Choosing the right metrics and analysis methods to track them can make the difference between delivering an OK product and delivering a great product.
In this guide, we’ll give an overview of how product teams define KPIs and use data to measure the success and health of their product. We’ll list some specific metrics product managers commonly track and describe several types of analysis your team can use to pull actionable insights from that data.
Finally, we’ll offer some tips to help you determine what metrics your team should be collecting and select the right type of tool to help you get the most out of your product analytics.
Table of contents
- What is product analytics?
- Why is product analytics essential?
- How do product managers define KPIs?
- How do product managers use product metrics?
- What are AARRR (pirate) metrics?
- How to gain insight from product analytics
- Choosing the right tools to track product metrics
What is product analytics?
Product analytics refers to a set of tools and techniques that reveal details about how users are interacting and responding to your product. Objective data about users’ actual behavior is incredibly valuable to a product team. Tracking these metrics can answer questions such as which features customers are using most, how long they are using a feature, what makes them stop using it, etc.
Without product analytics, your product team would miss out on meaningful insight into its customers. While surveys and customer interviews can provide a glimpse into a customer’s perception of the product, behavioral data can reveal trends and missed opportunities.
Why is product analytics essential?
When building a product, conflicting opinions tend to disrupt the development process. By providing hard data without emotional bias, you can ensure your product team is building features that appeal to users while still maintaining business goals.
Product analytics is also crucial to building a loyal customer base. It’s important to understand why your customers choose your product over your competitors. Data can provide insight into what makes people convert into customers and what features they use most frequently. Organizations can take this data and try to replicate it with other users to increase their conversion rate.
On the other hand, you can also use product analytics to determine why a user didn’t complete a process by revealing where they may have become frustrated and dropped out of the funnel.
How do product managers define KPIs?
Before you can define your KPIs and determine which metrics to analyze, you need to establish a desired outcome or goal. There’s no point in gathering data if you don’t have a plan on how to use that data.
By creating list a of questions first, you can make sure your team is collecting the most relevant data without wasting time or resources.
Some examples of questions you might ask to help narrow down which KPIs, metrics, and events to analyze:
- Why are users not completing the funnel?
- What actions within a journey led to a conversion?
- How often are users engaging with the product?
- What actions did a free trial user complete to become a paid user?
How do product managers use product metrics?
Tracking product metrics comes in handy when formulating a hypothesis or goal for testing a new beta feature. For example, you might hypothesize that adding a “Help” link to a checkout page will increase sales. This would prompt your team to start gathering data and performing analysis on events related to checking out items from the shopping cart.
Because this data has a purpose, it can actually help the product team generate actionable insights rather than waste time collecting disparate data points that are filed away and seldom referenced again.
What are AARRR (pirate) metrics?
There are numerous frameworks designed to help product managers and their teams focus on goals and decide which metrics to measure. One of the most popular product analytics frameworks is called AARRR or pirate metrics.
The AARRR framework follows the entire customer journey and demonstrates which user behavior metrics are analyzed for each stage. This helps product managers evaluate whether a product is meeting user needs throughout the customer journey.
The steps to the AARRR framework are as follows.
Acquisition
Acquisition focuses on where people are discovering a product. This is usually a summary of the different marketing channels and their effectiveness.
Some insights you can gain from tracking acquisition metrics include:
- How long do people stay on a page?
- What do they click?
- Which pages do they view?
- Which marketing channels are most engaging?
Activation
Activation refers to how many people have a happy first experience with a product and indicate they want to continue engaging with it.
Some user behavior metrics to monitor include:
- Do users tend to create an account?
- What events led to a conversion?
- Which pages or features do they interact with?
- How long do they spend on each page or feature?
Retention
Retention describes how long users stay engaged with a product or app over time.
Below are some examples of user behavior metrics to help you determine retention:
- Daily/weekly/monthly active users
- Clickthrough rate of email newsletters
- Which features are most used and which are not
- What events lead to users closing the app
Referral
It’s always valuable to track referrals to gauge customer satisfaction because happy customers are more likely to share or recommend your product.
