What Is Product Analytics? A Guide for PLG Teams

What is product analytics really for?

The answer is simple: To make decisions based on how customers interact with your product.

I’ve seen how product teams, despite tracking tons of data, still build features based on gut feelings and the loudest customers. The reality is that true product analytics must be actionable. It should help you prioritize your roadmap, minimize user friction, and increase ROI.

Now, I’ll go over what product analytics truly is, the most important metrics to track, and the best tools to turn data into actionable insights.

What is product analytics?

Formally, product analytics is the process of collecting, analyzing, and interpreting user behavior data (such as clicks, scrolls, feature usage, and navigation paths) to make data-driven product decisions. Unlike website analytics or business intelligence, product analytics is all about what happens inside a software product. It answers questions like:

• Which features are users engaging with the most?
• Where do new users get stuck during onboarding?
• How did our latest feature affect user retention?
• What actions lead users to become paying customers?

Most companies store tons of data, but since it’s all fragmented and siloed, it never leads to a clear path of action. But as I mentioned, product analytics must be actionable. The data should be centralized, easy to access, and laser-focused on current goals (rather than random data points).

Why actionable product analytics is indispensable

I’ve seen firsthand how bloated data leads to wasted efforts. Teams spend months building features that nobody uses, all based on an assumption or a request from a single loud customer.

Actionable product analytics helps with:

• Making data-driven decisions: Instead of relying on opinions, you can use product data to prioritize what will actually move the needle. For instance, finding that 80% of users drop off at a specific step can lead to prioritizing that aspect of the product.
• Eliminating friction: Product analytics platforms can spot rage clicks, high drop-off rates, or features that are consistently ignored. Once you find these pain points, you can use a friction log to track them and then design solutions to smooth out the experience.
• Improving user onboarding and adoption: Analytics help you measure if people are using core features regularly. For example, you can analyze the behavior of users who successfully activate versus those who churn, then build an onboarding experience that guides new users toward their “Aha!” moment faster.
• Measuring the impact of your work: Product analytics help you show stakeholders the value of your efforts. Instead of saying, “We think the redesign is better,” you can say, “After the redesign, we saw a 20% increase in feature adoption and a 15% decrease in support tickets related to that module.”

Product analytics vs. data analytics vs. business intelligence

Although all types of analytics are relevant for product decisions, there are clear differences between product analytics and other kinds of data analysis.

Here’s how other types of analytics work:

• Business intelligence (BI): Business intelligence tools focus on a company-wide perspective to track the overall health of the business. They pull data from dozens of sources, including CRMs (like Salesforce), financial systems (like NetSuite), marketing automation, and support tickets. All with the purpose of creating a single source of truth for executives and team leaders.
• Data analytics (DA): DA is more about the discipline of turning raw data into insights through complex data mining, statistical analysis, and predictive modeling. The overall purpose of data scientists is to turn massive datasets (using languages like Python or R and complex SQL queries) into a compelling story that helps stakeholders make business decisions.
• Web analytics: As the term suggests, it tracks website visitors using tools like Google Analytics. It’s usually owned by the marketing team, and it’s focused on insights that help attract visitors and convert them into leads or trial users.

Now, despite their differences, they can overlap with product analytics. BI determines the optimal business KPIs to improve, and general data analysts also look into product data to support business decisions. All in all, most teams in a product-led company will benefit from product analytics insights.

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My product analytics process in a nutshell

In the end, how you apply product analytics depends on where your company is. In early-stage startups, it’s about creating visibility. Whereas mature organizations focus more on optimization and strategy.

However, if I had to define a general guide to product analytics that can apply to almost any company, it would involve these 5 steps:

1. Define your goals: What are you trying to understand or improve? Is it user retention? Conversion rates for a free trial? The adoption of a new feature? Before diving into customer data, I recommend following a goal-setting framework like SMART or OKRs to get started.
2. Collect the right data: A good data tracking plan is essential. This mostly involves setting up events that are relevant to your goal (an event is any action a user takes, like ‘Clicked Create Report Button’ or ‘Completed Onboarding Checklist’). No-code analytics tools like Userpilot can automatically track in-app events (via autocapture) to reduce the burden on engineering teams.
3. Analyze user behavior: This is where you dig in and look for patterns. The goal is to use different analysis types (e.g., funnel analysis, trends, cohorts, etc) to turn raw data into different hypotheses. Then, you can cross-reference different data points and sources to validate those hypotheses.
4. Act on validated hypotheses: Act around the data. Let’s say you confirm that users who create a project in their first session are 50% more likely to stick around. Then, you might decide to build an onboarding checklist to incentivize new users to create a project first.
5. Measure and iterate: After you’ve made a change, go back to the data. Did your change have the intended effect? Measure the results, learn from them, and start the cycle over.

