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Did you know that in the 1980s, American Airlines saved $40,000 per year by introducing just one change in their operations? They found out that no one ate olives in their in-flight dinner salad. They removed just one olive per person, saving the company a fortune over the years. The way they did that was simple: measuring the user experience.
User analytics is the same thing but for your product. By measuring how users interact with your product, you can learn more about them. The findings can impact your future roadmap, the integrations you build, how you market and sell your product, and more.
Today, we’re going to show you what user analytics is, why it matters, and how you can get started with it.
User analytics is the process where product, engineering, marketing, customer experience and other experts track how users are interacting with a product. Through the use of user analytics tools, they collect qualitative and quantitative data about user engagement and experience.
The main purpose of user analytics is to find out where users drop off, what they find valuable, to establish and confirm hypotheses about their use of the app and most importantly, to create a better product that someone wants to use and pay for.
Tracking user behavior is crucial for your product and overall business, as any product manager, marketer or executive can tell you. But here are some specific benefits to tracking user analytics.
User analytics help you determine the way your customers interact with your product. Which functionality they are (not) using, which features are not useful to them and which aspects of your product may be broken.
By better understanding the way users behave, you can refine your product, its UX and UI, come up with new features and integrations and guide your roadmap.
The role of marketers is to create demand, but they cannot do that if they’re focusing on the wrong things. For example, you may have a retail app and you’re promoting ease of use as your biggest advantage. On the other hand, your customers may find the on-premise retail POS system much more useful.
User analytics help you uncover the strengths of your product. When you know what your user base finds valuable, you can apply that knowledge to your marketing. This can not only help with the retention of your existing customers but also with new customer acquisition.
By taking a look at real-time user data, you can find out if a certain aspect of your app is causing users to churn. Visualizations such as heatmaps can show you if someone is rage-clicking in your product or simply leaving it after unsuccessfully trying to do something.
Armed with these insights on customer experience, your engineers can remove bugs and friction points for your customers. As a result, you get better UX and UI, higher retention, and more profit.
With the help of user analytics, you can create cohorts and segments in your user base. This can lead to amazing analytics data. For example, you find out that enterprise users of your products are big on using your API and white label features.
You can then relay this information to sales. In their future calls with the enterprise demographic, they won’t bother showing your interface or integrations - they can move straight to pitching API and white label as your biggest selling points.
Behavior analytics help you find out what users think of your product without actually asking them. While surveys and questionnaires are extremely useful, they require action from your customers. And for many of them, taking a few minutes to answer questions about their product usage is too much of an effort.
User analytics captures feedback for product teams directly in the product. This means two things. One, your customers give you qualitative data just by interacting with your product. Two, the feedback is contextual as it happens right as they are using the product - not days later when they are doing a survey about it.
The main difference between user analytics and end-user analytics is that the latter is built for your customers. Let’s explain.
For example, you have an app for tracking deliveries as they are shipped out through the country. You sell the app to couriers who deliver those packages. Within your app, you - as a SaaS vendor - would have user analytics to track how couriers are using it and which problems they are running into, so you can improve your product and make it easier to use for couriers.
Within your app, you can add end-user analytics, for the couriers. For example, if a courier wants to check the most optimal routes for delivering packages, or compare the time to deliver packages during different times of day to plan their deliveries more efficiently. Visualization is a huge part of end-user analytics, as it helps your data become easier to understand and facilitates decision-making.
End-user analytics is crucial if you want to provide more value and attract new users who want more value out of your SaaS product or apps.
Go beyond traditional user analytics by integrating courier management solutions to ensure seamless and real-time tracking of deliveries. These solutions are designed to improve the efficiency of routes, enhance customer service, and optimize delivery schedules. Discover which courier software can empower your business to adapt to increasing logistics demands while providing visibility across all operations.
For more examples of how end-user analytics work in practice, make sure to check out our case studies. And to get started today, you can build your first analytics dashboard in Luzmo, for free!
Thanks to a variety of tools and systems, getting started with user analytics is actually pretty easy. Here is how to get started.
What does the user want to do once they sign up for your app? What is the ultimate goal in their user journey?
For example, if you have an invoicing app, there could be multiple use cases, but it boils down to one goal: creating and sending invoices and getting those invoices paid.
By finding out what the users have set out as their goals, you can use different metrics and KPIs to find out if they are achieving them or not.
There is a multitude of metrics that you can track for user analytics, depending on the user goal from step one.
For example, if you want to measure website analytics, you could use metrics such as: session length, bounce rate, traffic source, conversion rate from visitor to free trial, conversion rate from free trial to paid user, completed goals in Google Analytics and many web analytics KPIs.
You can also use survey metrics, as surveys are an excellent way to get quantitative insights. Examples include NPS, CSAT and CES scores.
Perhaps the most important for the customer journey are the product metrics. Examples include feature usage, active users, product activation rate, product adoption and others.
Once you find out which metrics are the most important for your users and their goals, you need to decide on an analytics platform to measure them.
For example, if you’re primarily concerned with web analytics, something as simple as Google Analytics can be just enough to measure them.
If you want to collect data from within the product, you can use a specialized tool such as Userpilot. It’s specifically designed for product analytics and can help you capture valuable data for your SaaS.
And if you want to get qualitative and quantitative insights from surveys, there are plenty of options that can be integrated with your SaaS app. Examples include Survicate, Typeform, SurveyMonkey and many others.
