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By Pavel Kukhnavets

Product Analytics

Applying product analytics to understand how customers actually use the product.

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Product teams always want to know exactly who their users are, what they want, and how to keep them. They simply use analytics data to be informed and to make the right decisions.

This guide is a comprehensive course into the world of product analytics. It will explain why analytics are so important in product management, what professional platforms can be helpful, and what possible pitfalls you may face working with analytics.

What are product analytics?

What is Product Analytics?

Product analytics is a set of powerful tools that allow a product manager and the whole team to assess the performance of the solutions they build. In simple words, these tools provide product teams with critical information and valuable insights.

We need product analytics to get important information for optimizing performance, diagnosing problems, and correlating clients’ activity with long-term values. Effective analytics tells us what is happening in our product and shares valuable info about our users’ behavior.

Product teams use analytics to see where they experience friction, how to diagnose exact problems, how to reduce churn and personalize interaction for users, and so on. This is about scaling users’ empathy and it is reached by understanding how they interact with the product.

Important questions that product analytics cover

Do you know a company that has no questions about its clients and the ways its products are performing? Product manager analytics utilizes data to give certain answers to the following questions:

  • Could you define the most valuable clients for our company? What actions do they take?
  • What clients churn and why?
  • Do your users have pain points? What are they?
  • What are the characteristics of highly engaged users?

Effective product management analytics will help you to test your assumptions and check if your intuition has led your path.

With the answers to these questions, you will be able to improve your product in different areas. That will definitely lead to accelerated retention and lifetime value.

The importance of analytics

Why are product analytics important? There is no absolutely simple product. Most of them require numerous decisions when building.

Luckily, today data science and analytics provide smart and accurate ways to decision-making, although we know that millions of successful products have certainly been created with only experience and instinct.

Good analytics is a godsend for any product team. There is no need to guess or rely on customer interviews when making decisions. With the help of product analytics, you can see how well you are meeting user needs in real time.

It is possible to measure the success of individual features. Used properly, analytics can transform your ability to generate ideas and design user experiences.

Product analytics tools

What can be done with product analytics?

Product management analytics can be implemented in many cases.

For any product team, it is crucial for measuring and improving key product metrics, such as AARRR (Pirate Metrics) where:

  • Acquisition is about understanding where your clients come from.
  • Activation is about the experiences that a user has with the product on the journey to becoming a paying customer.
  • Retention is about understanding what clients are staying or leaving.
  • Referral is about knowing whether your purchasers are talking up your product and if they post on social media.
  • Revenue is about how you make money with your product.

Product Analytics vs Marketing Analytics

What are the differences between these two kinds of analytics? Many people still think that they are almost the same thing. However, the difference is crucial. Here are the core distinctions between those types of analytics:

1. Events are a key data source in the product analytics

We know that web analytics defines which user actions are considered a conversion. It can be registering for a webinar or filling in a contact form. The same thing is with the marketing funnel.

Product analytics looks more complicated. Every single app has its own structure and specific features. Therefore, setting goals and funnels look less obvious. To define your product goals, you first need to turn them into user actions, and then connect with certain events. It means that you need to have enough time for the proper event configuration.

A good tracking plan is what will assist you to succeed. It will contain all the events and properties you want to track. This will help you to avoid critical errors and connect events with your business goals.

2. The mature stages of the customer journey are on the focus of product analytics

Marketing or web analytics usually focus on acquisition and conversion that definitely helps managers and teams to convert visitors into subscribers or paying clients.

Product analytics is more focused on retention and engagement. It provides you with the data that will help you to get the answers to the questions about user activation, user referrals, user engagement, and user retention.

3. Product analytics involves various goals and metrics

In most cases, the goals and metrics are directly related to the product design objectives.

You may use special frameworks for defining product analytics. They contain different kinds of metrics. Here’re some examples of such metrics:

  • Engagement – to measure the frequency, depth, and intensity of user interactions with a product.
  • Happiness – to measure attitude or satisfaction.
  • Adoption – to measure how easily and quickly users adapt to new product features.
  • Retention – to track how many existing users retain in a certain period.
  • Task success – to evaluate the effectiveness of the tasks users complete within the app.

4. Product analytics is about operating on more sensitive data

You should also admit the difference between the types of data the tools collect and the responsibilities entailed.

Product analytics assumes that a huge part of what you strive to measure takes place in secure member areas in your product (while web analytics analyzes traffic on marketing sites and takes advantage of publicly accessible information).

Those areas of the product are filled with personal and often sensitive data about the users, including:

  • names and surnames
  • telephone numbers
  • home addresses
  • medical records
  • credit scores, etc.

If you want to analyze this info, you must be sure that your software solution provides the highest level of security.

Who uses product analytics?

Analytics in product management

Anyone who wants to make better decisions needs product analytics. Effective analytics can provide answers to inquiries from stakeholders everywhere in your organization. Who exactly benefits the most from product analytics?