This sounds simple enough. But in order to find any success with this process, it’s indispensable to know: how to gather data, what product metrics to measure, and how to analyze product data.

1. Types of data you can track with product analytics

To get started, it’s crucial to understand the different types of data you can track.

Many product managers track so much data that they get stuck in analysis paralysis. Remember, the goal is to collect meaningful data that helps you achieve business objectives, not to add random correlations to a report and take it at face value.

For product analytics, you’ll mostly collect three types of data:

• Event data: An event is any specific action a user takes within your product (think of generating a report, inviting a teammate, or upgrading). To track it, you usually have to instrument them and create a good taxonomy to keep it organized. However, modern no-code analytics tools (like Userpilot) can track events automatically via autocapture. All you need to do is label them with a visual editor (no engineering required).
• User properties: These are attributes tied to a user’s profile (e.g., plan type, device, language, location, integrations, signup date, etc), and they help you analyze different user segments. For instance, you can see if admins on an Enterprise plan tend to generate more reports than Pro plan users.
• User feedback: These are mostly survey responses that can help you measure user sentiment. This data type complements behavioral analytics very well, as it tells you why a user doesn’t use a feature (e.g., maybe there’s an unknown bug or the UI is ambiguous).

2. Measuring key product metrics based on business goals

Instead of chasing dozens of vanity metrics, it’s best to focus on a few that give you a holistic view of user value and product health.

There are dozens of metrics that could be relevant for your business. However, these are the most common product metrics in B2B SaaS:

• Activation rate: The percentage of new users who perform the key action (or set of actions) that delivers an “Aha!” moment.
• Feature adoption rate: It measures how many users are engaging with a specific feature. This tells you if you’re building products people actually use or if you’ve just built another data export tool for two customers.
• Daily active users (DAU) and monthly active users (MAU): These are standard measures of your user base’s size. Additionally, you can track the DAU/MAU ratio to see how many users stick around over the months.
• Session duration: How long users spend in your product per session. You might want it to be longer or shorter, depending on the context.
• User retention rate: The percentage of users who come back over a given period (e.g., Day 1, Day 7, Day 30).
• Churn rate: The percentage of users who stop using your product in a given period.
• Customer lifetime value (LTV): The total revenue you can expect from a single customer account.
• Customer acquisition cost (CAC): The cost of acquiring a new customer. Additionally, LTV/CAC ratio and CAC payback period are common indicators of business health.
• Net Promoter Score (NPS): It measures sentiment by asking users, “How likely are you to recommend us?”. The higher the score, the higher customer loyalty.

3. Analyzing user behavior to validate hypotheses

There’s a whole world when it comes to data analysis techniques.

You need to learn how to validate hypotheses that have weight. I’m talking about using frameworks such as “The 5 Whys”, data triangulation, or fault-tree analysis to spot the potential root cause of a problem.

For product analytics, specifically, the most common tools for monitoring user behavior include:

• Funnel analysis: A funnel is simply the series of steps to reach a critical goal (like signing up, completing onboarding, or making a purchase). Funnel analysis measures the conversion rate between each of those steps, pinpointing exactly where users are dropping out. With Userpilot, for instance, I’ve mapped our user onboarding flow, activation steps, secondary feature adoption, and more with it.

