A Complete Guide to Product Analytics: Metrics, Types & Tools

What is product analytics?

Product analytics is the process of gathering, and analyzing data to better understand customer behavior, product usage, and other business metrics.

It can be used by multiple teams to optimize product performance, identify improvement opportunities, and measure marketing campaigns’ success.

Why is product analytics important for businesses?

All business leaders want to improve their business while attracting and retaining the right customers. Instead of walking this path blindfolded, product analytics can help product leaders focus on the right experiences, product features, and customer relationships.

Let’s dive a bit deeper into the benefits that analytics can offer for your digital product:

Get better insights about your customers to improve their journeys

Every product has one purpose: to serve customers and help them meet their goals. It’s not enough to add “useful” features to your product and let users interact with it whenever they come across it.

Each feature you add to your product must solve a specific pain point that your customers have already communicated with you via customer interviews or welcome surveys.

With product analytics, you can monitor the journey different users take to get to their goals and get insights such as:

• Are there any steps in the journey that can be skipped to shorten the time-to-value?
• Is there a better feature for this specific pain point/goal that my customer may not know about and needs help using it?
• Are there any steps users get stuck on which creates friction on their side?

With answers to these and more questions, you can improve customer journeys.

Prioritize feature creation based on product analytics data

It can be very tempting to create the next big feature but the smart decision here would be to look into product analytics and identify which features are used most frequently and which are underutilized.

This way you can prioritize development efforts to focus on features that deliver the most value to your customers.

Improve elevated expectations for product experience

In today’s world, people expect products to just “get them,” providing seamless, intuitive experiences.

Analytics helps you stay ahead by constantly learning and improving digital interactions, making your product not just good, but something your customers can’t live without.

Who benefits from product analytics?

Anyone in your company should care about product analytics.

So if you’re into any of the following positions, here’s why product analytics are a must for you:

• Product manager: Product managers can monitor user behavior and see if a feature is driving engagement and fits the user’s needs. This allows them to make data-driven decisions to prioritize development, sunset features, create a successful roadmap, and overall, build a better product.
• Product marketing manager: Product marketing managers can track usage patterns and purchase behavior to come up with data-driven pricing strategies, upsell messages, and in-app promotion tactics. This data also allows us to measure business success through KPIs like customer lifetime value, user retention, monthly active users, and expansion MRR.
• UX designers can monitor how users interact with your product to measure its usability. This way, they can find opportunities to improve the product experience and make it as seamless as possible.
• Software developer: Knowing which features work and which don’t can give you an idea of what type of work you should prioritize—helping developers design a product that better fits the target market.
• Customer success managers can watch over product usage data and in-app behavior data to understand what path is more likely to lead to success, measure the effectiveness of the onboarding process, and reach out to disengaged users who are likely to churn.

How can product analytics impact key metrics

Product analytics can significantly impact key business metrics by providing actionable insights that drive improvements across the entire customer journey.

Acquisition

Product analytics helps identify which marketing channels and touchpoints drive the most engaged users. If you understand how potential customers interact with your product before they sign up, you can optimize your acquisition strategy to attract more high-quality leads.

Activation

Analytics can reveal what actions new users take that lead to them experiencing your product’s value quickly (often referred to as the “aha moment”).

By understanding these behaviors, you can optimize the onboarding flow to increase the number of users who get activated and fully engaged with your product early on.

Adoption

By tracking how users interact with different features, analytics can help you identify which ones drive ongoing use.

This allows you to prioritize product improvements, promote the most valuable features, and develop specific flows to guide further adoption.

Retention

Product analytics sheds light on the behavioral patterns that keep users coming back.

By identifying patterns that correlate with long-term engagement, you can create strategies to re-engage at-risk users, reduce churn, and maintain a loyal customer base.

Referral

Understanding what motivates users to share your product with others is crucial for growth. Analytics can pinpoint which aspects of the product encourage users to refer friends or colleagues, allowing you to amplify these moments and build a strong referral engine.