  • Product managers can understand what their users do, make proper decisions, run experiments, boost activation, conversion, and retention.
  • Developers can fine-tune features, resolve user frictions, and eliminate bugs without deploying additional efforts.
  • Marketers can track not only which marketing channels bring in the most visitors, but which channels bring in the visitors most likely to convert, and most likely to exhibit long-term retention.
  • Growth managers can get a complete view of user engagement to optimize retention strategies.
  • UX designers can see how users navigate feature sets and identify roadblocks.

Actually, your customers will benefit the most, as the thoughtful application of product analytics will result in a product that will be easy to implement, intuitive, and cozy.

Getting Started With Building the Right Foundation

In order to benefit from product analytics, you should start thinking about certain foundational elements that will make your life easier in the long term.

Connect business goals to data

Product analytics is aimed to help you grow the business. It is about increasing users, revenue, referrals, or anything else that really matters. Many companies have problems with these indicators and to avoid those problems, they should try to understand what parts of the business could be improved with more data.

Perhaps your user retention is too low or the onboarding funnel has a poor conversion rate. This is where data may help to figure this out.

Prepare a tracking plan before writing code

Most platforms fall into a category called “event-driven” tools. For example, Intercom, Mixpanel, or Amplitude. They rely basically on events to collect data.

Your tracking plan can be performed in an Excel or Google Spreadsheet and it will contain all the events and properties that you would like to track.

Product analytics tools: how do they work?

A good analytics platform will provide you with the following benefits:

  • You will get tracking automatically to user actions across your sites and apps.
  • Segmentation will let you know who your users are, where they came from, and when.
  • Dashboards will let you visualize data in revealing ways.
  • Profiles will allow establishing user categories around criteria of your choice.
  • Notifications will permit you to alert product teams and communicate with users.
  • You’ll be able to set funnels to explore different paths to conversions.
  • Measurement tools will allow you to evaluate each feature’s user engagement.

Product analytics platforms work by tracking the actions users take on sites (page views, clicks, swipes, form fills, etc). Any good product analytics tool should have the following features:

  • Automatic data capture to get the most use out of any analytics tool.
  • Cohort analysis to show how many users are returning within specific time periods.
  • Behavioral segmentation to get greater refinement than demographic segmentation. It will let you sort users according to the actions they take in your site.
  • Virtual events to ensure endless flexibility in taking advantage of all the data you’ve gathered through auto-capture.
  • Data governance to keep your information clean and safe.

How to Use Product Analytics: Simple Tips

Product teams should implement analytics only after their products have reached some minimum number of customers/users.

If the user base is still small, the data you derive from product analytics will not be a large enough sample to give your company good guidance on what to do with the product. That’s why until your product reaches that type of benchmark, you can use qualitative product feedback such as interviews and surveys.

If your product has enough user base, collect and analyze quantitative metrics to help make your product even better. The best way to get these metrics is to use product analytics.

1. Connect your data to business goals

It is better to first outline specific business objectives for the data you plan to collect. This will help you to avoid resources and time wasted on gathering data your business won’t be able to put to productive use.

2. Prepare a tracking plan

Product analytics data is usually broken down into events that describe actions users take with your product. It can be access to a feature, sending a message, or opening a new screen. The tracking plan can be created with the help of a simple spreadsheet.

3. Choose the appropriate analytics software

Do not be lazy to research all available product analytics tools on the market. Since no platform performs all of the tasks, you’ll likely need to sign up for a couple of these tools to implement your unique product analytics strategy.

Some bad examples to avoid

  • This is a bad practice when you get to the end of an experiment and realize you don’t have all the events you need. Try to run analysis before you start the experiment using some dummy data. This will help you to quickly understand the gaps in what you’re capturing.
  • Generating a hypothesis can be time-consuming. However, you have to make sure you have one. You should be confident you can prove/disprove it with the analytics you have before you launch.
  • Make sure you’re testing on enough users and for a long enough period of time. Your analytical results must be statistically significant.
  • Throw away bad ideas. Your aim is to test features as cheaply as possible. So, failing fast is ok.

How to use product analytics

It is not simple to have a wealth of data at your disposal. When you decide what to track and measure, keep in mind the following questions:

  • What data sources do you use?
  • Are you collecting qualitative or quantitative data?
  • Have you discussed with all stakeholders what is most important to your business? Have you incorporated that into your product strategy?
  • Are you measuring this because it looks good or because you can actually use it to grow and improve?

Make sure that you’re constantly asking yourself if the info you’re reviewing is still applicable. Since you feel what is important to your business, you will be able to refine the data and get the most valuable information.


Knowing how your clients interact with your product, the ability to discover aha moments and your clients’ behaviors, and integrating with marketing data and business intelligence makes product analytics rather compelling.

We hope that this Hygger guide has given you a clear overview of the most essential characteristics of product analytics and the threats involved in dealing with your users’ sensitive data.