Product Analytics: What It Is, Why You Need It, and How to Use It

Data about your customers is everywhere. And collecting it is one thing, but understanding it is another. Product analytics can help you do just that.

Product analytics takes raw data and turns it into something helpful. It tells you about your customers—how they interact with your website or app, what steps they take in a purchase funnel, whether they are retaining or churning. It can be easy to get lost in endless metrics and jargon, but we’re here to break down what product analytics is, why you need, and how to get started using it.

Product Analytics is about scaling customer empathy by understanding exactly how users interact with your product. Whether you make a SaaS product or consumer goods, there is a digital trace of that behavior. Product analytics involves tracking that behavior and arranging it in a way that makes sense.

Here at Amplitude, we’ve been leading the conversation about product analytics for years. As we’ve noted before, product analytics needs to be real-time, collaborative and self-serve in order to align product vision during rapid cycles of iteration. It needs to be easy to use in order to create that elusive data culture.

The point of product analytics is to get beyond the numbers—past vanity metrics like page views or downloads. Instead, product analytics gives you a window into the very human relationships that people have with your product. It can show you how your product fits into people’s everyday lives, what value they get out of it, what needs it meets, and where it falls short.

You need product analytics because it can give you a deep understanding of your customers. And the better you understand your customers, the better you can meet their needs. The better you meet your customers’ needs, the more your business will grow. According to a survey of 331 executives by Forbes magazine, data-informed companies are “six times more likely to be profitable year-over-year.” Seventy-five percent see increased engagement, and 53% say data allows them to be more consumer-centric.

Product analytics enables your team to dig into the data and perform analyses, like a cohort analysis. Cohort analysis is the practice of grouping users by a particular trait. That could be where they logged in, when they signed up (acquisition date), or whether they took a specific action or sequence of actions. With product analytics, you can also layer user behavior into your approach to cohorting. The benefit of cohort analysis is that it reveals how different people use your product in different ways. That might sound obvious, but you wouldn’t be able to reveal this information without product analytics. With it, you can adapt your product strategy to add value and find unique solutions for each cohort.

Product analytics can give you insights that are hard to gather from qualitative data—sources such as customer surveys, support tickets, or focus groups. People aren’t always the most reliable narrators. While direct feedback from customers can be very useful, it should be taken in context with data analytics. Otherwise, it is easy to fall into the trap of addressing the customer feedback that you heard most recently rather than what is urgent or part of a larger trend.

Product analytics relies on quantitative data. It processes large-scale numbers of events across all of your customers. Product analytics tells you about your customers’ actual behavior and actions rather than their individual perceptions. After capturing all that data, product analytics gives you information to solve business challenges.

Product analytics can answer important questions

Businesses usually have plenty of questions about their customers and how their product is performing. They may even have decent instincts about the answers to those questions. But product analytics uses data to give a concrete answer to those important questions, such as:

• Who are my most valuable customers?
• What actions do those valuable customers take?
• Who are the customers that churn?
• What actions do churned users take?
• What are the characteristics of highly engaged users?
• What are my users’ pain points?

Product analytics allows you to test your assumptions and see whether your intuition has led you astray. The answers to these questions will give you areas to test and improve your product, leading to increased retention and lifetime value.

There are lots of metrics you can track as part of your product analytics method, such as lifetime value (LTV), monthly recurring revenue (MRR), and cost of acquisition (CAC). But the numbers alone are not enough. Numbers alone don’t give you information you can act on. Clicks and page views are meaningless unless you understand how they contribute to your product’s overall value. You should use metrics as a window into the human experience of your product—your customers’ goals and needs. Frameworks give your metrics context.

Here are some frameworks to help you choose and understand what metrics are relevant to your business.

Pirate Metrics (AARRR)

Dave McClure of 500 Startups and PayPal introduced the concept of pirate metrics in 2007 to get founders to think about metrics as a reflection of the customer experience at five points in the customer journey:

• Acquisition: People visit your website or app for the first time.
• Activation: Users have their first valuable experience with your product.
• Retention: Customers come back and continue using your product.
• Referral: They like it enough to tell their networks.
• Revenue: People take actions that make you money.

What metrics are relevant to each category will be unique to your business. As an example, let’s look at hypothetical pirate metrics for a music-streaming service use case:

Pirate Metrics for Music-Streaming Service

The temptation will always be to slide into vanity metrics—metrics that make you look good but don’t actually reflect the health of your product or company. Instead, focus on making sure your metrics are actionable.