Some metrics to monitor for insight into referrals include:
- Number of active users sharing invites
- Clickthrough rate of referral links
- Conversion rate of referral links
- What events lead to conversions?
Revenue
As a product manager, it’s your responsibility to prove that features are or will be profitable.
Some product analytics software can create revenue insights for funnels to pinpoint critical areas. Otherwise, you can look at metrics that answer questions such as:
- What is the average revenue per user?
- What is the customer acquisition cost?
- What is the customer lifetime value?
- What is the conversion rate of marketing campaigns?
Once you’ve identified which AARRR stage and metrics you want to analyze, product teams can then implement tools to collect data. The next step is to analyze the data and make necessary adjustments to improve the product and marketing strategies.
How to gain insight from product analytics
It’s one thing to track product metrics, but it’s an entirely different skill to glean meaningful and actionable insights from the resultant data. This is where data analysis comes into play.
The most relevant type of analysis depends on on the kind of answers your team needs. Let’s review some common types of product analysis you can use to measure and understand the product data you collect.
Trends analysis
A trends analysis evaluates how certain features perform over time. The main goal of a trends analysis is to determine whether the adoption rate is increasing or decreasing. This can help you decide whether a product feature needs adjustments.
For example, many tools will reveal the exact click action that caused a person to stop using your product. This type of data can show whether an action is an isolated incident or a trend among users.
By noticing trends such as rage clicks (when a user repeatedly clicks on an element, indicating frustration), you can remedy the issue to improve the user experience.
Path analysis
A path analysis evaluates the steps a user takes to complete a journey. Some examples might include the making a purchase or submitting a form.
By evaluating the user journey, you can see where users dropped out of the desired funnel and how successful the conversion rate was for the funnel.
Attribution analysis
Attribution analysis evaluates the users who completed the expected journey and identifies what attributes contributed to the successful completion. This can reveal positive insights into what is influencing users to continue to use the product.
For example, attribution analysis might reveal that a “Help” button has helped increase the user conversion rate.
Cohort analysis
A cohort analysis groups together users with similar characteristics and analyzes their behavior. This data can help you identify, among other things, what high-value customers are doing on the platform, how a newer cohort responded to a customer service experience, etc.
You can also group users by browser or device. This can help you discover and resolve inconsistencies in the user experience between customers who use your product on a mobile device versus those who are using a laptop.
Retention analysis
Retention analysis focuses on users who continually use or engage with your product over time. Metrics such as customer satisfaction rate, active users, or feedback surveys can help you determine what makes a user continue to use your product.
Retention analysis also evaluates the churn rate, which is the rate at which users stop using your product.
Funnel analysis
Last, but not least, a funnel analysis evaluates the entire funnel and helps you identify areas for improvement.
The first step is to analyze user journeys that failed to complete the funnel.
For example, let’s say only 27 percent of users moved to the next step in the funnel. This rate obviously has the potential to grow, so you might decide to analyze video playback, or sessions, of users who didn’t complete the funnel.
You may also evaluate the funnel insights provided, which will delve into possible reasons for a lost conversion, such as rage clicks, dead clicks, exceptions, and network errors.
Choosing the right tools to track product metrics
Product teams often need to use more than one tool to gather and analyze data. That’s because it’s difficult to find a singular data analytics tool that can produce every report and chart a product team requires.
When choosing product analytics software, ask yourself:
- Does this tool integrate with other tools the product management team uses?
- Is the user interface easy for team members to use?
- Does it generate actionable reports?
- Can the team track custom events?
- Does it cover essential product metrics?
- Is it cost-effective?
The most popular and widely used data analytics tools help product managers and teams accomplish two tasks:
- Gather relevant data
- Produce actionable insights
Product managers frequently invest in multiple tools to gain access to all of the data they want to monitor and evaluate.
Begin your search by determining what questions they need to answer. This will help you discern which tools have the necessary features to track and analyze the data you need to keep your users happy, gain new customers, and grow your product and business.
Quantitative product data is crucial to understanding user behavior. Once you have obtained statistically significant data, your product team can use this knowledge to improve the user experience, reduce churn rate, and generate ROI.
It all begins with choosing the right tools and monitoring the most relevant metrics.
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