7 product analytics metrics every PM should know.

You're a product manager looking at your analytics dashboard. You see either too many different metrics being tracked or almost none that are relevant to you and your team. Your product is doing well on its own merits, but you know it could reach its full potential with the right adjustments.

In moments like this, the problem is often that, in your endless array of data from different channels and sources, you aren't sure which metrics are closely related to your products' success. In other words: Which metrics should you pay the most attention to, and which ones don’t really matter?

Answering that lucrative question is possible with the right tools. The Quantum Metric Platform is a highly intuitive and in-depth digital analytics software that helps product managers track their most important metrics in real time. Below, we share seven metrics every PM should consider tracking and detail why product analytics metrics matter so much for product managers.

Why product analytics metrics matter to PMs.

Product managers (PMs) are team leaders who ensure that product development and digital marketing efforts align with a company's key business objectives. To do that successfully, they need more than just their gut instinct, immense creativity, and strong organizational skills.

They also need hard, factual data based on user behavior. This is where product analytics metrics come in. These essential metrics help track user behavior, so there's no guesswork about what users think and feel about your product. Then, by analyzing and drawing accurate conclusions from these metrics, product managers can go even further.

Product analytics metrics help determine future development and design.

By seeing users engage with different features of your product, product managers can determine what their team should improve or add. That way, PMs can keep loyal customers via retention optimization of their product, while also adding new elements and launching fresh campaigns to earn new customers.

Using the right essential product metrics also helps PMs avoid wasting resources.

Product managers and their teams must be agile to exceed competitor practices and customers' expectations. Using product analytics metrics allows PMs to make fast, data-driven decisions that prioritize errors and new developments that affect their customers and business the most. With precise product metrics tracking, your product team can stay efficient and proactive. Use our product analytics guide to learn how.

Product managers can use these metrics to bring customers closer to their brand.

These days, customers are looking for smooth, responsive user experiences. PMs use key performance indicators to identify what their users like and where they experience issues when interacting with a website or app. From there, they can direct their teams to test and enhance specific products for improved user experiences.

All in all, keeping up with relevant product analytics metrics is a primary duty for product managers. Considering your PMs often wear many hats at once, you can save time and simplify product development using a product analytics tool like Quantum Metric. Features like our Felix AI help streamline analysis and decision-making, so you always stay on top of your products' performance.

7 key metrics for product managers to track.

Your digital product metrics are key data points that tell how users interact with your product. Analyze them well, and you can discover ways to raise customer satisfaction by fixing friction points in the user journey or improving a popular feature. Below, we share seven of the most significant PM analytics metrics you should track:

1) Daily active users (DAU) and monthly active users (MAU).

DAU and MAU indicate the number of unique users who engage with your product daily and monthly. Together, they can be used to calculate a product's "stickiness" or level of customer retention within a specific time range.

Simply divide the number of daily active users by the number of monthly active users. If the result is equal to or greater than 0.2 or 20%, then your product has a healthy retention rate.

Example: A high-end fashion brand wants to track both DAU and MAU on their eCommerce site to see how many repeat customers it gained during the summer season. The company can then use this information to optimize its site for even more sales next year.

2) Retention rate.

The customer retention rate is measured by the percentage of users who return to use your product within a chosen period of time. As one of the most important retention metrics for products, it ensures your team can reduce customer acquisition cost (CAC), thus improving revenue. Compared to DAU/MAU, this metric is broader and more focused on the long-term.

To calculate your retention rate, measure the number of returning users and divide by the total number of users who interacted with your product. Multiply the remainder by a hundred, and you have your answer.

Example: A subscription-based exercise app’s product team measures the number of returning users after three months. Then, they divide that amount by the total number of users within that period. The team finds that 9,000 out of 11,000 users returned. This means the company had an 81% retention rate, which is decent for a SaaS company.

3) Churn rate.

The churn rate is as rough as it sounds. Churn refers to the percentage of users who stop using your product within a given period. Churn rates are crucial, as they can signal all kinds of warnings for product managers. If they rise quickly, it can mean a technical bug or issues with your product. Reducing even a small amount of churn year over year can also result in a huge boost to lifetime value per customer.

To calculate your churn rate, divide the number of customers lost within a certain period by the total number of customers gained at the start of that period, then multiply by a hundred.

Example: An online game company sees that they have a total monthly player count of around 100,000. However, one month in, it loses 2,000 players due to the new release of a similar game with a fresh setting and better graphics. That results in a 2% churn rate, which is considered relatively low.

4) Conversion rate.

The conversion rate measures the percentage of users who take a desired action when interacting with your product. It is among the most important product performance metrics for product managers, as it helps measure the success of your user engagement strategies. It can also be a strong metric for tracking your company's goals and objectives.

To find your conversion rate, divide the number of customers who perform a desired action by your platform’s total number of visitors or users. Multiply the result by a hundred, and that's your rate of conversion.

Example: An outdoor apparel brand's eCommerce store updated its checkout page in September. They found that the total number of purchases, 2,500, divided by the total number of site visitors, 50,000, resulted in a 5% conversion rate — a successful increase compared to the 2% conversion rate of the previous year.

5) Net promoter score (NPS).

The NPS metric indicates the level of customer loyalty and satisfaction your product earns. NPS, for product managers, is a common form of customer feedback based on a question found on most apps and websites: "On a scale of one to ten, how good was your experience with our product?"

To calculate your product's NPS, simply subtract the percentage of responding users who gave a score between 0 and 6 (called Detractors) from the percentage of users who gave a score between 9 and 10 (called Promoters).

Example: A SaaS company surveyed its users in its most recent quarter. Out of 1000 respondents, 60% were promoters while 15% were detractors. This resulted in an NPS of +45, indicating a highly satisfied user base.

6) Feature usage rate.

The feature usage rate shows how much a particular feature is used, highlighting its high or low popularity. High usage rates are a sure sign to encourage your product team to optimize a great feature further. Low usage allows you to decide to remove (or improve) one or more features, allowing your product and development teams to save time and stay efficient.

Calculate feature usage rates by simply taking the number of unique feature users (the number of people who use a given feature of your product) and dividing them by the total number of unique product users (the number of people who use your product within a given timeframe). Multiply the remainder by a hundred, and you will have your feature usage rate.

Example: A budgeting app adds a new "Dynamic Savings Goal" feature. Out of their 40,000 monthly active users, around 10,000 used the feature most frequently. Calculate the rate by dividing 10,000 by 40,000. Then multiply the remainder by 100, and you get 25% of users who used the new feature. This indicates that the feature could benefit from more iteration or marketing efforts.

7) Average session time.

Average session time gauges the average amount of time a typical user spends interacting with your product. It enables you to identify points of friction within a particular web page or section of your app. Average session time can also differentiate which areas are more effective at captivating a user than others.

To calculate your average session time, divide the total duration of all sessions by the total number of sessions in a certain period of time.

Example: A video streaming service wants to find out the average per-session duration of its users so it can find out how engaging its app is. They find that the average user's total time spent is approximately 2 million minutes, split across 100,000 sessions. This translates to an average of about twenty minutes per user session, which can be considered poor for a streaming service.

Drive effective product management with analytics by Quantum Metric.

Use Quantum Metric for simple yet intuitive product management analytics. Features like session replay, journey analysis, and advanced heat mapping provide key insights into your product's performance. With help from our Felix generative AI, you can get instant summaries of your most vital events (and insights on what to do next).

With Quantum Metric, you'll be able to drive faster and better decision-making, as well as boost key metrics to reach business objective targets with ease. Discover the full capabilities of our digital analytics platform by speaking with our sales team today.