Many product people fall into a common trap when tracking user behavior by focusing too much on numbers or relying only on user feedback.

The truth is that you need to find the silver lining between quantitative and qualitative data to analyze user behavior effectively.

Quantitative data shows you what users are doing.

But if you want to get to the heart of things, you need to understand the why behind those actions and how users interact with your product. That’s where qualitative data comes in.

Quantitative data

Quantitative data refers to insights that can be quantified and expressed using numbers. It can be categorized into five types:

• Product usage metrics show you how users interact with your product daily. So if you track only product metrics and have a bad TTV, it can indicate that users are not getting value from your product. And users who don’t reach value within the first 24 hours are 3x more likely to churn.
• User journey metrics map the user’s progression through your product, identifying where they succeed or struggle. Most users who churn don’t complete key feature adoption, so tracking these progressions is crucial.
• Engagement metrics measure the depth and frequency of users’ interactions with your product. For instance, a decrease in log-in frequency can be an early sign of churn.
• Customer health metrics such as NPS and CSAT scores can indicate user satisfaction or dissatisfaction.
• Business impact metrics connect user behavior to business outcomes. For instance, after the analyses, you can see that teams using 3+ integrations have 40% higher retention rates, meaning that integration usage strongly correlates with product stickiness.

As you can see a lot can be analyzed from quantitative metrics alone, but with qualitative analytics you can understand the “why”: Why are people falling off in the onboarding and not getting value? And most importantly, how can we fix this?

It’s only when you combine quantitative metrics with qualitative insights and visual data that you can truly decode user behavior signaled by your metrics.

What can you track with quantitative data

In SaaS, quantitative data helps analyze user behavior in three key areas:

1. User patterns: Capture clicks, page visits, and form submissions with auto-capture.
2. Product metrics: Track product health and user adoption with analytics dashboards using product analytics tools (like Userpilot) to monitor system performance, error rates, and key activation metrics.
3. Customer feedback: Track quantifiable inputs like NPS scores, CSAT ratings, and support ticket volumes.

Instead, combine metrics that enable thorough user behavior analysis and indicate product success such as:

• User Activation: Percentage of users completing key actions like finishing onboarding or using core features.
• Time to Value: Time taken for users to reach their first “Aha! moment“.
• Feature Adoption: Percentage of total users actively engaging with specific features.
• Retention Rate: Percentage of users returning over time (daily/weekly/monthly).
• Churn Rate: Percentage of users who stop using your product in a given period.
• NPS: Score showing customer likelihood to recommend.
• CSAT: Rating measuring satisfaction after specific interactions.
• CES: Score indicating the effort required to use your product/feature.

How to collect quantitative data

Now, the question arises: How do you collect all this quantitative data to analyze user behavior? Let’s explore some collection methods.

Automatically track events with autocapture

Implement autocapture using Userpilot to start analyzing user behavior across your product instantly. Once the tracking script is installed, configure domain settings and enable autocapture in the tool dashboard.

This will start tracking clicks, page visits, and form submissions immediately. As autocapture is a one-and-done task, you don’t even need any additional coding.

Now, instead of guessing or waiting for user interviews, you’ll get real-time insights into when users access features, where they get stuck, and whether they achieve their goals right away.

Let me show you what this looks like in practice: Say you’re revamping your product’s onboarding. Autocaptured data reveals that new users keep returning to the help documentation during setup, suggesting unclear UI elements. Or, when rolling out team collaboration features, you discover users create shared workspaces but rarely invite teammates, highlighting an adoption barrier.

This direct visibility into user behavior helps you iterate quickly and fix real problems.

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Set up A/B and multivariate testing to collect experiment data

A/B testing lets you test single variables like button placements or copy to improve problematic pages identified through analytics. Multivariate testing expands this by examining multiple changes simultaneously.

Testing these specific variables reveals how users naturally interact with different design options. This shows their preferences and pain points. By analyzing these user behaviors, you can identify which designs feel intuitive and where users might struggle or drop off.

These tests also provide robust quantitative data for analysis as each user interaction gets converted into measurable metrics.

For example, the A/B test results below show clear performance differences between the two onboarding flows.

The onboarding flow with specific teammate invite features achieved 19% higher completion rates compared to the control group, providing clear quantitative evidence of which design works better for user engagement.

Launch surveys to gather NPS, CSAT, and CES scores

Unlike the first two user behavior data collection methods, surveys turn user expectations and feelings into trackable numbers you can trend over time.

These survey methods work by asking users to rate their experience on specific numerical scales.

• NPS quantifies satisfaction and loyalty.
• CSAT quantifies happiness with specific features or interactions.
• CES quantifies how easy or difficult users find particular tasks.

Let’s say you released a new feature and want to see its impact on your customers, so you trigger an NPS survey after they use the feature. This can be a sign of a potential feature regression.

To gain even richer insights, combine these numerical ratings with open-ended questions that ask for explanations of qualitative responses and use them for user behavior analysis.

Monitor key metrics with customizable dashboards for real-time insights

You need to collect all quantitative data in one place to analyze user behavior patterns effectively.

Product analytics platforms typically include pre-built dashboards to simplify this process. For example, in Userpilot, you can access dashboards like:

• Product Usage Dashboard: Gather active user counts across periods, session duration metrics, and patterns in feature interaction.
• Core Feature Engagement Dashboard: Gather adoption rates for key features, usage frequency data, and user engagement patterns.
• New Users Activation Dashboard: Gather onboarding completion rates, time-to-value metrics, and activation milestone data.
• User Retention Dashboard: Gather cohort retention rates, churn indicators, and long-term engagement patterns.

