User analysis simply explained: methods, tools and use cases

What is user analysis?

User analysis systematically examines the behavior of visitors on a website or in an app, providing information on which pages are visited particularly frequently, where users jump off, which elements are clicked on and how visitors move through the content.

The aim is to better understand the behavior, wishes and experiences of users in order to derive opportunities for improvement.

Example user analysis:
An online shop finds that many visitors fill the shopping cart, but too often the purchase process is not completed. Through user analysis, the team recognizes that the check-out is too complicated. With this insight, the page is being revised.

Why is user analysis important?

User analysis helps you understand whether people can really use your digital offering, whether they want to use it and whether they can easily find the right ways to do so.

Without user analysis, you might notice that something isn't working, such as high bounce rates or few conversions. Why this happens, however, remains unclear.

How do user analysis and market research differ?

Market research examines the overall market, target groups and prevailing trends. User analysis looks at specific behavior when interacting with a product.

Market research asks people what they want. User analysis observes what they are really doing.

What methods of user analysis are there?

If you want to understand how people interact with your digital product, you should know why something is happening and how you can fix mistakes. This is exactly what quantitative and qualitative methods of user analysis exist for.

Quantitative methods of user analysis

Quantitative methods give you hard numbers. They help you to make user behavior measurable and identify patterns in it. Typically, this involves large amounts of data.

Examples of quantitative methods of user analysis:

• Web analysis using tools such as Google Analytics or Matomo: You can see how many users visit your site, how long they stay, or where they jump off.
• Heatmaps and scrollmaps (e.g. Hotjar): These show you how far users scroll, where they click, and which areas they ignore.
• Click tracking and session recordings: You can track entire user sessions and see how people navigate through your product.
• Conversion analysis: You can see where a purchase, registration or other important step is canceled.

Quantitative methods are good for identifying weaknesses in user flow and making the impact of changes objectively measurable.

Qualitative methods of user analysis

Through qualitative methods of user analysis, you can find out the reasons for user behavior. They help you understand the thoughts, feelings, and motivations of your users.

Examples of qualitative methods of user analysis:

• User interviews: You talk directly to your target groups and find out what bothers them, what they want and what they don't understand at all.
• Surveys & feedback-forms: An easy way to collect structured feedback, either on your website or afterwards.
• Usability testing: Users complete specific tasks with your product while you watch how they do it, for example using a think-aloud method.

Combining quantitative and qualitative methods of user analysis

Some methods combine both perspectives. A good example of this is A/B testing: Here you test two variants of your site against each other and measure which works better. You get numbers, but often the biggest value is understanding which content or structures are better received by your users.

How do I choose the appropriate method for my question?

The right method depends directly on what you want to find out. If you want to know where users are jumping off, then you need quantitative data, e.g. from a funnel analysis. On the other hand, if you want to understand why users drop out, you need qualitative methods such as interviews or usability tests.

Rule of thumb:

Numbers & patterns → Quantitative methods (analytics, heat maps, tracking)
Opinions & reasons → Qualitative methods (interviews, tests, surveys)

Decision support: Which method suits which question?

How does user analysis work in e-commerce, SaaS and apps?

The basic principles of user analysis always remain the same. However, the process of a user analysis differs depending on the context, such as online shops, software-as-a-service (SaaS) or apps.

User analysis in e-commerce

The online shop is about optimizing the buying process.

Typical questions:

• Where do users jump off?
• Which products are frequently viewed but rarely bought?
• How do individual categories, filters, or checkout steps perform?

Tools: Google Analytics, Hotjar, Clarity, A/B testing with VWO or Convert

Focus: Conversion rates, shopping cart abandonments, product page performance

User analysis in SaaS

Software-as-a-Service focuses first on conversion and then on sustainable use.

Typical questions:

• How do users get on with onboarding and navigation?
• Which features are actively used and which are ignored?
• Why do customers cancel (churn)?

Tools: Mix panel, heap, user interviews, in-app feedback

Focus: Retention, feature adoption, activation, churn reasons

User analysis for apps

In apps, every second counts and users are quickly distracted or annoyed.

Typical questions:

• Where is the operator stuck?
• Which screens work, which frustrate the user?
• How does load time affect behavior?

Tools: App Analytics (Firebase), UXCam, Lookback, Session Recordings

Focus: Touch behavior, flow interruptions, performance, user feedback

How does a user analysis work step by step?

A user analysis follows a clear process in five steps:

1. Define goal

→ What do you want to find out or improve?

2. Select method

→ Depending on the goal: Observe, interrogate, test (or combine).