Revenue

By analyzing user behavior you can identify your power users and trigger upsell and cross-sell opportunities to expand your revenue metrics.

8 Key types of product analysis for different insights

There are various kinds of product analyses that product teams should carry out to gain necessary insights. Let’s talk about the most useful ones.

1. Trend analysis

Product trend analysis is a research methodology used to examine current and historical data to identify patterns, shifts, or developments over time in customer behavior related to your product.

Trend analysis focuses on gaining valuable insights into when and why customers perform certain actions. And then use these findings to predict future events and trends, and make better-informed product decisions.

2. Cohort analysis

Cohort analysis is a method of analyzing user behavior by observing the actions of a particular group of users (cohorts) within your platform.

This can help you assess the impact of your actions on users. For example, you may be able to tell that a certain update or feature release has increased user engagement or churn.

Also, dividing your users into cohorts makes it easier to find the root cause of issues. That’s because you can cross-reference the data.

3. Retention analysis

Retention analysis looks into how many users keep coming back and using your product. This is important because it’s an indication of perceived product value and can help you forecast product growth and revenue.

Retention analysis focuses on identifying the factors that contribute to increased user retention and maximizing them.

4. Churn analysis

Churn analysis provides deep insights into why customers are leaving. It involves analyzing churn surveys to identify trends that you can address.

Maybe they found the user interface too complex, or perhaps they didn’t quite get the value for money they were expecting.

Now, churn analysis is not just about recognizing patterns—it’s about using this feedback to address these concerns head-on and prevent churn.

So if users find your app’s UI to be too convoluted, for example, now you know that revamping your product’s interface will help you reduce churn in the long term.

5. Funnel analysis

Funnel analysis tracks and analyzes the flow of user behavior through a series of steps toward achieving a specific goal.

For example, if there is a sequence of actions needed for users to activate, each of them is going to be one stage in the funnel.

Looking at how users progress through the funnel allows you to understand where users experience friction that slows them down or makes them drop off.

6. User journey analysis

When trying to figure out what your customers want, who should you ask? The customer!

Instead of just blindly developing your product, listen to your customers by studying and analyzing their journeys. This will enable you to gain a deeper understanding of what your user wants and how you can provide them with it.

Apart from this, there are several more advantages of analyzing user flow, which we discuss below.

• Understand user behavior and what makes them stick: Know what users want and develop features around it, thereby increasing repeated engagement and product stickiness.
• Improve customer lifetime value: When you work to give users what they want, they’ll stick around for longer, thereby increasing the customer lifetime value.
• Reduce churn and improve user experience: By identifying and improving friction points where users would drop off, you can provide more positive user experiences.
• Retain more customers: When you uncover and erase pain points users encounter, customer satisfaction goes up, and so does your retention rate.

7. Milestone analysis

Product managers can often set specific user goals and lay out milestones throughout the customer journey. With this info, you can:

• Use analytics to track the average user’s progress and where they stand within the journey (like in the screenshot below).
• Use milestone analysis to spot stages where users may be struggling to find progress or where engagement may be waning.
• Find ways to aid your customers in achieving further goals.

For example, if many users are having trouble adopting core features, you can try adding an onboarding checklist and see how it influences new users to reach the activation stage faster.

8. Customer behavior tracking

Customer behavior tracking is all about understanding how people use your product so you can make smarter decisions. Here’s a quick look at the main types:

• Session Recording: Watch replays of user sessions to see exactly how they interact with your product, highlighting any usability hiccups.
• Event Tracking: Track specific actions like clicks and button presses to see which features are getting the most attention.
• Heatmaps: Visualize where users click and scroll on your pages, showing hot spots and ignored areas.
• A/B Testing: Experiment with different versions of a feature to see what works best based on real user feedback.

• Clickstream Analysis: Dive deep into the exact sequence of clicks users make, helping you streamline navigation.

These tracking methods give you the insights needed to fine-tune your product, making it more user-friendly and effective.

How to implement product analytics system

Imagine you’ve just launched a product that you’ve poured time, energy, and heart into. You’re excited—it’s live, and you can’t wait to see users interacting with it.

