Metrics that Matter

Company after company quips that their teams “make data-driven decisions”. But when you ask them which metrics they are currently tracking, they typically respond with Daily Active Users (DAU) and downloads. Both of these metrics are very popular because they make it easy for companies to compare themselves to their competitors (a key fundraising tactic for startups) and because they are super easy to track. Simply add Google Analytics to your product, log into their pre-made dashboard, and find your answers instantly! Unfortunately, it’s impossible to make any product decisions based solely on these metrics because they’re vanity metrics. If we are going to make “data-driven decisions,” we need to analyze metrics that will help us make decisions; we need actionable metrics.

There are three types of metrics product managers should care about:


Simply tracking the number of users is not enough. Rather, it’s important to take a more nuanced look at your users. A detailed look at your users will make it harder to compare your company to others, but it will give you more important information about what to do next with your product. The metric I suggest most often is repeat usage. Repeat usage of your product can tell you a lot about where you need to dig with your user interviews. If users are not coming back to your product, you know you have a market fit problem. Either you haven’t found your market yet, or you are not solving a big enough problem for the market you found. So when it comes to making a decision about what to do next, building on new features or doing small optimizations are probably not going to get you anywhere. You should instead go back and validate your value proposition and ensure that you are both meeting it with your existing product and that you have identified a customer segment that will benefit from what you’ve built.

Another way to look at repeat usage is by measuring your retention rate. This metric can be calculated in many different ways, but the most common method in analytics applications is rolling retention. Rolling retention tells you whether or not users have returned to your product in a given time period. (If you want to learn how to calculate retention, this post at AppLift explains it very simply.) Retention is an excellent metric to use once you have an established product because you’re most likely gaining and losing a lot of users each month. By using retention along with cohort analysis, you can determine whether your newer users are returning to your product, rather than mixing them into the same data as your established users.


Conversion metrics are the most versatile and powerful for making day-to-day decisions. A conversion is the number or percentage of people who complete a given task within your product. The most impactful conversion metrics are based on the value proposition of your product. What did you promise your users? What did you tell them they would be able to achieve with your product? That is the conversion you should be tracking.

If we look at some examples of popular products and their value propositions, we can deduce what some of their conversion metrics might be. Evernote, for example, has put forth this value proposition: “Get organized. Work smarter. Remember everything.” One of their conversion metrics might be as simple as “a note created.” But if they find that users who create five or more notes per day use more tags to stay organized and thus stay paid customers longer, they might make their conversions both the creation of the fifth note and that tags were added.

That brings us to funnels. Funnels are a popular way of using conversion metrics. With a funnel, you look at how many users continued through a series of actions, like adding notes in Evernote. By examining which actions users complete before and after meaningful conversions, you can determine which actions in your product lead to more engaged users or more revenue. You can then find other ways within your product to encourage users down those paths.


At the end of the day, your company and product only exist if people buy your product. Simply tracking total revenue, however, is way too broad a metric for making product decisions. A better way for a product team to approach revenue is to instead measure the amount of each sale, or the revenue generated from a particular group of users. People have many different reasons for why and how they spend their money. By focusing on segments rather than the aggregate, you can isolate different groups’ motivations for buying and modify your product to provide them with something that they feel is worth spending their money on.


Everyone loves to say they are data-driven. What people really mean when they say that is that they want to use data to help them make decisions. To do that, product people need to focus on tracking actionable metrics and leave the vanity metrics for press releases and board review slide decks.


Originally written for the blog on L4