You can’t optimize your existing product experience without taking a good look at your existing state of affairs. Aggregate your quantitative and qualitative insights in one place (ideally, a dashboard), so you can analyze the behavior data with ease.
Remember that qualitative insights will take longer to summarize and it’s hard to assign numerical values to them.
During the analysis, pay special attention to user barriers, common complaints and pain points that your users may not be having. These should be a priority for your product and engineering teams.
In-app analytics can be a goldmine. Or a graveyard.
Used right, they tell you exactly what users want, where they get stuck, and what keeps them coming back. Used poorly? You’ll end up squinting at dashboards, second-guessing every move, and still launching updates that flop.
The difference isn’t the data. It’s the questions you ask and the actions you take.
This article breaks down how to stop staring at charts and start using in-app analytics to improve your product, drive engagement, and retain users—whether you’re a SaaS founder, product manager, or marketer trying to make sense of what’s happening inside your app.
Let’s turn that activity log into your most valuable source of truth.
Too many teams dive into analytics thinking “Let’s see what the data says.” That’s a trap.
You don’t need more data. You need better questions.
Start with what you’re trying to improve:
Good analytics starts with a clear goal. Once that’s in place, the metrics will matter more—and the noise will matter less.
Instead of tracking everything, pick 3–5 questions and use analytics to answer those. For example:
Clarity cuts through chaos. And in a product full of moving parts, that’s essential.
Your total user count? Not that helpful. Your average session duration? Misleading.
Why? Because averages lie. One power user spending 3 hours per day can skew your entire dashboard.
So instead of looking at global metrics, break them down by user segments. Compare:
When you zoom into how specific types of users behave, patterns start to show:
When you treat every user the same in your analytics, you end up designing for nobody.
A feature usage spike doesn’t tell you why it happened. A drop-off doesn’t tell you what caused it. You need to look at the flow.
Use in-app analytics to map actual user journeys—step by step.
Example:
Or:
This is where path analysis and funnels help you uncover the story behind the numbers.
You’re not just tracking usage. You’re tracking behavior sequences—and that’s where the magic happens. In many ways, this mirrors the employee life cycle, each phase, from onboarding to long-term engagement, holds key behaviors that can be tracked and optimized.
Once you see what successful users actually do (and in what order), you can design experiences that guide others down the same path.
Every sticky product has one.
It’s that moment of value when something clicks and users think, “Okay, I get why this is worth using.”
Maybe it’s generating the first report.
Maybe it’s importing data and seeing instant insights.
Maybe it’s just building a dashboard and sharing it with a team.
In-app analytics help you identify that moment—and then optimize the path toward it.
Start by analyzing what active, long-term users do early in their journey. What action do they take within the first 1–3 days that correlates with long-term retention? For startups and businesses working with MVP development companies, understanding and defining this key moment of value is crucial. These companies can help you build and test your product’s minimum viable features to ensure users quickly reach that “aha” moment, driving engagement and growth from the outset.
Once you find long-term retention, shape everything around it:
Your onboarding isn’t complete when users sign up. It’s complete when they hit the aha.
Not all activity is equal.
Opening a tab ≠ using a feature.
Clicking a button ≠ completing a task.
Spending time ≠ getting value.
In-app analytics becomes more powerful when you define and track meaningful events—those that show intent, effort, or outcome.
For example:
The more intentional your event tracking, the more useful your conclusions.
And the better you’ll be at predicting future behavior, not just observing past activity.
Silent frustration is a killer.
If you’re only tracking logins and sessions, you’ll miss the moments that quietly erode retention:
Look for micro-signals of friction in your analytics:
You can even layer heatmaps or session recordings to dig deeper.
Once you spot where users struggle, you can fix it before it becomes a support ticket—or a churned account.
Here’s where teams get stuck: they set up dashboards, review them once a month, and forget them.
Instead, make your in-app analytics part of a feedback loop. That means:
For example, let’s say you notice that users who engage with Feature B are 3x more likely to upgrade. You run an experiment: add a guided tooltip in onboarding that leads to Feature B.
After two weeks, you check: did more users reach it? Did upgrade rates move? What did the qualitative feedback say?
This loop turns analytics into product fuel—not just passive reporting. And if your team’s small, that’s okay, plenty of startups bring in part‑time analysts or freelance UX specialists to help uncover patterns faster.
It’s easy to get obsessed with pretty charts.
But metrics are only as useful as the decisions they drive. So always tie your analytics to clear outcomes. For instance:
If your analytics don’t lead to actions, they’re just decoration.
A quick note for product marketers and customer teams: analytics aren’t just for you. They’re for your users too.
Consider what usage data you can surface inside your app to help users understand their own patterns.
Examples:
This builds habit loops, boosts perceived value, and makes analytics part of the experience—not just a back-end function.
Used well, in-app insights aren’t just about you understanding the user. They’re about helping the user understand themselves.
In-app analytics isn’t about watching what users do. It’s about learning what they need—and helping them get there faster.
If you want better retention, stronger adoption, and a product roadmap that actually aligns with user behavior, stop guessing. Stop staring at vanity metrics.
Ask better questions. Define meaningful actions. Track what matters. Then build with clarity, not assumptions.
The result? Less waste. Better UX. And a product that quietly improves with every click.
Because when you listen to your users—not just what they say, but what they do—you stop building in the dark.
And if you want to provide that value to your users and their customers, an analytics dashboard is an excellent place to get started. Get in touch with us today and you can build your first one for free - it’s on us.
Build your first embedded data product now. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