But then, the reality sets in: How are they using it? Which features do they love, and where are they getting stuck?

This is where product analytics comes in, acting like a detective that reveals the stories behind your user interactions, guiding you on what to improve.

Here’s how you can set up a product analytics system step-by-step, making the data work for you:

Step 1: Define your business objectives

Before diving into the data, take a moment to reflect on what you want to achieve. Think of this as setting your North Star—do you want to boost user activation, reduce churn, or figure out which features are most engaging?

By defining your objectives upfront, you’re not just collecting data for data’s sake; you’re creating a purposeful roadmap that keeps you focused on what matters most. Without clear objectives, you might find yourself drowning in data, overwhelmed, and directionless.

💡Make sure your objectives are SMART

Step 2: Select the right analytics tools for your goal

Choosing the right tools is like picking the right gear for a road trip—you need the best fit for your journey. Whether it’s Userpilot, Mixpanel, or Google Analytics, your choice should align with your objectives.

Are you keen on detailed behavioral data? Or maybe you need an intuitive dashboard for quick insights? Each tool has its strengths, so invest time in understanding what each offers. Don’t hesitate to try a few out, explore their features, and see which feels like the best fit for your team’s needs and skills.

💡By the way, we compiled a list of analytics tools you may want to check out: Best User Analytics Tools for SaaS in 2024

Step 3: Integrate your product analytics tool with different tools to get unified data

Here’s the reality: your product doesn’t exist in a vacuum. Users interact with your marketing emails, support team, and maybe even your sales reps.

Integrating your analytics tool with other systems—like your CRM, support software, or marketing platforms—gives you a unified view of the customer journey.

Imagine seeing how a user’s support ticket history influences their product usage, or how an email campaign impacts feature adoption. This holistic view turns scattered data into a connected story, giving you richer insights into your users’ behaviors.

This is also why you should consider your analytics tools integrations when choosing one.

Step 4: Start the data collection process

Now, it’s time to roll up your sleeves and start gathering quantitative data. Set up your tracking events to capture key user actions: clicks, sign-ups, feature usage—you name it.

This is where your initial planning pays off, as you’ll want to ensure you’re collecting data that directly ties back to your business goals. It’s like setting the stage; if you set it right, the data will flow seamlessly and tell you exactly what you need to know.

Step 5: Analyze and interpret data

Data analysis isn’t just about numbers; it’s about understanding the stories those numbers tell. Dive deep into analyzing behavioral data and look for patterns.

Are new users dropping off during onboarding? Is there a feature that’s a hit with power users but ignored by newbies?

Use dashboards, reports, and visualizations to bring the data to life.

Think of yourself as a data detective—your job is to piece together clues that reveal what’s working and what’s not.

Step 6: Establish benchmarks for key performance indicators to measure progress

Setting benchmarks is like planting a flag that says, “This is where we are, and this is where we want to be.” It gives context to your data, helping you understand whether you’re on track.

Establishing KPIs—like conversion rates, time to activation, or feature adoption rates—provides a way to measure progress. And when you see a dip or spike, you’ll know exactly what to investigate further.

💡To help you with benchmarking, we have created a SaaS product metrics benchmark report based on first-party data from 547 companies – go check it out!

Step 7: Take action based on insights

The final step is the most exciting—turning insights into action. This is where all your hard work pays off.

Found that your onboarding process needs improvement? Tweak it.

Did you discover that a certain feature is underused? Maybe it needs a little spotlight in your next update or a tutorial to guide users.

The key is to keep iterating and optimizing based on what the data is telling you. It’s a cycle of continuous improvement, and each tweak gets you closer to creating a product that truly resonates with your users.

Best practices when working with product analytics

Working with product analytics is not just about collecting data; it’s about making that data meaningful.

To get the most out of your analytics, here are some best practices that can elevate your approach and bring deeper insights:

Couple your product analytics with surveys for better insights

While product analytics shows you what users are doing, surveys reveal the “why” behind those actions. Pairing quantitative data from your analytics with qualitative data from surveys can provide a fuller picture of user behavior.

