12 product metrics to track for success

Data visibility is critical to product-led growth, but deciding which product metrics to track can be a time-consuming process.

If you track too few metrics, you end up with an incomplete picture of your product and customers. However, if you track too many metrics without structure, you end up with information overload.

In this guide, we provide a list of the key product metrics all organizations should track to support sustainable growth. We also explore the why behind each of these metrics to help you build more strategic processes. Lastly, we look at how you can build a complete picture of both in-product and out-of-product data with Optimizely Warehouse-Native Analytics.

Put simply, product metrics allow you to make better decisions. They allow you to continually adjust your strategy throughout the product life cycle to drive sustainable growth.

In the early stages of product development, this might look like testing product hypotheses more effectively and identifying potential issues before they escalate. Then as you start to see more engagement, you could start looking at introducing new features that resonate with your audience.

With the right approach to product metrics, you can adapt to market changes and user issues proactively. And with studies showing that 67% of churn is preventable if issues are resolved early on, it’s clear how important this is.

Conducting real-time analysis allows you to adapt to market changes and user issues more quickly. However, tracking behavioral analytics across channels is key to building a more complete picture.

Without integrating out-of-product analytics alongside your product metrics, you might miss out on crucial insights. For example, you might experience a sudden drop in daily users that cannot be explained with product data alone but can be understood with insights from customer service.

In this way, product metrics and customer insights are also essential for uniting teams towards shared goals. They provide a common language through which you can align departments and engage stakeholders.

With so much data available across different channels, it’s crucial to find the insights that align with your specific product vision and growth plan.

In your initial planning, it’s important to be aware that some metrics might seem important at first, but actually result in little momentum or action. These are considered to be vanity metrics.

One common example of this is time on page. Taken in isolation and without context, a high time on page could mean that a viewer was engaged by your website or that they left their monitor on and it went idle.

The key to deciding which product metrics to track is to choose a product analytics platform (such as Optimizely Warehouse-Native Analytics) that makes it easy for you to:

The AARRR “pirate” metrics model is the most common within product analytics. And though you can experiment with other frameworks such as Dave McClure’s AARRR model to focus more closely on retention metrics, AARRR provides a solid foundation for most PLG companies.

Representing each stage of the user journey, the letters stand for:

Throughout the next section, we’ll explore which metrics you should track in each category and what impact tracking these could have on your product engagement and growth. We’ll also look at how you can track these product metrics.

Acquisition metrics give you insights about when someone first starts using your product. They help you understand how effectively you're attracting new users to your product and which channels are most successful in driving traffic.

One of the most important product metrics for all software organizations, this is the rate at which new users are onboarded onto your product. Calculating acquisition rate alongside attribution-related marketing analysis can show you which channels are most effective in your campaigns.

To calculate: Number of users acquired / specified time period

This is the average amount of money your company spends to acquire a new customer. Monitoring this allows you to assess the efficiency of your marketing and sales efforts. You can also benchmark these against the industry standards posted online.

To calculate: Total cost (marketing + sales) / Number of new customers

Activation metrics are used to measure the stage at which users are deriving value from your product; acquisition typically refers to a sign-up or conversion event.

DAU and MAU are the key engagement metrics indicating how your user base is growing and how they respond to improvements you make to the product. It looks at how many active users you have on a daily or monthly basis.

You will need to establish what engagement looks like and pair this with specific insights into user behavior before you can track DAU/MAU meaningfully.

To calculate: Total MAU= Unique active users in one month + returning users in one month

Total DAU= Unique active users in one day + returning users in one day

Average MAU/Annual MAU= Sum of each month’s active users / 12

This measures how well you are increasing the number of active users in your product by looking at how many people complete a certain milestone during the onboarding process. A social media platform could analyze how many people add their first friend, for example.

Activation rate acts as a growth lever, helping you understand how to improve your retention. You can also benchmark against industry standards.

To calculate: Number of users who completed the milestone / total number of users x 100

This is the window of time between a user signing up for your product and completing their first activation milestone. By understanding this alongside mapping the customer journey, you can identify issues that are preventing users from seeing the value of your product. This might be an issue during onboarding, for example.

To calculate: Activation timestamp − sign-up timestamp

Feature adoption measures interaction with a specific feature rather than with your product as a whole. This helps you to understand how much value you are adding to your users; ultimately it helps you boost retention. The more features a user adopts, the less likely they are to leave your product altogether.

To calculate: Number of customers who adopt feature / number of total customers) x 100

Retention metrics give you insight into your ability to keep users engaged over a longer period of time. You can combine this with qualitative data in the form of feedback from your users to iterate and improve.

Retention rate forms the baseline for understanding how many users continue to use your product over a certain period of time. From here, using an analytics platform like Optimizely Warehouse-Native Analytics, you can build out more detailed reports to identify where they are dropping off.

To calculate: Number of users at the end of the time period - number of users acquired during the time period/ total number of users at the beginning of the period

This allows you to understand how many users leave your product during a particular period. By doing so, you can benchmark against industry rates and identify whether you might have a problem with high churn.

From here, you can undertake churn analysis. This will typically include segmenting users who aren’t engaging as much with your product, predicting future churn based on previous patterns, and testing strategies to reduce it.

To calculate: Number of customers who left during a period / Total number of customers at start of period x 100

Referral metrics relate to your ability to encourage existing customers to refer new ones. This can help you understand how effective your product is at supporting customers by illustrating how loyal they are.

By tracking referral events and sources, you can understand how many of your total sales or acquisitions have been made by referrals. Inside Optimizely Warehouse-Native Analytics, you can take this a step further and understand the drivers of your referrals.

To calculate: Number of referred purchases / number of total purchases x 100

This is the percentage of referred leads that become customers. Understanding this metric allows you to optimize your referral sources.

To calculate: Number of referred leads converted / total number of referred leads) x 100

Revenue is the ultimate goal of the AARRR model of metrics, outlining the financial success of your product.

This is the amount of money an average customer spends during their entire relationship with you. Understanding this helps you learn how much you should pay to attract customers and when analyzed on an individual level it can enable you to identify your most loyal users.

To calculate: CLTV = Average order value x purchase frequency x customer lifespan

This metric is the total amount of revenue your product generates each month. Particularly for subscription-based companies, it’s crucial for gaining insights into growth trajectory. It’s also the springboard from which you’ll dig deeper to understand revenue trends such as fluctuations in subscription sign-ups, upgrades, downgrades, and cancellations.

To calculate: Number of customers x amount each customer pays per month

With the right product analytics platform, you can utilize product metrics to power sustainable growth.

Optimizely Warehouse-Native Analytics allows you to get a 360-degree view of user behavior, both inside and outside of your product, building a complete understanding of the customer journey to pinpoint improvements and potential issues. In one platform, you can understand revenue and retention at the account level, freely explore answers to queries, and flexibly model any entity and relationship.