What is product analytics?

Product analytics is a business intelligence tool that tracks user interactions within digital products like websites and apps. It helps businesses understand user behavior, improve product experiences, and drive growth. Key features include tracking events, analyzing user journeys, and identifying areas for optimization. Product analytics is crucial for product managers, UX designers, and growth strategists to make data-driven decisions and enhance user engagement, ultimately leading to better business outcomes.

A breakdown video of what product analytics is and how it works

Product analytics, explained: How to become data-driven - YouTube

Product analytics, explained: How to become data-driven

Why is product analytics important?

Today’s companies must adopt a digital-first mindset in order to best serve their customers (and make sure they stick around). Software users expect tools that are seamless, intuitive, and delightful–no matter if they’re using an application in their personal life or at work. For businesses tasked with delivering on those expectations, improving one’s digital product starts with understanding how users are engaging with it. Product analytics provides a foundational layer of data that companies can use to measure and optimize their users’ experiences.

Who uses product analytics?

Product managers, user experience (UX) designers, and growth strategists rely on product analytics to track digital interactions within their apps, websites, and devices. HR and IT managers also rely on analytics to gauge how effective their employee onboarding programs are, strategize ways to improve productivity, and ensure compliance in key areas such as security.

What insights does product analytics provide?

The way the data is grouped and queried plays a major role in how useful product analytics are to the product manager, UX designer, or growth strategist. The same is true for employee-facing roles, be they in HR, change management, or information security. Some of the most common ways to understand product usage include:

• Trends: Graph engagement with certain features or pages and compare against other parts of the product over time, or compare engagement with a single part of the product over two different time periods.
• Funnels: Track the levels of drop-off at each step across a specific subset of features and pages in the product. With a funnel analysis, any combination of steps can be reviewed in any chronology.
• Paths: See all the product journeys users take either leading up to or following a specific interaction, with a measure of how common or uncommon the next step being taken is. Unlike funnels, paths include all possible upstream or downstream interaction scenarios.

Product analytics vs. marketing analytics: Understanding the difference

Product analytics focuses on post-acquisition behavior—understanding how users interact with your product after they become customers. It tracks feature adoption, user flows, retention patterns, and in-app engagement to optimize the product experience and reduce churn.

Marketing analytics focuses on pre-acquisition behavior—tracking how prospects discover your brand and convert into customers. It measures campaign performance, traffic sources, conversion rates, and customer acquisition costs to optimize marketing spend.

Why both matter, and how they work together

The most successful companies don't choose one over the other—they use both in tandem. Marketing analytics tells you which campaigns bring in the highest-quality users, while product analytics reveals whether those users find value and stick around.

When these insights are connected, such as through Pendo's integrations with CRM and marketing platforms, teams can optimize the entire user journey from first touch to product advocate.

Bottom line: Marketing analytics gets users in the door; product analytics keeps them there and growing. Pendo analytics offers both!

Why should I use product analytics?

User insights and ROI

Until recently, product decisions were evaluated by whether or not a feature launched on time. Product analytics allows product and UX teams to better understand the effectiveness of their strategies or user engagement and their return on investment (ROI). The data that comes from tracking in-app events helps product teams learn what parts of the product are being used, how often, and by whom, as well as the product experience paths that lead to the outcomes that matter most.

At IHS Markit, the team uses product analytics to pinpoint which features get little to no use, so they can retire them and reduce technical debt.

Growth and experimentation

Product analytics unlocks the metrics by which hypotheses are made and meaningful engagement is measured: adoption by monthly active users (MAU), adoption by daily active users (DAU), stickiness by return rate over time, breadth across features or products, depth across users in a specific cohort or account, and how they relate to business metrics. With product analytics, product managers, UX designers, growth strategists, and change managers can observe a challenge or opportunity, develop a plan, deploy the change, measure outcomes, and iterate with minimal latency or dependencies.

Successful digital transformation

A successful digital adoption strategy incorporates product analytics into its gameplan. Robust analytics not only help managers see how well employees are, for example, using a new product feature. They also let teams analyze existing workflows and employee behavior within and across software to help inform decisions about future app purchases and recommended best practices.

How is product analytics used?

For product managers, UX designers, and change managers alike, the application of product analytics starts with a question to answer. Examples of common questions that can be answered by product analytics include:

• How would a change to the experience affect engagement?
• Which features should we retire to improve outcomes?
• What combination of interactions contribute most to conversion?
• Why are certain products in my portfolio stickier than others?
• Where are the biggest frictions and leaks during onboarding?

