What Is Product Analytics? The Complete Guide

In today’s fast-moving digital landscape, you’re under pressure to continually improve your product. But how do you learn what users need most, or where your product’s going wrong? Product analytics give you the insights you need to make decisions without guesswork.

Product Analytics

A comprehensive guide to product analytics

Key takeaways

When you’re trying to evaluate and improve your product, there are a mountain of questions you need to answer.

Where do your users get lost? Why do some stick around while others leave? What features do users find most helpful—and which ones are most closely tied to revenue generation?

For product teams, solving these mysteries can mean the difference between sink and swim. Thankfully, product analytics lets you use data to answer these difficult questions so you can make better product decisions.

In this article, we’ll explore:

  • What product analytics does

  • How it can help you solve problems and unlock growth

  • How to choose a product analytics solution

  • What the limitations of standalone product analytics tools are

Let’s get started!

What is product analytics?

Product analytics is the process of collecting, analyzing and interpreting data about how users interact with your product. This data can be used to improve the product by identifying areas where users are struggling, finding ways to make the product more user-friendly and tracking the effectiveness of changes that have been made.

It captures customer journeys in your mobile app or website and uses the data to show you:

  • Who’s using your product

  • What they’re doing in it

  • How long they engage with it

  • Which features they use

  • When they perform key actions (like converting or unsubscribing)

📊Product analytics tools help you harness this data to improve your app without relying on guesswork. The resulting insights give you the keys to optimize user journeys, maximize conversions and put the user at the heart of the business.

Access to product data is critical, especially because according to research, data driven businesses are:

  • 23 times more likely to attract customers

  • 6 times more likely to retain customers

  • 19 times more likely to be profitable

Go beyond surface-level insights with Glassbox

Glassbox helps you identify hidden trends in what users are doing, then discover the why behind their behavior.

Discover Glassbox

How do product analytics tools work?

Product analytics tools log details about every individual journey users make in your product. They capture which features they used, how long they stayed, where they clicked and more.

They marry this information with account-based data about the user themselves, such as their subscription type or sign-up date.

The tools then allow you to analyze this information in helpful ways:

  • Segment user groups to understand the differences between them. For example, your analytics tool could tell you how often (on average) trial users engage with an app compared to paid users.

  • Track growth metrics. Analytics tools can show you how many daily active users you have, or how often they engage with a new feature

  • Identify user behavior trends. For example, identify the activities users tend to do before making a purchase or upgrading their plan.

Product analytics tools also let you monitor the technical performance of your product, such as its page load speed or error rates.

🔍With these insights, you can uncover new opportunities to improve your app and website—and encourage customers to spend more with you.

Why is product analytics important?

Today’s business landscape is challenging for product-based businesses. Consumers and businesses have high expectations for digital products—and if you don’t tick the right boxes, a competitor is ready to take your place.

So when there are a million potential changes you could make to your product, how do you make the right decisions?

Product analytics remove the guesswork by showing you:

  • Where issues are occurring

  • Which parts of the product deliver most the (or the least) value to customers

  • How your key success metrics (like revenue and retention) are changing

📈Often these insights are exactly what you need to stay competitive and resolve issues before they get out of control.

5 ways product managers use product analytics to make better decisions

1. See where users are coming from. Product analytics tools show you where new users are coming from and which channels are most profitable.

2. Identify user struggles. Journey Analytics lets you see where users are dropping off—so you can use session replay to investigate their journeys and see what’s going wrong.

3. Run experiments. A/B test different page variants and check your analytics data to see how users respond.

4. Determine the success of individual features. Learn how much value specific features deliver by monitoring user engagement with them.

5. Discover which users are the best prospects. Segment users to find out which groups spend most, stay longest and generate the most referrals.

Who uses product analytics?

💡The answer is simple: anyone who needs to make well informed decisions! Product analytics data is helpful for a wide range of teams and roles that are focused on creating customer-centric products.

Teams using product analytics

Multiple departments need to know what’s happening in their product, and product analytics tools can help them collaborate effectively. Remember, data is not owned by one team. Teams starting at the leadership level all the way down can benefit from these rich insights.

Here are common teams that can benefit from product analytics:

  • Leadership can use analytics insights to make data-driven decisions about the product’s overall direction.

  • Product teams can discover how customers use the product and use this knowledge to plan the product roadmap.

  • Marketing teams can learn more about customers and refine which audiences they target and with what message.