3. Collect data

→ For example, via analytics tool, interviews, usability testing or session recording.

4. Evaluate results

→ Identify abnormalities, patterns and problems, if necessary, discuss them with the team.

5. Derive & test measures

→ Formulate hypotheses, implement and retest solutions.

Which tools can I use for user analysis?

The following is a selection of proven tools for user analysis, broken down by their main function.

Tools for quantitative user analysis

If you want to work with numbers and behavior tracking, these tools are your way to get started:

• Google Analytics/GA4: The classic for website tracking. Shows you page views, bounce rates, length of stay, goals, and more. GDPR-compliant, only with adjustments.
• Matomo: Privacy-friendly alternative to GA. Can be run on your own server and offers many features for tracking, funnels and heat maps.
• Microsoft Clarity: Free tool with heatmaps and session replays. Statements about scroll depth, rage clicks and fast loading times are particularly exciting.
• Plausible/Fathom: Lightweight, GDPR-compliant, and cookie-free. For small websites with a focus on data protection.
• Mix panel: More intended for apps or more complex digital products. Tracks user actions (“events”) and allows deep funnel analyses.

Tools for qualitative user analysis

If you want to understand why users act in a particular way, you need tools for feedback and monitoring:

• Hotjar: In addition to heat maps, it also offers surveys, session replays, and feedback widgets. Perfect for beginners
• Usability Hub: Platform for remote testing, such as first-click testing, 5-second testing, or design comparisons.
• User testing: Larger and more professional if you want to systematically test target groups.
• Typeform/Survicate/Google Forms: Tools for surveys and feedback forms. Flexible, easy and often free of charge.
• Lookback.io: Particularly suitable for moderated usability tests with video and sound, e.g. via zoom or live session.

A/B testing & experimentation tools

This is about specifically testing variants:

• VWO: Powerful testing tool for A/B, split, multivariate testing.
• Convert.com: GDPR-friendly, very good integration for complex tests.
• Optimizely: More for enterprise levels, but very meaningful.

Tip: You can find out more about this in our article on A/B testing tools.

Free tools for user analysis

Especially if you're starting small and paying attention to the GDPR, these tools are suitable:

• Matomo (on-premise): free with self-hosting, no cookie requirement
• Microsoft Clarity: free, many features for start-ups & SMEs
• Plausible/Fathom: GDPR-first, deliberately kept minimalistic
• Hotjar Basic/Smartlook Free: limited but usable free plans
• Google Forms/Feedback Tools: free of charge, ready for immediate use, no previous knowledge required

What are the typical problems with user analysis?

There are always technical, organizational or methodological problems in user analysis:

• No clear goals defined:

You start with user analysis without first determining what you actually want to find out. Then you'll collect a lot of data but learn little from it.

• The wrong method was chosen:

Not every method fits every question. Some rely exclusively on web analysis tools and overlook the lack of qualitative insights. Others do surveys, but with poorly formulated questions or an unsuitable target group.

• Data is misinterpreted:

You can see that many users leave a page early and automatically assume that the content is poor. But maybe the charging time was too long. Or users found exactly what they were looking for very quickly. The correct classification of the results is therefore missing here.

• Results are sanding up in the company: 

It often fails because no one does anything with the results. The UX research fails as a result or meets with internal resistance (“We've always done it this way.”).

• Data protection & GDPR are forgotten:

Data protection is a sensitive issue, particularly in German-speaking countries. If you use user records or tracking tools, you should follow the rules.

How do I carry out a GDPR-compliant user analysis?

To ensure that your user analysis is in line with the GDPR, you should consider three things:

1. Check the legal basis

→ For tools that process personal data (e.g. IP address), you need consent (e.g. via cookie banners).

2. Work in a data-efficient way

→ Use tools that only collect as much data as necessary. Tools such as Matomo (self-hosted), Fathom or Plausible are privacy-friendly.

3. Inform users

→ Your privacy policy should make it clear which tools you use, which data is collected and for what purpose.

Why is user analysis an important part of conversion rate optimization?

User analysis is part of a functioning Conversion rate optimization (CRO). The aim is to understand how users really interact, to specifically test this behavior and to optimize it sustainably based on the results.

With Axisbits, I know from personal experience that conversion rate optimization is very detailed and time-consuming, but Just as lucrative can be.

If your website or shop consistently falls below expectations, it might be worth taking a look from the outside. A neutral assessment often helps to identify hidden potential. Get in touch with us and we'll show you how we approach user analysis and conversion rate optimization of your site.

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