Product analytics, as it’s typically used in SaaS, is fundamentally broken.

• Many companies rely on inefficient, fragmented tools that create data silos and errors.
• There’s an abundance of low-quality data, redundant tracking, and metrics that don’t align with business objectives.
• Product teams only get superficial insights and no clear path to action.

So how do you fix product analytics and turn it into a tool that genuinely drives growth?

It comes down to two critical things:

1. Tracking product metrics that push business goals.
2. Focusing on actionable data that connects insights to decisions (while scrapping everything else).

That said, let’s rethink product analytics for a second:

What product analytics is not

To be honest, I don’t think there’s a misunderstanding of what product analytics is—I think it’s just poorly implemented.

Many companies limit their product analytics processes to just:

• Tracking a bunch of metrics related to user behavior or product performance.
• Using product analytics tools to collect data and feedback.
• Trying to swing a strategy based on whatever superficial insight they can get from what’s essentially irrelevant information (and then call it a “data-driven strategy”).

Aside from the last point, all of these are legitimately part of product analytics, it’s just that the approach is flawed. And what’s worse, most courses or online guides only focus on these vague approaches.

Instead, metrics need a purpose to be valuable for your business. Collecting data or feedback must be done strategically so it can truly provide clarity instead of confusion.

Plus, there’s a missing ingredient in this mix that’s often overlooked or taken for granted (hint: you already know what it is).

How you should approach product analytics

If product analytics isn’t just tracking metrics and collecting data, then what else?

Strategic action.

Sounds unsexy and obvious, but it’s uncommon for companies to build an environment where data can be easily translated into action.

This is why, oftentimes, product analysts are always cleaning up the data, and why product decisions often involve many stakeholders.

Now, instead of complying with flawed practices, here’s how you can approach product analytics in a way that leads to strategic action (even if you’re not the Chief Product Officer of your company):

Connect it with the business goals

It’s easy to misunderstand what it means to align metrics with business goals.

It doesn’t mean to track business KPIs like MRR, retention, or customer loyalty. Rather, it’s about taking your main business KPI and dissecting it into specific metrics that affect it (which you can target to improve).

So if your business is focusing on increasing MRR, you should come up with specific metrics that affect MRR such as free-to-paid sales, expansion revenue, and upgrades.

Why? Because tying metrics to your product gives more nuance on why a user behaves in a certain way, making it more actionable. Plus, it’s easier for a product manager to try to increase upgrades than MRR because they’re essentially more sensitive metrics that can lead to an increase/decrease in the main business KPIs.

An example of this is how Duolingo created a whole Markov model around their main metric (DAU) to identify smaller metrics that drive it indirectly. This way, they could make a simulation to determine which specific metrics have the most impact on DAUs, focus on improving it, and quadruple their DAUs number in four years.

Question if it’s actionable

Too much data leads to inaction.

Think about it, how many impactful decisions have you made based on data?

When you’re trying to pay attention to feature adoption, churn, retention rates, user engagement, MRR, DAU/MAUs, and conversion rates—all at the same time—it’s no wonder product analytics will impair decision-making instead of improving it.

For this, Kevin O’Sullivan—our Head of Product Design—suggests to:

• Avoid using too many data management platforms to measure the same metrics. Keep everything in one place if possible.
• Focus on making small improvements to the user experience. E.g. Reducing steps in the “Aha!” moment.
• Experiment with “soft solutions” to encourage adoption. E.g., Implementing interactive walkthroughs to make the onboarding process frictionless.
• Mix qualitative feedback to avoid getting too immersed in quantitative data. E.g., Watching session replays, customer interviews, etc.

All in all, Kevin argues doing this would eventually lead to increased revenue as you start tracing what metrics are more impactful, what leads to churn, and what behaviors represent an upselling opportunity.

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How you can improve product analytics

Now, focusing on strategic actions is not easy in your typical SaaS environment.

Here are some tips you should consider when making changes to your product analytics approach:

Learn to work with bad and low-quality data

Different from other types of analytics, product analytics is subject to a lot of inaccurate data (from users that might be just bots, to event properties that just don’t register).

That’s why it is a day-to-day task of product analysts to figure out ways to improve data quality so your insights can match reality.

Think of:

• Checking your DAU/MAU ratio (it will rarely be more than 50%).
• Cross-verifying if the session starts, logins, app opens, and screen views have similar numbers (a mismatch means there could be a tracking issue).
• Making sure your DAU metric is used to calculate Retention instead of other activity metrics.

But, a more impactful decision is to use a product analytics solution that systematically avoids bad-quality data. At Userpilot, for example, we recently updated the segmentation feature so any old, corrupted, or invalid segments will now be flagged in the UI (without affecting segment results or evaluation).

