As product managers, we take every opportunity we get to learn more about our customers because understanding their needs is critical to building useful products. This means conducting customer interviews, running surveys, and examining in-product analytics. The data we glean from product analytics tells us how users actually use the product – not what they want to do, how they think they're using them, or even how we think they are using them.

Where software development differs, and home building could definitely benefit, is the use of the agile methodology. Agile allows multiple teams to respond to changes, quickly. So how can agile, a method based on frequent, continuous delivery exist with long-term, big-picture planning? Is it possible to create a realistic forecast over a long period of time, knowing that the one constant is change?

As a PM, questions like, "How much time do users spend with the product each day?", "What actions do they take most?", and "Which features get used least?" are incredibly valuable for understanding your users and give us clues as to how to make their experience better. In this post, I'll explain what product analytics are and why you should use them; how to gain a true understanding of your users so you can pay off "empathy debt"; and how to use analytics to help guide new feature development

Let’s get started!

What is Product Analytics?

Product analytics is a robust set of tools that allow product managers and product teams to assess the performance of the digital experiences they build. Product analytics provides critical information to optimize performance, diagnose problems, and correlate customer activity with long-term value.

At its most powerful, product analytics tell you exactly what is happening in your product, giving you the how and when and what and where—and even the who—about your users’ behavior. For product teams, product analytics provides critical information and valuable insights. Who is using your product, and how? Which features they use…and don’t use. Where they experience friction. How to diagnose problems, reduce churn, and personalize interaction for users. And also ways to correlate user behaviors with long-term value.

It is a type of business intelligence software that captures and exposes usage patterns from digital products like web and mobile applications via event tracking, event properties, and event and property grouping. This data informs decisions about how to improve the product experience, increase product engagement, and drive business outcomes. Usage data tends to be more reliable than user surveys and product testing alone.

Product Analytics is the term applied to the automated gathering, analysis, and visualization of data. Most people first get their introduction to analytics through the popular Google Analytics, but there is a variety of analytics software specifically for tracking user actions on digital products. In order to get a quantitative understanding of what users are doing with your product, the first step is instrumenting it with product analytics. The idea is to fire an event for every action that a user can take in your product so you get an aggregated view of how many users use a feature, and how often they're using it. For example, if you want to track the number of times a user clicks a specific button, you might fire an event called "big-red-button.click." From there you can see which features need work, and which are most important, and use that information to prioritize changes.

It’s not just Product Managers who need to implement product analytics. Anyone involved in software development, from engineers to designers, can use data to make more informed choices.

As a Product Manager, you don’t need to be a data scientist, but you do need to be comfortable analyzing and utilizing data. It’ll benefit you not just for your current product, but throughout your entire career.

Being a Data-Driven Product Manager

You may have heard the term ‘data-driven Product Manager‘ thrown around a lot in recent years. But what actually makes a data-driven Product Manager? Essentially, a data-driven Product Manager is one who doesn’t just rely on their instincts, but someone who arms themselves with as many facts as possible.

Now that you’re a data-driven Product Manager, you might be on the lookout for some quick wins! But being dedicated to data doesn’t work like that. It’s more about playing the long game. Investing your time in data is exactly that…an investment.

You need to build product analytics into everything you do right from the start. Being data-driven won’t make you an overnight success. It’s a long-term practice that influences your product decisions and leads you to long-term success.

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.

The shift toward meaningful insights is particularly relevant in multi-app portfolios—especially across platforms and devices—where tracking and correlating a variety of product data dictate the design, functionality, and experiments that drive product strategy and growth. Companies are now reaping the benefits of product analytics not just for the software they create for customers, but for their employee-facing applications as well.

Who Benefits from Product Analytics?

The best part about implementing product analytics is that almost every team can benefit from it. According to Gainsight, Customer Success can use the data to make more proactive recommendations to customers, Marketing can use it to tailor their messaging, and Sales can use it to identify the right time to contact a prospect.