What is product analytics?

Product analytics refers to the process of analyzing how users engage with your product. It involves collecting data, tracking user actions and product metrics, and uncovering insights that will inform your product decisions.

Product managers are passionate about product analytics. After all, you want to deeply understand your customers. And you want to determine how well your product solves the problems it was designed to fix.

Product analytics focuses on quantitative metrics. For example, you might dig into usage metrics for a set of features or identify friction points in your trial signup process. Analytics helps eliminate guesswork by giving you data-driven reference points to guide your decisions.

Of course, you will still need to gather qualitative feedback — from customer calls, support tickets, and an ideas portal. Together qualitative and quantitative data paint a full picture of your customers' experience.

Why is product analytics important?

You need all the customer input you can get. Product analytics is one of the best (and most efficient) ways to collect customer data across a broad set of users. When you know how users engage with your product, you can make better decisions about how to serve them.

Product analytics is also valuable for measuring product goals. The process of analyzing user engagement can help you identify benchmarks, compare data across time, and determine product gaps that you need to address.

Which teams use product analytics?

As the name implies, product analytics is primarily used by product teams — but it is useful for other teams, too. Cross-functional teammates collaborate with you to build, market, sell, and support your product. So it makes sense that they have a stake in user engagement and want to know more about the value the product provides.

Below are some of the ways that teams across an organization use product analytics:

Customer support	To monitor engagement and inform customer conversations. Product analytics clarify how to help customers use the product more efficiently and explore new functionality.
Engineering	To identify areas of friction. Engineers can hone in on which fixes to prioritize based on the level of user engagement.
Executive team	To determine if product performance is on track to reach business goals. Product metrics are often used as indicators of overall business success (e.g., customer retention and revenue).
Product management	To make decisions about the product roadmap — from product strategy to feature prioritization. Quantitative data enriches your understanding of who your customers are and how they interact with your product.
Product marketing	To better understand customers and how they act. Product marketing teams observe engagement trends by segmenting customers into groups based on common characteristics (like demographics or in-app behavior).
UX and design	To identify areas of the user experience that can be improved. UX managers pinpoint whether desired outcomes are being achieved by observing user actions like clicks and pageviews.

What is the difference between product analytics and product metrics?

Up to this point, we have been using the term product analytics to refer to the discipline of analyzing user engagement. Product metrics are the particular data measurements that you capture during your analysis.

The metrics you choose will vary based on your strategic planning process, industry, company size, and product type. Reliable metrics also hinge on available technology and tools. For instance, product teams increasingly rely on third-party analytics tools to capture user data.

When selecting metrics, start goal-first. Assign a success metric to each product goal that you are targeting. That way your data is tied to meaningful outcomes. Outside of the metrics attached to your goals — often referred to as key performance indicators (KPIs) — you will likely track more detailed metrics as well. A comprehensive view of product and team performance gives you greater context when making decisions.

What are the most common types of product metrics?

Ideally you have all sorts of options when it comes to measuring product success. Now you just need to identify which metrics will be useful for your team. In general, product metrics can be grouped into the following categories:

Business metrics: Data about company performance.
Product usage metrics: Usage data that illuminates how users interact with your product or offering.
Customer satisfaction: Metrics that help you understand whether customers are happy with their overall experience.
Roadmap progress: Data about how the product team is progressing against the release timeline.

20+ examples of product metrics you should track

Below are some examples of the most commonly tracked product metrics in each category to help you get started. If these metrics do not fit your product or offering, use these examples as inspiration to identify others that will suit your needs better.

Business metrics

With a SaaS product, it is important to understand how you are attracting, converting, and retaining paid customers. Consider tracking the following business and marketing metrics:

Annual recurring revenue (ARR): Annual value of recurring revenue from all customers (excluding one-time fees and variable fees).
Churn: Percentage of customers that you lose during a certain time period, typically calculated monthly and annually.
Conversion rate to trial account: Rate at which website visitors sign up for a free product trial.
Conversion rate to paid account: Rate at which trial users convert to a paid account.
Customer acquisition cost (CAC): Average cost associated with capturing a new customer.
Customer lifetime value (LTV): Average revenue from customers over the life of the relationship.
Monthly recurring revenue (MRR): Recurring revenue at the end of a given month — including new and existing customers.
Website visitors: Number of unique and repeat visitors to your website each month.

Product usage metrics

Understanding how people are actually using your product is crucial for identifying which areas need attention. Commonly tracked product metrics that demonstrate product health and engagement include:

Daily active users (DAU) or monthly active users (MAU): Number of customers who use your product on a daily or monthly basis.
Feature usage: Number or percentage of customers who use a certain feature or set of features.
Session length: Amount of time a customer spends using your product in one session.
Sessions per user: Frequency of sessions per customer in a given time frame.