For example, if you notice users dropping off at a particular step in your product, a quick survey can help you understand if it’s due to confusion, lack of interest, or something else entirely.

By blending these data points, you get actionable insights that guide product decisions with a user-first perspective.

Automatically track metrics with autocapture

Setting up manual tracking for every interaction can be tedious and prone to error. That’s where autocapture comes in—tools like Userpilot can automatically track user interactions without the need for pre-defined events.

Autocapture helps you gather comprehensive data effortlessly, ensuring you don’t miss critical behaviors that might have been overlooked otherwise. Plus, it allows you to explore user interactions retrospectively, identifying new patterns and opportunities for optimization without having to set up specific tracking in advance. However, you can label events for future use.

Learn from other companies’ success stories

Case studies are a great source for learning how other businesses use product analytics to grow their digital products:

• Beable Education improved user engagement and efficiency using Userpilot’s analytics and feedback features. By leveraging features like page tagging, funnel analysis, surveys, and resource center analytics, Beable enhanced user onboarding, tracked engagement, and gathered feedback, leading to better decision-making and higher user participation.
• Zoezi lacked insights regarding product usage and had an inefficient help center. They chose Userpilot after analyzing Pendo, ProductFruits, and Appcues. Zoezi improved customer communication and made better product prioritization decisions after using Userpilot’s analytics, onboarding, and support features.
• Shelterluv switched from Pendo to Userpilot due to Pendo’s complexity and high cost. Userpilot provided better value, ease of use, and improved customer communication, reducing support workload.

Product analytics tools for different teams

When it comes to choosing the right product analytics tool, different teams have unique needs. Whether you’re a product manager trying to boost engagement, a customer success team tracking health metrics, or a UX designer looking to improve user flows, there’s a tool designed just for you.

Here’s a look at some of the top product analytics tools, their standout features, and what you can expect to pay.

Userpilot: Best product analytics software for product teams looking to enhance user engagement metrics

When it comes to product teams looking to enhance user engagement, Userpilot stands out as a top choice. It’s not just another analytics tool; it’s a full-fledged platform designed to help you understand how users interact with your product and guide them to success.

Here’s why Userpilot is a favorite among product managers and teams striving to create a seamless, engaging user experience:

• No-Code Event Autocapture: Track all user interactions without needing code, making setup quick and hassle-free.
• Trend Analysis: Visualize how user behavior changes over time to identify growth opportunities or problem areas.
• Funnel Analysis: Understand where users drop off in key processes, helping you optimize onboarding and conversion paths.
• Path Analysis: Map user journeys through your product to uncover common routes and unexpected behavior.
• Retention (Cohort) Analysis: Analyze user retention patterns to improve engagement strategies.
• Custom Dashboards: Tailor dashboards to display the metrics most relevant to your team’s goals.

Product teams choose Userpilot because it goes beyond just collecting data—it helps create experiences that truly resonate with users.

Pendo: Best product analytics platform for customer success teams to track customer health for web and mobile apps

Pendo is perfect for customer success teams that need deep insights into how customers use their products and how they feel about the experience.

It comes with:

• Behavioral Analytics: Track and visualize user paths, funnels, feature adoption, and overall product health.
• In-App Guidance: Create guided walkthroughs and resource centers to improve onboarding and ongoing engagement.
• Sentiment Analysis: Capture user feedback and NPS scores directly within the app to gauge satisfaction.
• Session Replay: Watch how users navigate your product, identifying areas for improvement.
• Integration Capabilities: Sync data with tools like Salesforce and HubSpot for comprehensive user insights.

Amplitude: Best product analytics solution for data analysts and product managers who need a powerful tool for advanced data analysis

Amplitude is the go-to choice for data analysts and product managers who need a powerful tool for digging deep into user behavior.

