How to choose product metrics that help you make decisions
Today’s the day. You’re presenting your product in a high-stakes business meeting. You have slides covering all of the ways your product improves your customers’ lives. You start walking through the deck, and a few minutes in, a throat clears, and a voice rings out: "This is great and all, but what impact does it have on the business or our customers?"
To answer this, you need product metrics. Better yet, you need the right product metrics. The key is to pick metrics based on the problem you’re trying to solve. And different stakeholders will find value in different metrics. A product manager would love to hear about feature adoption rates, while the finance team would appreciate a focus on revenue metrics.
In this guide, we’ll break down what product metrics are, how to choose the right ones, and how to avoid common pitfalls that can lead you astray. Examples included!
Product metrics measure how a product or feature is performing. They show you what’s working with a product, what’s not, and where to dig deeper. Now, here’s where people get tripped up:
Product metrics ≠ KPIs.
KPIs (Key Performance Indicators) are tied to specific business objectives. You should proactively monitor them to achieve your goals and track the overall success of the business or a specific initiative.
Product metrics, on the other hand, are more granular and focus on feature usage and the performance of a particular product. Unlike KPIs, you don’t need to check product metrics every day, but they’re essential when reviewing performance or planning the next move.
Product metrics drive better product decisions. Zooming in on how a specific feature or product is performing on a monthly or quarterly basis helps you identify what’s working and what needs fixing in a product.
And here’s the kicker: these metrics don’t just uncover problems. They help you solve them. They show where users are getting stuck, what features they love, and where your processes could use a little elbow grease.
These metrics also back up your product strategy during product presentations. Let’s say you’ve launched a new product or feature. Instead of relying on gut feelings, you can walk into that meeting with data-driven insights like engagement metrics, conversion rates, and retention data that tell the whole story. It’s your strategy’s strongest sidekick and your best defense in a room full of questions.
Metrics take a ton of weight off the shoulders of your product managers, too. By tracking customer behavior and feedback across products, they can prioritize what actually matters, create a competitive product roadmap, and allocate resources where they’ll make the biggest impact.
Different metrics tell different stories about your users, your features, and how sticky your product really is. Let’s break down a few key types, starting with the crowd favorite: user engagement.
When people stick around and interact with your product, it means they’re finding real value, and that’s your ticket to higher retention and happier customers. Here are some example metrics to measure this:
Average session duration: This tells you how long users spend on your product or service. Think of it like the length of time guests stay at your party. The longer, the better. Calculate this metric by dividing the total time spent by all users by the number of sessions.
Conversion rate: This measures the percentage of users who complete a desired action within your product, like signing up, starting a trial, completing onboarding, or upgrading to a paid plan. Calculate it by dividing the number of users who convert by the total number of visitors.
Feature adoption rate: The feature adoption rate measures a feature’s success among users. To calculate it, divide the monthly number of active users who tried out the feature by the total number of logins in the same month.
Retention and churn are two sides of the same coin. Retention shows who sticks around. Churn tells you who’s heading for the exit. Some sample metrics include:
Net Promoter Score (NPS): A net promoter score measures customer loyalty. You ask users how likely they are to recommend your product on a scale of 0 to 10. Scores 9-10 are promoters, and 0-6 are detractors. Subtract the percentage of detractors from promoters, and you get your NPS. A high score means you’ve got fans, not just users.
Drop-off point: During the customer journey, where do most users stop using your product? 👻 That’s the point you often need to focus on or improve.
Retention rate: This shows the percentage of customers who continue using your product over time. It’s calculated as (Number of customers retained / Total number of users) * 100.
Revenue churn: Every time a customer leaves, your monthly recurring revenue (MRR) takes a hit. Revenue churn calculates that percentage. To measure this, divide the lost recurring revenue by the total recurring revenue at the beginning of the month and multiply by 100.
Not all financial metrics belong solely in finance dashboards — some are core to understanding how a product is performing. Metrics like MRR, CAC, and CLTV sit at the intersection of product and finance. 💵 They help product teams understand whether their product is driving revenue and contributing to long-term business value.
Monthly Recurring Revenue (MRR) is the revenue a company expects to receive each month from its existing customers.
The formula: average revenue per user × total number of active users per month
Customer Lifetime Value (CLTV) estimates the total revenue/profit expected from a customer throughout their time with the company.
The formula: (average purchase value x frequency) x lifespan
Customer acquisition cost (CAC) calculates the cost of acquiring a new customer, helping to evaluate marketing efficiency.
The formula: (total cost of marketing / number of new users).
Not all product metrics are created for you; some can be misleading. It can be easy to fall for vanity metrics — numbers that look good in reports but don’t tell you anything useful.
Here’s an example of this in practice:
Say you're building a website to manage your vendors. After launching the product and kicking off marketing campaigns, you start seeing a flood of sign-ups and it feels like a win. But what if only a small percentage of users engage? The rest signed up out of curiosity and dropped off without ever using the product. So, in this scenario, the sign-up number is a vanity metric.
