5 Metrics Every Product Manager Should Know

Product metrics, sometimes called key performance indicators, are quantifiable data points that an organization tracks and analyzes to gauge a product’s success. They measure everything from user behavior to market performance. It’s important to note that not all product metrics are created equal. In fact, some are more valuable than others.

For instance, so-called vanity metrics are statistics that look spectacular on the surface but don’t necessarily translate to any meaningful business results. Examples include the number of social media followers your organization has or the number of views on a promotional video.

More valuable are metrics that tie back to the product strategy, ideally as a leading indicator. A leading indicator is a metric that indicates a change in future activity or results. In the case of product metrics, a leading indicator serves as an early warning signal of an impact on revenue. Conversely, a drop in revenue is a lagging indicator that there may be an issue with the product.

In addition to serving as an early warning signal, good product metrics matter because they help you align your product team around common definitions of product success and make better product decisions. Since they aid in gaining executive approval for product initiatives in line with the company strategy, they also set the stage for business success. Furthermore, high quality product metrics serve as the foundation upon which outcome-driven roadmaps are built.

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The full set of product metrics a team may follow will vary based on the stage of the company, the maturity of the product, and the industry in which the product is used. There are, however, 5 metrics that every product manager should track.

  1. Customer Acquisition Cost
  2. Trial to Paid Conversion
  3. Feature Adoption Rate
  4. Customer Retention Rate
  5. Daily/Monthly Users

A few disclaimers on product manager metrics

Before addressing the details of each of these metrics, it’s important to acknowledge that obstacles may exist in the collection of data necessary to calculate these metrics. For example, the product may lack the necessary instrumentation to accurately capture user interactions with specific features. Adding this capability comes with an opportunity cost of engineering cycles, which could be spent adding new features instead. When faced with this dilemma, a product team will have to ask a question of itself. Is it more important to keep adding features without a way to evaluate their success? Or is it paramount to gain the visibility needed to make data-driven decisions? What’s right for any one team will be a matter of prioritization.

Organizational dependencies

Another potential obstacle some product managers may face, depending on the quality of their relationships within and the overall culture of the organization, is the dependency on other departments for data. In a company where transparency across departments is not the norm, teams may be reluctant to share data that could reveal inefficiencies in their own operations to peers in product. For this reason, it’s important for product managers to be able to explain why such data is needed, how it will be used, and how the metrics that will be calculated tie to organizational objectives. Explaining the context for the data request and the mutual benefit of the product success metrics can go a long way toward alleviating concerns other teams may have.

It’s all in the timing

Something else to be aware of when calculating product manager metrics is the timeframe for the data that is included in the calculation. While there can be value in using all data from the beginning of time in some cases, the quantity of data collected before a change in the product can have a blunting effect in terms of understanding the impact of the change. The risk of this effect can be addressed either by using data sets from before and after a change or data only from specified number of days in the past. These so-called rolling averages can give you a better view of what’s happening with the product right now. A change that creates a big swing in a rolling 30-day average may show only a modest impact on a data set from the past 5 years.

Customer Acquisition Cost


CAC=Cost of Sales and Marketing / # of New Customers Acquired

How is it a leading indicator?

CAC serves as a leading indicator by alerting product managers to a potential change in the competitive environment or to the strength of the product’s value proposition. Whereas a rising CAC could signal the need for a reevaluation of a mature product’s differentiation in the market, a falling CAC could be a good indicator of a new product having achieved product-market fit.


A potential challenge comes in gathering a full calculation of the costs incurred by sales and marketing. It’s important to include not just direct expenses like paid advertisements, printed collateral, and conference fees but also indirect expenses like salaries and sales commissions, overhead (office space, benefits), and tools (software and hardware).

When counting new customers, product managers will want to consider their revenue model to determine whether to count paying customers or total users. In a B2B environment if license fees are based on the number of users, counting total users may make sense. With enterprise licenses that allow for an unlimited number of users, counting paying customers/organizations may be a better plan.

Trial to Paid Conversion


Trial to Paid Conversion=# of Paid Signups / # of Free Trial Users

How is it a leading indicator?