  • Engineering/DevOps teams can identify issues, bugs and performance problems.

  • Customer success teams can see what key accounts are doing in the product and reach out to them when needed.

  • UX/design teams can learn how users navigate the product and find ways to improve their experiences.

Roles that benefit from product analytics

Product analytics are useful to anyone who needs to understand customers relationships with their product, such as:

  • Product managers

  • Analysts

  • Marketers

  • Growth managers

  • Engineers/DevOps

  • Data scientists

  • Customer success managers

  • UX/UI designers and researchers

  • CEOs and leadership teams

  • Sales leaders

Everyone within the organization needs to collaborate to identify and share any findings that impact the customer journey. No matter what you’re trying to do–like understand customer behavior, identify opportunities or form a hypothesis–your answer can always be found within your product analytics data.

Industries using product analytics

Any industry with a digital platform can benefit from product analytics to better understand the customer experience, such as:

  • Retail & e-commerce

  • Fintech

  • Consumer tech

  • Entertainment

  • Travel & hospitality

  • Insurance

  • Telecom

  • Software as a Service (SaaS)

Some of the ways product analytics are used by industries are:

  • Conducting A/B tests for new product features to help make data-driven decisions

  • Identifying highly engaged customers and using these insights to improve retention

  • Understanding the customer journey on web or mobile apps, from the landing page to checkout (or if they dropped off somewhere in between)

💡Expert tip: Standalone product analytics tools inform you how your customers engage with your product, but they can’t provide the context that tells you why. A digital experience intelligence platform provides rich insights that give you a complete picture of what your customers are doing and the critical data to uncover why they’re behaving that way. We talk about the limitations of standalone product analytics tools a little later in the guide, so keep scrolling!

The types of products that benefit from using product analytics

Product analytics can measure the performance of several digital products or channels. This is important since the customer journey takes place across multiple channels. For example, a customer may start on the web and switch to mobile to complete their transaction. Customer interactions are different depending on the channel, so it’s important for you to understand how customers engage on each digital channel so you can optimize it for success.

Product analytics is beneficial in software, or in other technologies that operate using software, such as:

  • Web apps

  • Mobile apps

  • IoT products

  • Smart devices

Product analytics metrics: Uncover your most important trends

There are dozens of product analytics metrics that can help you learn more about your users and their journeys. For example:

  • Daily active users (DAU) measures the total amount of people who actively use your product each day.

  • Acquisition rate measures the amount of visitors who start engaging with your app (before becoming paid customers).

  • Activation rate is the number of users who convert to paying customers (for example, by upgrading from a free trial account to a paid subscription).

  • Churn rate is a measure of how many customers leave (either by unsubscribing, ceasing to make purchases or ceasing to use the product).

  • Retention rate is the opposite of churn rate and is a measure of how many customers stay subscribed or make regular purchases.

  • Revenue in the form of subscriptions or in-app purchases.

📏Some of these metrics can be measured in different ways—for example, an active user could be someone who logs in to your app, or someone who engages with a core feature. Ideally, your product analytics tool should let you choose how you define these metrics.

Want to learn more about product metrics? Check out our guide to essential metrics for digital products.

Product analytics examples: What can you do with product analytics?

Trend analysis

Trend analysis identifies how patterns in user behavior change over time. For example, it can track user engagement to see if your number of daily active users is decreasing, or if users are spending more time using a specific feature.

Trend analysis also helps you measure if changes to your app affect engagement, conversion or retention rates.

Attribution analysis

Attribution analysis reverse-engineers successful customer journeys—e.g. ones where a conversion was involved—to learn which touchpoints should get credit for the conversion.

For example, attribution analysis can help you determine which marketing channels or which product features are generating the most conversions.

Journey analysis

Journey analysis involves visually mapping out the journeys users take within your product, both before, during and after a purchase.

This allows your business to further investigate users’ sentiments, needs and pain points at each step and make improvements to the customer journey.

Cohort analysis

Cohort analysis is where you categorize your users into different groups—also known as segments or cohorts—based on shared characteristics. This allows you to compare different groups and look for trends in their behavior.

Depending on what information you gather, you can create cohorts based on:

  • Account type (e.g. free account, paid account)

  • Behavior (e.g. users who logged in in the last 30 days)

  • Demographics (e.g users who are based in Europe)

Churn analysis

Curn analysis investigates trends in users leaving your app. It helps you determine why people are leaving by looking at:

  • Which cohorts churn more

  • What activities (or lack of activities) precede users churning

  • Which users' journeys typically lead to users churning

By learning more about when and why users churn, you can develop new strategies for keeping them.