Now, layer in a more insightful metric like average time spent in the product after sign-up. It tells you whether users are sticking around and engaging with your product features. That’s the kind of signal that points to value.
That’s why choosing metrics that give you an indicator of lasting value your audience is using is critical. Before diving into numbers, ask yourself: Which metrics truly reflect the user experience and how do users derive value from my product?
If standard metrics don’t reflect the true drivers of your product, define your own. Build them around what actually matters to your users or product.
So we know we need to avoid vanity metrics, and ensure the data we’re using truly connects our product’s performance to our objectives. Let’s explore how to pick the right product metrics.
Identifying the right product metrics starts with the problem statement you are defining and solving. A targeted approach works best here — if you define a clear issue you want to address through your product, it’s easier to identify relevant product metrics.
Let’s say your churn rate is low, but user engagement has taken a dip. Teams aren’t inviting new members. They’re barely touching that shiny new feature you shipped. In this case, customer retention rate alone won't tell you what’s going on. You should measure:
Feature adoption rate: Who’s using what, and how often?
Session length: Are they spending time in your product or bouncing right out?
These give you real signals.
A product metric framework provides a structured way to measure a product’s success. A strong framework includes both quantitative and qualitative metrics. Here are a few popular options that you’ll come across:
HEART framework Product managers often use the HEART framework to measure and improve customer satisfaction. It includes metrics for Happiness, Engagement, Adoption, Retention, and Task success. This framework is best for UX-focused products, especially in mature teams.
Pirate metric The Pirate framework (named for its “AARRR” acronym) focuses on five key user behavior metrics: Acquisition, Activation, Retention, Referral, and Revenue. It’s a favorite among startups and SaaS companies following a product-led growth approach.
North Star metric The North Star metric is the single most important measure of a company’s success. It’s a company-wide focused metric that measures the core value a business delivers to its customers.
OMTM framework
OMTM stands for “One Metric That Matters.” This is a team-specific metric seen in sprints and short-term objectives. It’s often used to measure or set marketing goals.
To measure product success, trace the entire customer journey. Track how users move, where they drop off, and what keeps them coming back. If you run a SaaS product, most user journeys look like this:
Sign-up → Onboarding → Usage → Renewal
Now, identify the key metrics for each stage. The Pirate framework performs best in these cases because users typically move through Acquisition, Activation, Retention, Revenue, and Referral phases. Each stage evaluates your product’s ability to attract those users, meet their needs, and generate sales. Likewise, based on your unique user journey, select the right metrics or framework.
Create a process to gather qualitative feedback along with the quantitative outcomes, and use both to decode the right product metrics.
Quantitative feedback tells you what’s happening, like how many users are rating your product highly, how many are active, and how long they engage.
Qualitative feedback tells you why it’s happening. These insights usually come from customer interviews, support tickets, and open-ended survey responses. They help you spot real problems, and once you know the “why,” you can define better metrics to enhance your product analytics workflows.
Now that you know how to pick the right product metrics and you’ve likely found a suitable framework to follow, let’s take it a step further. By avoiding a few common mistakes, you’ll make your metrics more accurate, more useful, and a whole lot easier to act on.
Choosing activities over impact: Metrics that measure activities (like how many product releases you deployed, how many emails you sent, or which features you improved) are easy to measure. But they don't reveal the true value of your product. Instead, chase the value-driven metrics that quantify how much business the product brings in, how many manual hours it saves, or how happy your customers are. These might be harder to measure, but ultimately reveal the real value of your product or features.
Using outdated metrics: Product managers focus on modernizing products and features to compete in a fast-moving market. But too often, they forget to update the metrics along the way. If your metrics don’t evolve with your product, you risk missing market shifts. So, update your metrics to suit the latest product changes and remain relevant.
Falling for vanity metrics: As we mentioned earlier, don’t fall for vanity metrics. To avoid them, use ratios or percentages instead of total numbers. For example, tracking the percentage of users who clicked and engaged gives you far more insight than just the total click count.
Ignoring data literacy gaps: Failing to promote data literacy across your team often flies under the radar, to your team’s detriment. If people don’t understand what the metrics mean or how to use them, even the best products may go dark.
Ultimately, invest in educating your team. Make sure everyone, from product to developers, knows how to read, question, and act on metrics. When your whole team speaks the same “metrics language,” decisions get sharper and faster.
Product metrics give you signals to act upon. They show what’s working and flag what’s not. Let’s say users sign up but don’t stick around, that’s a red flag. You need to see where they drop off and why. But to get there, you need a platform that makes this data easy to find and explore.
A self-serve analytics tool like Hex makes that happen. It’s a robust, unified platform built for both data scientists and business teams. While your data experts build advanced models and use cases, your product managers explore those insights in the same workspace without needing to switch tools or ping the data team for answers.
Want a head start? Check out these KPI templates to build product metrics dashboards effortlessly.