Similar to CAC, the Trial to Paid Conversion rate can serve as an indicator of the effectiveness of the product in delivery of the promised value proposition. The CAC is more of an indicator of the strength of messaging about the product; the Trial to Paid Conversion rate is a direct reflection of the product’s value itself. Can it do what was promised?


Again similar to CAC, selection of the right data to use in a B2B environment is important. Counting multiple free trials at a single organization may or may not make sense. The key is to be deliberate, consistent, and transparent about the rules for calculations.

Feature Adoption Rate


Feature Adoption Rate=# of Feature Users / Total # of Users

How is it a leading indicator?

Feature adoption is a valuable metric. It can even represent your team’s North Star, a single metric that best captures the value your product delivers, because it lets you know which aspect of your product’s functionality is genuinely drawing new users and keeping your existing user base loyal to your product.

By analyzing Feature Adoption Rate, product managers may learn that users interact with only a small number of features. This insight could result in a reprioritization of the road map to focus on enhancements to these areas of the product or a re-working of the sales and marketing messages to emphasize this product value.

You can also use Feature Adoption Rate as an indicator in a limited roll-out about whether a feature is ready for release to the entire user population. Slow uptake of a new feature may indicate it’s not yet ready for widespread release.


Different features have different expected frequencies of use. For instance, accounting software may have certain features that are designed to be used daily or multiple times per day. Others may only be used occasionally such as those to help close the books at the end of a month, quarter, or year. Selecting the right time frame for analysis will vary feature by feature.

It may also be important to segment users and adoption rates if you have a product that serves multiple segments. Low adoption of a particular feature by medium-sized enterprises may be of little concern, but the same rate among enterprises may be a red flag.

Customer Retention Rate


Option 1

CRR=(# of Customers at End - # of New Customers Acquired) /
# of Customers at Start

Option 2

CRR=# of Subscription Renewals /
(# of Subscription Renewals + # of Subscription Cancellations)

How is it a leading indicator?

Customer Retention Rate is the all-important metric for subscription-based businesses. According to Bain & Co, a 5% increase in CRR produces more than a 25% increase in profit. For a product team, a falling CRR could be signs of major concern as a signal that a competitor is gaining ground or that an established position is being disrupted, pointing to a potential need for a major overhaul in product strategy.


Product teams may want to consider monitoring not just Customer Retention Rate but also User Retention Rate. In a B2B environment, a decrease in the number of users within a single paying customer could be a harbinger of a contract cancellation that has not yet occurred. By catching such signs early, there may be time to address the problem before it impacts future revenue streams.

Daily/Monthly Users


This metric is a simple count of the # of users of the product or part of the product during a given period of time. You can also enhance this metric a bit by focusing on Daily/Monthly Active Users (DAUs/MAUs). For that variation, calculate how long it should take your user to complete a desired activity in your product, and then measure the number of users (per day or month) spending that time in your product.

How is it a leading indicator?

As with all of these metrics, the value comes not via a snapshot of the value at a single point in time but by monitoring changes in them over time. An increase or decrease in this metric is a good indicator of potential changes in customer retention, loyalty, and lifetime customer value. An accurate picture of your product’s active users—per day or month—can give you a strong sense of whether your product is solving the user problems you built it to solve.


Correctly calculating this metric is key to avoiding the risk of misinterpreting the results. For instance, if you have a strong marketing campaign that is bringing in a large number of new users but the product is not keeping them engaged for a long period of time, a calculation of Daily Active Users may suggest an overly rosy picture. On the other hand, the same calculation could present an incorrectly negative view if your product is not one in which daily use is the expected norm. Or you could have power users who interact with your product multiple times per day but only get counted once.

Final thoughts on product manager metrics

The five metrics above are far from a complete list of measures a product manager may want to monitor. They do represent a solid set with which to begin. Considering the insight these metrics can provide and the cost that can be involved in reengineering an existing product to deliver the granular data that is needed for some of them, teams launching new products are well advised to consider these key metrics from the start. The ability to gather the feature and user-level data some of these require can make the difference between continuously taking shots in the dark and having the ability to shine a bright light on a product’s performance.

With these and other metrics, the key is to make sure that you are implementing them appropriately within your organization for the product you are managing. One size does not fit all when it comes to product metrics.