With product analytics tools, companies can also correlate their product insights with user analytics and other operational metrics to get a clear view of how the product impacts behaviors and leads to key business results such as a reduction in support tickets, increased productivity, and an overall higher ROI on their software.

What is a good product analytics workflow?

A structured analytics workflow ensures your team extracts maximum value from product data. Here's a proven five-step framework:

Step 1: Define Clear Objectives

Start every analysis with a specific question aligned to business goals:

✓ Good: "Why is Feature X adoption below 20% for enterprise users?"

✓ Good: "Where do new users drop off in the onboarding flow?"

✗ Too vague: "Let's see how people use the dashboard."

Step 2: Establish data governance standards

Create event naming conventions:

• Use consistent formats (snake_case recommended)
• Use present-tense verbs ("button_click" not "button_clicked")
• Create a documented event dictionary

Form a user segmentation strategy:

• Define cohorts based on behavior, not assumptions
• Document segment definitions for cross-team alignment

Step 3: Prioritize analyses by business impact

For this step, focus on:

1. High-traffic, low-conversion areas (biggest opportunity)
2. Core user flows (onboarding, activation, key features)
3. Churn indicators (declining engagement, feature abandonment)

Step 4: Move from insight to action

With Pendo's unified platform, you can:

• Identify friction points in product analytics
• Deploy in-app guides to address issues immediately
• Measure impact in real-time (days, not weeks)

Step 5: Create regular analytics review cadences

Consider adopting something like the below:

Weekly: Review key metrics dashboards

Bi-weekly: Deep-dive on specific features or segments

Monthly: Present findings to stakeholders Quarterly: Assess whether analytics investments are driving outcomes

What type of data does product analytics track?

Product analytics solutions typically track two types of data about user interactions:

1. Event Tracking: User actions are commonly called “events.” Events include clicks, slides, gestures (for mobile and other device types), play commands (for audio and video), downloads, page loads, and text field fills. The event includes the type of element, the name of the element, and the action the user took.

2. Event Properties: The way one understands the specific attributes of the tracked interactions is the work of event properties. Product managers, UX designers, and growth strategists don’t only care if something happened, but also the context that distinguishes activity from impact when analyzed longitudinally.

How does product analytics help interpret A/B test results?

A/B testing and product analytics work hand-in-hand. While A/B testing tells you which variation won, product analytics reveals why—and helps you design smarter experiments.

Before the test: Identify what's worth testing

Product analytics surfaces high-impact opportunities:

• Funnel analysis reveals where users drop off
• Feature adoption tracking shows underutilized capabilities
• Path analysis uncovers unexpected user journeys

During the test: Set multi-dimensional success criteria

Rather than relying solely on conversion rates, product analytics helps you define comprehensive metrics:

• Primary metric example: Onboarding completion rate
• Secondary metrics related to onboarding completion rate: Time to first value, 30-day retention, feature adoption

Analyzing results: segment for deeper insights

Product analytics enables you to:

• Analyze by cohort: Does variation B work better for enterprise vs. SMB?
• Compare by user maturity: Do new users respond differently than power users?
• Check for side effects: Did the test improve one metric but harm another?

What’s the origin of product analytics?

For today’s product managers, UX designers, and growth strategists, product analytics is the key to building a product roadmap and driving innovation and continuous improvement. Where web properties were historically judged by metrics that revealed little about the relationship between digital products and business objectives—page views and session duration—the modern app-based web and mobile internet is powered by more telling and contextual interactions: events, engagement, and journeys.

Why is product analytics important for digital adoption?

Product analytics matters for digital adoption because in order to assess whether employees are getting the most value out of software, managers have to be able to track how they are using it and what, if any, roadblocks they are encountering.

How do you implement product analytics software?

It’s critical to choose a product analytics solution that can start collecting all of your product’s data automatically and allow you to examine it retroactively. This means you can implement your solution and access the data you need right away as different questions arise.

Is Google Analytics a good tool for product analytics?

Google Analytics is a powerful tool, but the insights it provides are limited in scope and designed more for web analytics, SEO, and marketing than for true product analytics use cases.

Why is Pendo the ideal product analytics solution?

Now you have the answer to “what is product analytics” and we have the solution. Pendo’s product analytics capture everything that happens in your product from the moment you install it.