It comes with:

• Deep Behavioral Analysis: Analyze user behavior in detail with features like event segmentation, cohort analysis, and user journeys.
• Predictive Analytics: Forecast user actions and identify the factors driving engagement and retention.
• Real-Time Data: Access insights immediately to make quick, data-driven decisions.
• Collaboration Tools: Share customized dashboards and insights across your team for better alignment.

Heap: Best product analytics tool for product teams that want to capture all user interactions automatically

Heap is ideal for product teams that want a hands-off approach to tracking user interactions. Its autocapture technology ensures you never miss a data point, making it perfect for teams that need comprehensive analytics without the hassle of setting up manual events.

It comes with:

• Autocapture Technology: Automatically records every user interaction, capturing clicks, swipes, and more.
• Retroactive Analysis: Review past user behavior without missing any critical data points.
• Funnel and Path Analysis: Identify drop-offs and optimize user flows for a smoother experience.
• Behavioral Segmentation: Group users based on their interactions for targeted analysis.

Hotjar: Ideal product analytics solution for UX teams who want to understand user behavior through heatmaps, recordings, and surveys

Hotjar is the tool of choice for UX teams focused on visualizing user behavior. It offers a suite of features designed to help you see how users interact with your product and gather feedback to enhance the user experience.

It comes with:

• Heatmaps: Visually analyze where users click, scroll, and spend time on your product.
• Session Recordings: Watch real user sessions to identify usability issues.
• On-Page Surveys: Collect feedback directly from users to understand their needs and frustrations.
• Feedback Widgets: Engage users with interactive feedback tools placed on specific pages

Google Analytics: Free web analytics service that is suitable for marketing teams and product owners who need basic website traffic and user behavioral data

Google Analytics is widely known for its ability to track web traffic and basic user interactions, making it a great starting point for marketing teams and product owners. While it’s not as specialized for product analytics as other tools, it provides foundational insights that are valuable for teams just starting their analytics journey.

Its key features include:

• Web Traffic Analysis: Track visitors, sessions, bounce rates, and other key metrics.
• Event Tracking: Set up custom events to monitor specific user actions.
• Audience Segmentation: Segment users based on various criteria like demographics and behavior.
• Custom Dashboards: Create dashboards to visualize the data that matters most to your team.

Conclusion

SaaS product analytics is an indispensable tool for understanding how your users interact with your product. It provides actionable data that can guide you to improve your product’s relevance, competitiveness, and profitability.

But remember, you need the right tool to unlock these valuable insights. So why not book a Userpilot demo to see how you can unlock new growth opportunities?

FAQs

What does a product analysis do?

Product analysis evaluates user behavior within a product to understand how features are used, identify friction points, and improve user experience. It helps teams make data-driven decisions to enhance engagement, retention, and overall product success.

What is the difference between product analytics and data analyst?

Product analytics focuses on tracking and analyzing user interactions within a product to optimize its performance. In contrast, a data analyst works with broader data sets across various domains, using data to inform business decisions beyond just product-specific insights.

Which teams use product analytics?

Product analytics is used by product teams, UX designers, customer success teams, data analysts, marketing teams, and growth strategists to improve user engagement, understand customer health, and make data-driven decisions.

What is the difference between product management and product analytics?

Product management involves planning, developing, and managing a product throughout its lifecycle, focusing on strategy, roadmaps, and user needs. Product analytics, on the other hand, provides data and insights into how the product is performing and how users interact with it, informing product management decisions.

What do you measure in product analytics?

Product analytics measures metrics such as user engagement, feature adoption, conversion rates, retention, churn, user paths, and overall product health, helping teams understand user behavior and optimize the product experience.

Product analytics tools vs data analytics tools vs data management platforms vs business intelligence tools

While these terms might sound similar and confusing to some, they represent distinct categories of tools with specific functionalities.

Product analytics platforms are specialized for tracking and analyzing user interactions within a product, data analytics tools offer a more generalized approach to examining diverse data sets.

Data management platforms are primarily concerned with the efficient data warehouse, and organization, ensuring that it can be easily accessed and used.

A business intelligence tool, on the other hand, focuses on transforming all your data into actionable insights that can drive strategic decision-making across an organization.