Take data ownership to improve data practice

Lack of proper data governance leads to chaos, no matter how compelling the idea of “democratizing data access” in your company sounds.

Let’s say your company is rolling out a new feature, and you’re tracking adoption rates. Without data ownership:

• Data engineers might build event logs but fail to validate them for completeness.
• Analysts could generate conflicting reports based on duplicate or inconsistent metrics.
• Product managers might waste time trying to resolve discrepancies.

So if you want to give employees the capacity to access and extract value from data, this must come with guardrails that make sure the data is properly managed.

To do this:

1. Assign specific roles for managing datasets. Ensuring every table, event, or metric has a clear owner who oversees accuracy, lineage, and updates to the data they are responsible for.
2. Set up data instrumentation protocols. Determine how data is collected, stored, and updated. Also, map out where datasets come from, what transformations they undergo, and how they’re ultimately used.
3. Create centralized documentation. Include data sources, definitions, and responsible owners. The goal is for teams to know where to find data, who to contact for clarifications, and how metrics are calculated.

Build and manage product analytics data within a context

Collecting data is only the first step, understanding the context in which it was generated is the name of the game.

Without context, data points are just isolated facts that can lead to misinterpretation and misguided decisions. For instance, knowing that a user clicked a button has no value without understanding the surrounding circumstances (such as the user’s journey leading up to the click and the intended outcome).

So if you want to make your product analytics more actionable, you need to:

• Understand the product’s mechanics. How are events generated and where are they stored? What properties do they have?
• Know how users interact with your product. How do they behave? Understand how each type of user acts, and why (use segmentation to get more nuanced context).
• Understand what constitutes a feature. What’s the series of activities you must perform to use a feature successfully? Which activity brings the most value to the user?

A great advantage of this is to use a product analytics tool with auto-capture events. Why? Because you’ll always have any type of event ready to pull whenever you need it. So if you suddenly realize that a core feature is driving retention, then you can quickly analyze what’s happening without having to wait weeks.

Focus on data user experience

A bottleneck that might limit your ability to find actionable insights is the lack of efficient data user experience.

Think of the tedious process you’d have to go through to figure out the feature adoption rate of users from Oceania when you have hundreds of event definitions and zero documentation.

If data feels tedious or inaccessible, it will lead to missed opportunities for decision-making.

This is what the data user experience involves, and improving it requires you to approach each of its components:

• Usefulness: Is the data pretty much useless? Prioritize datasets and dashboards that align with business-critical questions, ensuring they directly address common use cases.
• Usability: Are dashboards intuitive? Do teams struggle to run common queries? Establish naming conventions and an SQL style guide to standardize queries.
• Findability: Is the data hard to find? Centralize documentation for metrics, definitions, and data sources to make them easily accessible.
• Credibility: Is data accuracy questioned? Ensure lineage and definitions are documented to build trust.
• Accessibility: Can teams find and use the data they need without excessive delays? Streamline access protocols.

In the end, know that raw data means nothing without insights. Any product analyst must understand that people only engage with data when it’s immediately clear, usable, and relevant to their specific needs.

So make data presentable and compelling. Build engaging dashboards, translate business KPIs to product metrics that move the needle, learn how to communicate insights when you find them, and so on!

This way, you’ll be able to translate data into a clearer narrative that resonates with stakeholders—improving both decision-making and buy-in.

Conclusion

As we covered, effective product analytics goes beyond collecting metrics and tracking user behavior.

By addressing challenges like low-quality data, fragmented tools, and poor accessibility, your team can transform analytics into a driver of growth (as it’s meant to be).

Want to see how Userpilot can help you implement product analytics? Book a demo today to see how our platform can collect high-quality data without any code!

Product analytics FAQs

What is the concept of product analytics?

Product analytics involves tracking, analyzing, and interpreting user behavior to understand how users interact with a product.

What does a product analysis do?

Product analysis collects and interprets data to improve product performance and user experience. It identifies key metrics, analyzes trends, and collaborates with teams to make data-driven recommendations that align with business goals.

What is the difference between product analytics and data analysis?

Product analytics is a subset of data analytics that’s meant to improve products.

The former focuses on user interactions and product-specific metrics (e.g., feature adoption or activation). Whereas data analysis is broader, encompassing various business areas like marketing, operations, or finance (think of Google Analytics).

How to break into product analytics?

To start a career in product analytics, focus on building skills like:

• Data visualization
• SQL
• Python
• Business strategy
• Statistics
• Data acquisition and collection
• Control over product analytics software like Amplitude or Mixpanel

Also, as self-service tools become the future, learning how they work and what difference they make will give you an advantage. Think of how teams will access data, and what obstacles they’ll meet (e.g., wasting time requesting data that doesn’t solve their problems), and understand how you can build a self-service data ecosystem that keeps data integrity in check.

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