Customer satisfaction metrics

There are few things as validating as hearing your customers rave about your product. If you want to achieve long-term success, you need to earn real love and loyalty from customers. Metrics that indicate customer satisfaction include:

Customer satisfaction score (CSAT): Rating of satisfaction with a product or company on a one to five scale (determined by a customer survey).
Net promoter score (NPS): Likelihood of your customers recommending your product or company to others (determined by a customer survey).
Referrals: Number of new customers acquired based on a recommendation or introduction from another happy customer.
Retention: Rate at which customers keep and renew their accounts.
Viral coefficient: Average conversion rate of new customers invited by an existing customer.

Roadmap progress metrics

In addition to metrics based on user actions, product managers need to evaluate work in progress to meet deadlines. Roadmap- and progress-related metrics include:

Features shipped: Number of new features released to customers in a given time period.
Sprint burndown: How the team is progressing towards completing all planned work in a sprint.
Velocity: Rate of progress or average amount of work completed in a given sprint or time frame.
Work-in-progress: Current features and tasks that are actively being worked on.

How do product analytics tools work?

Product analytics tools capture data by tracking user actions within your product or website. You can extract insights from this data by using different methods of analysis. Here are some common examples of product analytics tool capabilities:

Attribution analysis: Analyzing customer touch points (e.g., demos, sales conversations, website visits) that lead to purchase.
Churn analysis: Examining your customer loss rate in an effort to understand what causes customers to leave.
Cohort analysis: Measuring behavioral patterns over time by separating users into related groups or cohorts.
Conversion analysis: Determining if customers are completing the desired conversion actions (e.g., signing up for a trial) or discovering where they drop off.
Funnel analysis: Mapping your customers' journey through different stages that lead to a goal. This helps you understand points of friction or churn.
Retention analysis: Understanding the factors that entice your customers to stay (the inverse of churn analysis).
Segmentation: Dividing your users into groups based on demographics, behavior, persona, and more to uncover deeper insights.

How to use product analytics to make better product decisions

Numbers alone cannot tell the full story — but they will help you spot trends and answer critical questions about your product.

For example, if a conversion analysis shows that your paid account conversion rate is decreasing, do not immediately conclude that no one wants to buy your product. Dig into the customer journey — what is the pathway to becoming a paid user? Where might users experience friction along that path? When you look more closely, you will uncover opportunities to improve particular features or things like your pricing structure.

The table below includes questions to help spark your discovery and explore the meaning behind your product metrics. Of course, you will want to supplement your analysis with questions of your own.

Attracting and acquiring customers	How do people find out about our product? Is each section of our website engaging and accurate? Is our product positioning and messaging clear? Is our trial signup process streamlined?
Converting to paid customers	Is it easy for trial users to convert to paid customers? Are there specific product features that drive paid signups? Where do trial signups fall off in their product usage? How long does it take for trials to convert to paid accounts?
Product usage and customer retention	What are the most and least popular product features? How long do users spend in our application? How long does it take users to complete specific tasks? Do customers expand their usage or add new users to their accounts over time?
Team efficiency	What is slowing down our release process? Is our feature prioritization framework clear and easy to follow? Has team velocity improved or worsened in the last six months? How can we improve team or individual capacity?

Note that you should always complement your quantitative data with qualitative discovery. This helps you get closer to the "why" behind product metrics and reveals truths that your data alone may not have uncovered.

Try speaking directly to customers at different parts of the customer journey and collect qualitative data in the form of interviews, ideas portals, empathy sessions, and surveys. With both product analytics and customer feedback in hand, you will be well-equipped to make meaningful improvements to your product.

When to invest in a product analytics tool

Because it is your primary tool for collecting, understanding, and visualizing all of your product data, it is a good idea to invest in product analytics whenever you have a viable product. The sooner you invest in product analytics, the more it will benefit you as your company grows — from informing your very first product launch to helping you retain important customers.

No matter which product analytics tool you choose, the overall objective should be the same — improve your product and customer experience. This means thinking broadly about what you are building and zeroing in on the data that brings the greatest insights. This is the mindset that leads to creating a product that customers love.

Frequently asked questions about product analytics

What is a user action?

A user action is an interaction with your product or website interface. Some examples are clicks, pageviews, and signups. Product analytics tools track user actions to help you understand how customers engage with your product.

Is product analytics the same as marketing analytics?

While product managers and marketing managers may use both, product analytics and marketing analytics are not the same. Product analytics examines user interactions with your product and marketing analytics tracks engagement with marketing campaigns and activities.

How do I choose the right product analytics tool for my needs?

There are many product analytics tools on the market. Consider the product metrics you want to track, the types of analysis you intend to perform, integrations you need, and budget you have to help you choose the best tool for your product and team.

You can set product strategy, gather customer feedback, and report on roadmap progress — all in one place. Try Aha! Roadmaps for free for 30 days as a companion to your product analytics tool.

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