• Path analysis: Path analysis shows the most common paths users take before or after a specific event. It helps you understand how users actually navigate your product versus what you expected them to do. For example, you might find out that “Pro” plan users are taking a five-click detour to access a key reporting feature.
• Cohort analysis: A “cohort” is simply a group of users who share a common characteristic, usually the time they took their first action (e.g., the “May 2024 signups”). By grouping users into cohorts, you can compare the behavior of different groups over time to catch significant differences. For example, it lets us compare the retention rate of cohorts who signed up in April vs those who signed up in May, and track their experiences to see if there’s an issue that leads to lower retention.
• Session replays: Session replays show how users interact with different elements on a page or product screen. It gives a qualitative perspective to events that would otherwise be only quantitative. For instance, you can use it to watch the sessions of users who dropped off from a feature and validate potential hypotheses.

What to look for in a product analytics tool

The best product analytics tool for you isn’t the one with the most features, but the one that can fulfill your use cases more efficiently.

In short, you must prioritize how a product integrates and becomes accessible for your whole team. For this, here are the aspects you should evaluate first:

• Data collection and segmentation capacities: Look at how the platform collects data and how deeply you can segment users with it. Some tools require engineers to instrument events, whereas other tools have event autocapture features with no setup. Userpilot, for instance, captures user interactions automatically, lets you label raw events visually (no coding required), and can segment users based in-app behaviors.
• Data analysis and visualization: Evaluate how a tool processes data and how your team can make decisions with it. See what behavioral reports are supported (funnels, paths, retention cohorts, etc) and if you can customize the dashboards/charts for your most important metrics. I’d also recommend a no-code analytics tool if your team is not technical.
• Quantitative and qualitative data: An effective tool must combine quantitative data with qualitative insights (like surveys, session replays, and heatmap analysis). I’d recommend looking for an all-in-one platform (like Userpilot) if you don’t have dedicated tools for qualitative data.
• Ability to act on insights directly: Data is useless if you can’t act on it. The most effective platforms let you identify a friction point and immediately launch an in-app guide to address it. Even if you already have a dedicated tool for user engagement, a platform that combines product analytics with user engagement will make the process smoother (no need to export data, set up integrations, or deal with delays).

To help you navigate the options, here’s a quick overview of my top picks for product analytics:

1. Userpilot for combining product analytics with omnichannel engagement

Okay, I’m biased, but for good reason. At Userpilot, we built a tool that doesn’t just show you what users are doing, but also improves the product experience to drive adoption. It’s a complete product growth platform for product teams at B2B SaaS companies.

We combine powerful behavior analytics with a suite of no-code engagement tools. This means you can identify a drop-off point in a funnel and immediately implement an interactive walkthrough to help users take the next step. All without writing a single line of code.

Key features:

• No-code product analytics features: Userpilot lets you set up event tracking and build analytic dashboards without writing code. These include autocapture, session replay, custom reports, advanced filters, behavioral reports (including funnels, paths, cohorts, and trends), and more.

• In-app engagement tools: It can trigger personalized in-app guides (thanks to its advanced segmentation), interactive walkthroughs, onboarding checklists, and hotspots with deep customization that look native.

• Customer feedback: Userpilot lets you customize in-app surveys (like NPS, customer satisfaction, or CES), with branching options for follow-up questions, and advanced targeting options (so you can send them when the user is more likely to respond).

• User and company profiles: Besides advanced segmentation, Userpilot also creates profiles of individual users and companies. This lets us, for example, focus on high-value accounts to prevent high revenue churn or follow account-based marketing strategies.

• Multi-channel analytics: Userpilot lets us track events from web, mobile apps, and email campaigns in one unified platform (no need to force-integrate email with product data).

• Product AI agent: The upcoming AI assistant (Lia) will automatically analyze user behavior, flag recurrent themes in survey responses, and even craft personalized in-app campaigns. The AI can also parse survey responses and generate insights. Join the beta here for early access.

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2. Mixpanel for advanced events analytics

Mixpanel is a powerful option for deep, event-based analysis. It’s best for data teams that need to understand complex user journeys and have the resources to maintain event tracking.

Key features:

• Custom event instrumentation to capture virtually any user interaction (e.g., button clicks, form submissions, purchases).
• Behavioral reports, including funnel reports, the “Flows” feature (i.e., path analysis), and a retention analysis graphic.
• The Insights reports, which allow ad-hoc querying on events. As well as customizable dashboards where you can pin multiple reports and KPIs.
• Session replays that link to your analytics data.
• An AI assistant for asking questions in plain English.