Retention analysis

Retention analysis measures how many users return to your product after their initial visit or account creation.

Performing this analysis can help you discover how “sticky” your product is with users—in other words, how well it meets an important need they have.

Funnel analysis

Funnel analysis involves mapping the journeys that customers take through your funnel and analyzing them for friction. By identifying where customers struggle or drop off, you can find ways to increase conversions and improve the customer experience.

Airline optimizes online booking performance with Glassbox

See how an airline used Glassbox’s funnel analysis to fix an error affecting 50% of its customers.

Read the case study

Conversion analysis

Conversion analysis examines behavior around key conversions you want users to complete—like making an e-commerce purchase or starting a software trial.

This is similar to funnel analysis, in that it examines where users are dropping off. However, conversion analysis focuses on understanding why users convert (or not) from a specific page.

Conversion analysis can involve examining how users react to different page elements that contribute to a conversion, such as page layout or button copy, and often involves A/B testing and interaction maps or heatmaps.

Customer experience analysis

Customer experience analysis means analyzing each touchpoint of the customer journey to understand the customer’s sentiments and perceptions. This should cover every interaction a customer has with your brand, both inside and outside your product.

Collecting user feedback through voice of customer (VoC) tools is vital for analyzing the customer experience. For instance, with in-app surveys, you could gather quantitative feedback like Net Promoter Scores® (NPS) from customers. By analyzing how NPS changes across touchpoints, you can find the weakest parts of the customer experience.

Milestone analysis

looks at key activities that users do on the way to becoming high-value customers. By reverse engineering what high-value users do, you can create strategies to encourage other users to do the same.

For example, you might find that trial users who interact with a core feature three times go on to purchase paid subscriptions. Accordingly, you try to direct more users to that feature.

Important milestones are sometimes referred to as the “a-ha” moment. However, milestone analysis can also involve identifying milestones on the way to a negative outcome, like customers churning.

Product analytics tools and software

There are dozens of product analytics tools out there, so which one is right for you? Read on to get up to speed.

Types of product analytics software

  • User-based analytics tools track how individual users behave over repeated interactions with a product. This allows you to segment users, identify group trends and find opportunities for personalization.

  • Session-based analytics tools are similar to user-based analytics tools, but they focus on how users behave in a single session. For example, session replay lets you rewatch a user’s journey in real time, helping to uncover areas where they encountered problems.

  • Event-based analytics tools monitor specific activities such as clicks, views and feature interactions. By analyzing these “events,” the tools can reveal how user activity changes between groups, over time, or across different parts of the product.

  • Cohort analytics tools help you learn more about specific user groups (or “cohorts”) based on their behavior or attributes. They enable you to look for trends in cohort behavior and use them to inform product decision-making.

  • Real-time analytics tools provide “live” visibility over their app’s performance and how users are interacting with it. For example, they can alert you to a spike in app errors, or to an influx of users engaging with a new feature.

  • Funnel analytics tools track how users behave across multi-step sales or marketing funnels. They reveal opportunities to improve the weakest part of a funnel, helping companies to increase their sales or lead generation results.

How to choose the right solution in 7 steps

Product analytics tools can be costly and complex to deploy, so it’s worth taking the time to choose a solution carefully.🤔

1. Start by defining your goals. Your overall objectives will affect the metrics you need to record and analyze. For example, if your goal is to maximize conversions and sales, you might want a tool with sophisticated funnel analytics features.

2. Research features and capabilities. Some platforms focus more on event tracking, while some provide features like session replay, error reporting and A/B testing.

3. Check what integrations are available. Many product analytics platforms offer ready-made integrations with CRMs, data warehouses and other analytics tools. Look for a platform that can sync with your tech stack without extensive development work.

Glassbox, for example, integrates with a wide range of analytics, testing, and application performance management tools.

4. Find out how much data the tool captures. Some platforms automatically capture all data from user journeys. This means if you decide to analyze a new set of events or user behaviors, the tool can run an instant analysis using historical data.

In contrast, some platforms require you to manually “tag” events and gather data before running a new analysis.

5. Consider pricing. Some product analytics tools offer a basic plan for free, while premium products and subscriptions could cost you thousands of dollars. Check to see which products best align with your needs and budget.