The Google Heart Framework

Google developed the HEART framework as a way to organize metrics around the user experience. The acronym stands for:

1. Happiness: Users feel good about your product, and they experience value.
2. Engagement: People interact with your product in meaningful ways.
3. Adoption: Customers try new features, or new users complete key actions for the first time.
4. Retention: Users continue to come back to your product over time.
5. Task Success: People complete important events without problems.

To keep your metrics relevant, Google recommends tying them to goals with the following matrix:

HEART Matrix for Music-Streaming Service

For this hypothetical music-streaming service, the metrics are directly related to product and UX goals, keeping you focused on the signals that matter.

North Star Framework

The North Star Framework organizes your product team around one key metric, the “North Star Metric.” That metric should encapsulate three things:

1. A measure of customer value
2. A representation of your product strategy
3. A leading indicator of revenue

Some examples: Netflix’s North Star Metric is monthly retention. It indicates that customers like the product enough to keep using it, it’s directly tied to their revenue model, and it is at the core of their product strategy. Facebook’s early North Star Metric was the number of users who added seven friends within their first 10 days on the platform.

North Stars can change. LinkedIn abandoned their North Star Metric for the endorsements feature when they discovered that it didn’t improve the product experience. At Amplitude, we shifted our North Star from “weekly querying users” (WQU) to “weekly learning users” (WLU) to better reflect the core value we wanted our customers to get out of our product.

Yes, some teams may have more than one North Star, but the point is to keep your team focused and accountable to an outcome. More than two or three North Stars and you risk distraction. Get started on finding your company’s North Star Metric by running a workshop.

So, now you should understand what product analytics is, why it’s important, who does it, and how to think about metrics. You’re ready to get started.

Step 1: Use a Product Analytics Platform

First, you need to choose a product analytics platform. Whether it’s Amplitude or another tool, look for a service that has clean data UX: the visualizations are easy to understand, you can view data in different ways, nontechnical users can access and understand it, it empowers collaboration through commenting or other features, and it integrates with multiple data sources.

Step 2: Identify Your User Flow

A user flow is the set of steps one of your users goes through in order to complete an action. Think about your own product and how people use it. If you’re an ecommerce business, it may be your purchase flow. A mobile game company might focus on the onboarding tutorial. Try choosing a key action you want your users to do, and then work backward.

Step 3: Set KPIs

Key performance indicators (KPIs) are metrics that will reflect whether you are accomplishing your goals. Think of a few business goals that you want to examine with product analytics, and then a couple of KPIs to go with them. Those KPIs could also be a question that you want product analytics to help you answer.

For example, the business goal for a social media platform may be to increase month-1 retention. The question and KPI you could look at: how does sharing N pieces of content correlate with retention? Or, if you’re a B2B SaaS business, you may want to increase revenue through recurring subscriptions. You could track whether increasing the number of users per account correlates to increased revenue per account.

Step 4: Create an Event Taxonomy

The event taxonomy is how you want to name and record the events in your user flow and the KPIs within your product analytics platform. It should capture the event types, event properties, and user properties you need to record. The important thing here is to have a clear and unique naming structure that everyone agrees on and understands. For example, when do you count a user in a “sign-up”? When they hit a landing page button or when the user record is created in your database? Set these definitions beforehand to avoid confusion.

Step 5: Get the Data Flowing

Now you can connect your data sources to your product analytics platform. This is typically done through directly injecting code into your website or product or through an integration partner (like a CDP) that collects data from multiple sources and sends it to Amplitude or another product analytics platform. Integrating with multiple tools lets you gather even more insights. For example, integrating with an engagement tool can let you see how push notifications impact user activity. An attribution integration can show you if users from different acquisition channels tend to have a higher lifetime value.

Once the data is flowing, you’re on your way.

Now you should have a good understanding of what products analytics is, why you need it, how to choose your metrics, and how to get started. As you implement product analytics across your organization, you’ll begin to see a flywheel effect—the customer insights you glean from product analytics will lead to improvements in your product. Product improvements will lead to new questions about your customers and new insights. New insights lead to more improvements. Each turn of the wheel powers the next. Now you’re on track to success.

Put Amplitude’s Product Intelligence platform to work for your product