Its main downside is that it requires manual event implementation, which needs developer help. Plus, since its pricing is based on tracked events, it can scale unpredictably and become too expensive without notice.

3. Amplitude for enterprise growth and experimentation

Amplitude is built for large-scale companies focused on growth and optimization. It offers a comprehensive suite of analytics tools, along with features for A/B testing and personalization, making it a great option for dedicated growth teams in bigger companies.

Key features:

• Real-time analytics and strong event modeling to analyze complex journeys.
• Built-in A/B testing and top-class feature flags (for experimentation).
• Custom dashboards for tracking advanced workflows.
• Standard behavioral reports (funnels, paths, cohorts), plus predictive analytics to forecast user behavior.

Although Amplitude can handle complex tasks, this comes at the cost of a steep learning curve that can be overwhelming for non-technical product managers. Plus, you’ll need engineers to install it and maintain events.

4. Heap for autocaptured events

Heap’s main selling point is its “autocapture” feature. Instead of manually tagging every event you want to track, Heap automatically captures every single click, tap, and form submission. It’s best for fast-moving teams (especially in startups and scale-ups) who need faster implementation and easy access to historical data.

Key features:

• Automatic capture of every user action (click, tap, swipe, page view, etc.), which you can always analyze retroactively.
• Strong segmentation tools to filter different user groups and understand their differences.
• Behavioral analytics, such as funnel analysis, journey analysis, and retention charts. It also offers the “Engagement Matrix”, which correlates frequency of certain actions with retention.
• Built-in heatmaps and session replays to visualize users’ real behaviors.

The downside with Heap is that there’s no easy way to filter the events it captures. And since its pricing is based on session volume, it leads you to pay for a bunch of events you don’t need.

5. Fullstory for visual analytics

Fullstory excels at telling you the “why” behind your data. It’s best for product, UX, and customer support teams who need to diagnose usability issues, fix bugs, and gain deep empathy for their users.

Key features:

• High-fidelity heatmaps and session replays, which you can filter by user segments or timeframe.
• Tagless autocapture for all user interactions (clicks, form inputs, page visits, etc.) without manual event tagging.
• Automatically detects “frustration signals” like “rage clicks”, “dead clicks”, “error clicks”, excessive scrolling, and back-and-forth navigation.
• Journey maps that illustrate common paths, drop-offs, sequences, and branching of user flows throughout your product. You can also create funnels for sequences of events, analyze conversions, and identify trends over time.

6. Google Analytics for general web analytics

Google Analytics 4 is a free tool for analyzing website traffic patterns and basic user engagement. Every company uses it to track basic website and app performance data, which can then be integrated with other tools.

Key features:

• Autocapture for basic events (like first visit, session start, etc.).
• Standard reports on user acquisition (channels, campaigns), engagement (pages/screens views, user stickiness), monetization (if ecommerce or ads), and user retention. The Explorations feature lets you do ad-hoc analyses like funnels, path analysis, segment overlap, and cohorts.
• Up to 25 custom user properties (like user type, plan, etc.), and you can create audiences based on behavior (which can be exported to Google Ads for retargeting, a big plus of GA).
• Privacy controls to work with or without cookies (consent mode). Plus, it implements IP anonymization by default to better align with privacy regulations.

Since GA4 is free, you should use it. But remember, it’s not tailored to product analytics like other dedicated tools, and it’s not enough to get meaningful insights for product growth.

Userpilot is the ideal product analytics solution for PLG companies

Almost any company with software products needs a product analytics tool.

Products like Amplitude or Mixpanel are great if you have the budget and engineering resources to use them to the fullest. Whereas Userpilot is, in my opinion, the best platform for lean PLG teams that don’t want to rely on devs.

So if you’re at a PLG company looking for a solid product analytics platform, book a Userpilot demo to start finding product insights without coding.

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Userpilot strives to provide accurate information to help businesses determine the best solution for their particular needs. Due to the dynamic nature of the industry, the features offered by Userpilot and others often change over time. The statements made in this article are accurate to the best of Userpilot’s knowledge as of its publication/most recent update on December 2, 2025.