And yes, in addition to these ready-made views, you can always create custom user behavior analytics dashboards within Userpilot that combine your most relevant metrics.

Use quantitative funnel analysis to track conversion rates

To understand complex user behaviors like drops in onboarding flows, you need to analyze the complete user journey.

Funnel analysis helps you break this journey into measurable steps by measuring how many users move through different funnels and how long they spend between stages.

This shows you exactly where users drop off in your product experience, whether it’s during onboarding, feature adoption, or regular usage patterns.

For instance, if 1000 users sign up but only 100 complete your onboarding flow within the first day, you know exactly where to investigate for friction points.

Check how many users adopted a feature with a retention table

When you release a new feature in your product, you want to know how users are engaging with it, right? But simply looking at raw numbers like total sign-ups or clicks doesn’t always paint the full picture.

For deeper insights, a retention table is an invaluable tool to track feature adoption over time.

By tracking retention over time, a retention table reveals the real impact of a feature on user behavior. Are users using it just once and then forgetting about it? Or are they consistently coming back?

Qualitative data

Qualitative data refers to information and concepts that can’t be measured in numbers. It includes direct insights about how users feel about your product and why they use (or don’t use) certain features.

Without these contextual insights, your user behavior tracking will only show surface-level patterns that can be easily misinterpreted and lead to incorrect conclusions.

In other words, quantitative data shows you ‘what’ happened, while qualitative user behavior data reveals ‘why’ and ‘how’ it happened.

Now, let’s break down what you can actually track with this user behavior data.

What can you track with qualitative data?

Qualitative data lets you dig beneath surface-level metrics to understand user behavior at a human level as you’ll be accounting for feelings like:

• User motivations and goals: What users want to accomplish with your product, from saving time on daily tasks to solving specific business problems.
• User pain points and jobs-to-be-done: Which obstacles prevent users from achieving their goals, and what core tasks do they need to complete?
• User perceptions: How users evaluate your product’s usefulness, which capabilities matter most to them, and what they feel is missing.
• The reasoning behind actions: Why users prefer certain features, abandon specific workflows, or use your product differently than intended.

How to collect qualitative data

Now, how to understand the deeper context behind user behavior?

Collect qualitative feedback from users with in-app surveys

In-app surveys help gather user feedback directly, and you can create them easily with Userpilot using ready-to-use templates, multi-language, and no-code building features.

Place these surveys strategically after specific interactions for a high response rate, after they cancel their subscription for instance.

Once you’ve completed the setup, start collecting qualitative insights like feature improvement suggestions, upgrade/downgrade reasoning, NPS follow-ups, and more.

Use session replays to see how users interact with your product

Unlike basic user behavior analytics, which just shows numbers, session replays give you an unfiltered window into user behavior. This lets you gain contextual insights into how users navigate and use your product.

Use product analytics to identify friction points. Then, watch the replays to uncover what caused the friction.

Now that you understand how qualitative user data helps with user behavior analysis, let me share how to collect these valuable user behavior insights from your users.

So, as you now have all user behavior analytics and insights at your disposal, it’s time to piece it all together and analyze user behavior.

What to do with the user behavior data?

Once you have collected different types of data, conducting user behavior analysis becomes much easier.

Take onboarding completion rates, for example:

• Autocapture data shows that 60% of users drop off at the integration setup step.
• Survey responses point to confusion about technical requirements.
• Session recordings reveal users hesitating on the API key page, repeatedly clicking the help icon, and eventually dropping off.

This three-dimensional view specifies that you need to provide better guidance about technical requirements to influence user behavior positively. It’s not just a vague “users aren’t completing onboarding” problem.

User behavior analytics tools to track user behavior data

As we’ve seen, relying on any single type of data to analyze user behavior gives you an incomplete and potentially misleading view.

Therefore, a good user behavior analytics tool should let you collect quantitative, qualitative, and visual data together. Here’s how some of the best-known names compare.

Now, let’s examine the pricing structure of these user behavior analytics tools.

• Pendo offers custom enterprise pricing ranging from $25,800 to $132,400 annually, according to Vendr’s contract data. Final costs vary based on usage and selected features.
• Amplitude pricing starts at $588 annually for basic features. Enterprise customers receive custom pricing based on advanced feature requirements and usage volume.
• Mixpanel offers Growth plans starting at $288 annually. Additional features and higher usage limits are available through custom pricing.
• Appcues pricing begins at $3,000 annually. Like other platforms, pricing scales with feature access and usage requirements.

But if you look at the feature table above, Pendo and Userpilot are the only user behavior analytics tools offering all-in-one solutions. However, their approaches differ significantly.

Pendo targets enterprises with complex usage-based pricing that can fluctuate monthly based on a product’s growth. This requires constant monitoring of usage limits.

In contrast, Userpilot delivers the same comprehensive feature set with transparent, predictable pricing starting at $2,988 annually.

The session replays add-on is available in the Growth plan at $9,588 annually. This includes all core features for user behavior analysis and clear scaling options as your product growth.

“High price was one of the decision criteria to move from Pendo because we were paying lots, and we were not using it.” – Leyre Iniguez, Customer Experience Lead at Cuvama

If you want to know more about how Userpilot’s user behavior analytics features can help you, book a demo here!

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