6. Investigate the support offered. Product analytics tools can be complex, and in many cases, they will have sophisticated setup requirements. It’s worth investigating what level of support, training and documentation is included in the service.

7. Read reviews. Most well-established product analytics platforms will have reviews you can read online (🔥humble brag time: Glassbox has a 4.8/5 rating on G2).

Product analytics implementation process

Follow these steps to get product analytics working in your business:

1. Set up tracking. Integrate the tool with your product data to start tracking user behavior and product performance. This may involve importing data from a data warehouse, using a software development kit (SDK) or connecting to the tool’s API.

2. Define your events. In this context, “events” means key user behaviors—like a user starting a free trial, logging into the product or using a core feature.

Many businesses will focus on a “North Star Metric,” which is the event most closely tied to growth, such as paid subscription trial sign-ups. However, it usually makes sense to track earlier events in the customer journey also.

The key point is that you shouldn’t just try to track everything—nor should you focus on “vanity metrics” that make you look good. Instead, choose metrics that give you genuine insights into your progress toward business objectives.

3. Run reports. Most analytics tools will create reports and visualizations of your key metrics—like charts showing your daily active users over the last three months. Set up reports that show your progress toward your objectives and help you identify trends.

4. Act on insights. With reporting set up, your team can begin reviewing data and make improvements to the product based on your insights. Continue monitoring key metrics to measure the impact of your improvements and develop a positive feedback loop.

Top product analytics tools

There are a lot of product analytics tools out there, so we’ve compiled a list of our top seven. Let’s take a deeper look at each one to help you decide what’s right for you.


Glassbox is a digital experience intelligence platform that analyzes the entire user journey. Combining real-time event tracking with analytics tools, it helps you build customer-centric products, fix technical issues and action customer feedback.

The platform features tagless data capture that logs 100% of event information in the product. This allows you to answer any question and test any hypothesis without manual event tagging.

Unlike standalone product analytics tools, digital experience intelligence platforms provide advanced insights that give you a complete picture of what your customers are doing and why they’re behaving that way.


Heap is a digital insights platform that helps you understand how and why customers engage with your product. It tracks events in your app and allows you to analyze data via reports, visualizations and dashboards.

The platform also has data science capabilities that automatically search your data logs to find hidden trends and identify points of friction.

Google Analytics

Google Analytics is a free web analytics tool that lets you monitor and analyze traffic coming to your website. Originally created to help companies manage their ad spend, the latest version of Google Analytics, GA4, also offers app analytics capabilities.

GA4 functions similarly to product analytics platforms, as it primarily works by tracking events. However, its primary purpose is to help optimize marketing campaigns by gathering data on how users access a website or app. Accordingly, it lacks some of the segmenting and behavior analysis capabilities you’ll find in product analytics tools.


Smartlook is a product analytics platform for apps and websites. It combines quantitative data, like events and funnel analysis, with visual analytics tools like session replay and heatmaps.

This combination allows companies using Smartlook to get a better understanding of user behavior trends. For instance, Smartlook helps you pinpoint drop-off points in your funnel, then view relevant session replays to see what users experienced before dropping off.

Adobe Product Analytics

Adobe Product Analytics is a platform designed to help product teams get a complete view of user journeys and experiences. It unites data that was traditionally siloed in different departments, thus helping product, marketing and CX teams collaborate effectively.

The platform offers guided analysis features that analyze data to reveal insights about user behavior, points of friction, cohort characteristics and more. It also integrates with other Adobe products, such as Journey Optimizer, to provide an omnichannel view of customer journeys.

Want to get more out of Adobe Analytics data? Find out how Glassbox integrates with Adobe to give you more clarity on your analytics and A/B testing data.

Limitations of standalone product analytics tools

Product analytics tools are great at gathering data about what’s going on inside your product—but that’s generally all they do.

Because they only look at a narrow range of data, they can’t take into account the wider context of the user’s journey.

For example, imagine that a user unsubscribes from your app. There could be multiple reasons for this, such as:

  • The user was frustrated by problems with the app’s performance

  • The user was dissatisfied with the help they got from customer support

  • The user was satisfied with the product but switched to a competitor’s product that better met their needs

Learning the user’s motivation is vital for making decisions about what to improve. However, all a product analytics tool will tell you is what the user did inside the app. It can’t analyze their wider journey to tell you why and doesn’t have the ability to show you the complete story.

