Product analytics: how to improve digital products with data

Product analytics shows how people actually use a product: where they get stuck, what they ignore and when they decide to leave.

In this article, we’ll look at what data to collect, which metrics matter and how to turn numbers into specific product improvements.

What is product analytics?

Product analytics is the process of collecting and analysing data about how people behave inside a product: what they click, where they get stuck, which features they use and what makes them come back — or leave.

Marketing analytics tells you where users came from. Product analytics tells you what happens next.

It helps teams:

• spot weak points — for example, understand why 40% of users abandon a form when asked for their phone number;
• see whether features are actually being used;
• improve the user journey;
• increase conversion, retention, LTV and other key metrics.

Instead of guessing what needs to change, the team can spot a problem in the data, form a hypothesis and test it in the product.

What data should you collect?

The exact set depends on the product and its stage, but there are a few types of data that are useful almost everywhere:

1. User actions. Clicks, navigation, form completion and feature usage.
2. Funnels and user flows. How users move from registration and onboarding to the target action — and where they drop off.
3. Entry and exit points. Where users come from and when they leave the product.
4. In-product behaviour. Which screens they visit, how long they spend in the product, how often they come back and which features they use.

But data alone doesn’t explain why something is happening. User behaviour analytics helps you see what is happening inside the product and spot recurring patterns. To understand why users behave that way and what to do about it, the team still needs context, metrics and hypotheses.

Which product metrics should you track?

Metrics help you understand how well the product is doing its job. Some of the main ones are:

• conversion — how many users complete a target action, such as registering, booking or paying;
• DAU / WAU / MAU — daily, weekly and monthly active users;
• retention — whether users come back;
• activation rate — how many new users reach their first valuable action;
• churn — how many users stop using the product.

Depending on the product, you may also want to track time-to-value — how quickly users experience value for the first time, engagement — how actively they use the product, and business metrics such as ARPU (average revenue per user) and LTV (lifetime value).

Together, these metrics answer a simple question: how well is the product actually working?

You don’t need to track everything at once. Different stages call for different metrics. Early on, the priority is to understand whether the core idea works. As the product grows, the focus shifts towards retention, efficiency and unit economics.

How analytics helps improve a product

Metrics can sit in a report, or they can help shape the product. The difference is in what the team does with them.

Here’s where product analytics is especially useful:

Spot a broken user flow. If half of users abandon registration, the next step is to find out exactly where they drop off and why. We saw a similar case in the Emex project.

Improve the interface. Data can show that users are missing a button, getting confused between steps or taking unnecessary actions.

Decide what to do with features. Instead of arguing about whether a new feature is needed, the team can look at how people use existing ones and what they seem to be missing.

Improve key metrics. Remove an unnecessary step from registration and check whether conversion goes up. Change onboarding and see whether activation improves.

The important thing is not to stop at the report. If the team finds a problem but changes nothing, analytics isn’t helping the product improve. It’s just another document or dashboard that people will eventually stop opening.

Where product analytics usually goes wrong

The problem is rarely the data itself. More often, it’s how the team works with it.

Tracking everything. The team records every click but misses the key event: did the user actually get value? There are plenty of numbers, but no answer to the most important question.

Looking at numbers without context. Time spent in the product has gone up. That might mean users are more engaged. Or it might mean they’re simply taking longer to find what they need.

We saw something similar in one e-commerce project. The online store was loading slowly, and the team initially blamed scraping and attacks. The real issue turned out to be messy legacy code.

Not acting on the findings. Analytics shows that users aren’t reaching a key step, but the team keeps shipping new features instead of fixing the flow.

Defining metrics differently. Marketing talks about registrations, while the product team looks at activated users. Everyone ends up looking at different numbers and drawing different conclusions.

Not forming hypotheses. Watching charts isn’t enough. The team needs to ask: why did this metric change, and what can we test?

A practical loop looks like this: data → hypothesis → test → product change → new data.

When should you introduce product analytics?

Ideally, during MVP design, so analytics is built into both the product and the decision-making process from the start. What you track will then evolve along with the product.

Early on, the team needs to define the key events: registration, core steps and the moment when users first get value. Even during testing, analytics can reveal where people get confused, so problems can be fixed while changes are still relatively cheap.

After launch, you start seeing real user behaviour — and it almost always differs from what the team expected. Analytics helps reveal bottlenecks: where conversion drops, which features users overlook and where they leave.

As the product scales, basic metrics are no longer enough. Segmentation, LTV, ARPU, long-term retention and depth of usage become more important. At this stage, analytics helps manage both growth and product economics.