you filter results based on participant attributes or responses, including:

Filters help you answer targeted questions like:

Filtered views help ensure insights are contextual and avoid overgeneralization.

Analysis in Maze is iterative. Start with aggregated results to understand what is happening, then zoom into sessions, themes, and clips to understand why.

With analysis done, your focus turns to reporting. Next, we look at how to turn your analysis into clear, shareable outputs using Maze reports, so findings are easy to present, discuss, and act on across your team.