Product analytics vs. digital experience intelligence

While product analytics and digital experience intelligence (DXI) tools both gather data about user behavior there are some core differences to consider.

Product analytics typically track user behavior with the product itself, tracking how users interact with features and complete key conversions. They use this data to identify trends and link them to key business goals.

In contrast, DXI platforms look at user behavior across a wider range of interactions on mobile apps and websites. They also seek to identify the reasons behind key trends by gathering data around user sentiment, or looking closely at user journeys using session replay.

Lastly, product analytics are used to understand individual users who keep returning to a product over time, where the customer is the user. Digital experience intelligence products seek to understand the entirety of a user’s journey, whether they are a customer or not.

Read our article on product analytics vs. digital experience intelligence to learn more about the differences.

The route to product success: 3 ways digital experience intelligence drives better decisions

Data is revolutionizing product management. With DXI, you can go beyond surface-level insights and make smarter decisions at every stage of the product life cycle.

1. Inform your product vision and strategy

Customers won’t be able to tell you everything they need—but they will leave clues behind in their journeys. Standalone product analytics tool reveal what actions took, but won’t analyze every detail of their journeys to find those hidden clues.

Digital experience intelligence platforms give you access to advanced insights that help you learn where your product needs to go in the future:

  • Customer journey maps show you every path customers take and which ones are most effective

  • Struggle analysis gets you instantly up to speed with problematic experiences

  • Funnel analysis reveals how user experiences of problems impact engagement and conversions

  • Session replay shows you exactly how customers interact with your product (while respecting their privacy)

2. Create an indisputable product roadmap

When data isn’t the basis for your product roadmap, it can lead to big problems across teams around consensus, prioritization, differentiation and growth. Worse yet, this can have a negative effect on your most important stakeholder: your customers.

With advanced insights, you can create a customer-centric roadmap backed up by user research and revenue considerations.

  • Gather actionable feedback via modern voice of customer tools. DXI platforms link customer feedback to individual journey records, so you can learn more from your audiences.

  • Analyze the data to identify themes and trends. Often the most useful insights are not obvious from a surface-level analysis of the data.

  • Prioritize features objectively by discovering what issues currently have the most impact on churn, retention and revenue. Digital experience intelligence solutions let you assign value to the product backlog based on captured user behavior.

3. Build, measure and optimize with deep user insights

As you build your new features, DXI insights help you make decisions without guesswork—then use data to keep learning and refining.

  • Scale user research efforts without breaking the budget. Explore the entire user journey to identify opportunities, form hypotheses and drive prototypes. Next, apply live user research testing to validate or test your ideas.

  • Put ideas to the test. What customers say won’t always match what they do. For every conclusion your draw from live user research, use digital analytics to investigate further before A/B testing the ideas that hold true.

  • Get the insights you need to build better products. Digital experience intelligence data gives UX teams a view into high converting flows, low converting flows, pages with high abandonment and much more—so you can optimize every part of the customer journey quickly and efficiently.

Use Glassbox to answer any question about your audience

Unlike most product analytics tools, Glassbox captures 100% of the events that happen in your product by default. With a complete dataset at your fingertips, you can quickly identify trends in engagement, funnel drop-offs, conversions and more.

What’s more, Glassbox gives you the tools to understand user sentiment and motivations, so you can learn why users act the way they do. With this unique combination of quantitative and qualitative data, you can beyond surface-level insights and answer any question about your audience.

Discover Glassbox


What is product analytics?

Product analytics means using quantitative data to understand how users behave inside your app or website. It typically involves tracking events like users logging in, interacting with a feature or making a purchase.

By tracking these events, product analytics tools can see how user behavior changes over time, or between groups. By identifying trends in behavior—like a specific user cohort unsubscribing more than another—companies can create strategies to improve their product, retain users and increase revenue.

What are the features of product analytics?

Most product analytics tools will feature:

  • Event tracking that captures when users complete certain actions

  • Reporting features that allow product teams to look more closely at data

  • Visualization tools that show user behavior trends in graph or chart format

  • Integrations that sync data with data warehouses, CRMs and other analytics tools

Some product analytics tools also feature advanced analytical tools that identify hidden trends and patterns in the data.

What is the objective of product analytics?

The objective of product analytics is to understand how users interact with an app or website. It allows researchers to answer important questions about their product—like how users find it, what features they use, where they struggle and what unmet needs they have.

Acquiring these insights helps researchers make better decisions about how they can improve the product and serve users.