7 Product KPIs Every PM Should Track (With Examples)

Product KPIs are the measurable indicators that tell you whether your product is creating real value for customers and the business. Without them, you're shipping features blindly. With them, you can prove impact, predict problems, and make better decisions.

"I'm not sleeping well if I don't base my strategy and my bets on data." — Konrad Heimpel, VP Product & Data at GetSafe

Konrad's honesty resonates with every PM who's been asked "How is it performing?" without a clear answer. In this guide, I'll walk you through the 7 types of product KPIs that will help you measure what matters, from North Star metrics to causal indicators, with practical examples for each.

KPIs vs. Metrics: What's the Difference?

Before diving into KPI types, let's clarify the difference between KPIs and metrics. The best definition I've found comes from Richard Hatheway:

KPIs are quantifiable values that reflect a business goal or objective (strategic) and how successful the business is in accomplishing it. A metric is also a quantifiable value, but it reflects how successful the activities are (tactical) to support the accomplishment of the KPI.

In short: KPIs are strategic, metrics are tactical. Metrics help you accomplish the KPI. This distinction matters because many PMs confuse the two. Tracking 50 metrics won't help if you can't tie them to your strategic KPIs, and your KPIs should connect to your broader OKR framework.

The 7 Types of Product KPIs

There are many books and resources about KPI diversity. One of my favorites is the book "Lean Analytics" by Ben Yoskovitz and Alistair Croll. The following KPI types combine theoretical foundations with practical applications I've learned from building products for over a million customers.

#1 North Star Metric (One Metric That Matters)

The North Star metric represents the core value your product delivers. It's the single KPI that, if improved, indicates your product is succeeding.

"There is one metric that really helps drive your entire business and it will change over time. It's not one metric that you measure your entire business by forever because your business from idea to scale is going to change radically." — Ben Yoskovitz, Co-author of Lean Analytics

Ben's insight is crucial: your North Star isn't permanent. As your product and business evolve, so should your primary metric. What matters at startup stage differs from what matters at scale.

Example North Star Metrics:

• Google Search: Time to find (lower is better). Google's goal is helping users find answers as fast as possible.
• YouTube: Watch time (higher is better). More time watching means more ad revenue and engaged users.
• SaaS products: Daily or Weekly Active Users (DAU/WAU) as a proxy for habitual usage.
• E-commerce: Revenue per visitor or conversion rate.

The North Star isn't just for measurement. It gives your entire organization a clear direction. Engineering teams at Google optimize for faster search results because everyone understands what drives value. This alignment is why your North Star should connect directly to your product vision.

#2 Leading KPIs

Leading KPIs tell you if you're on track to hit your goals. They look forward and predict future outcomes.

"What is happening today in your product, on your website, in your service that tells you what's going to happen in the future. Everybody tracks lagging indicators. It does tell you what has happened. The leading indicators do predict the future." — Ben Yoskovitz

The concept of leading indicators transformed how I define KPIs. I remember an Agile Coach approaching me after a meeting where I'd presented my KPIs. He said my KPIs were defined nicely but were missing leading indicators. That feedback changed everything.

Example Leading KPIs:

• Landing page: Visitor-to-signup conversion rate predicts future revenue.
• SaaS: Feature activation rate predicts retention.
• E-commerce: Add-to-cart rate predicts purchases.
• Mobile app: Day 1 and Day 7 retention predict long-term engagement.

I also call these "health KPIs" because they tell me if things are moving in the right direction before it's too late to course-correct.

#3 Lagging KPIs

Lagging KPIs tell you what already happened. They measure success after the fact.

While lagging indicators can't predict the future, they confirm whether your efforts worked. The key is pairing them with leading indicators to create a complete picture.

Example Lagging KPIs:

• Revenue / Monthly Recurring Revenue (MRR): The ultimate business outcome.
• Customer Churn Rate: Percentage of customers who stopped using your product.
• Net Promoter Score (NPS): Customer satisfaction and likelihood to recommend.
• Customer Lifetime Value (CLV): Total revenue from a customer relationship.

Practical tip: When I work on features, I always ask two questions: How do we measure success (lagging)? And how do we check if we're on track (leading)?

#4 Vanity KPIs

Vanity KPIs make you feel good but don't drive decisions. They're the "ego KPIs" that look impressive in reports but don't tell you what to do next.

"Vanity metrics make us feel good, but they don't move the needle." — Ben Yoskovitz

I've fallen into this trap. It feels great knowing 1,372 people clicked a button. But why did no one buy? Basic Google Analytics data often falls into this category, tracking pageviews and sessions without connecting them to business outcomes.

Common Vanity KPI Traps:

• Total page views or unique visitors
• App downloads (without activation data)
• Social media followers or likes
• Raw click counts without context

The danger isn't tracking these numbers. It's when they become the only numbers you track. If you truly want to be data-driven, you need to read between the lines.

#5 Actionable KPIs

Actionable KPIs tell you exactly what to do next. They're the "real feel-good KPIs" because understanding data and knowing your next move is genuinely satisfying.

"Just because you can track something doesn't mean you should." — Adam Greco

Adam's quote is a perfect filter for KPI selection. Before adding any metric, ask: "What decision will this help me make?"

Example Actionable KPIs (Converting Vanity to Actionable):

• Instead of: Total downloads → Track: Downloads from Facebook ad (Jan 1-31) with 7-day activation rate
• Instead of: Time on page → Track: Time from page open to first meaningful interaction
• Instead of: Followers count → Track: Engaged followers (comments/shares vs. passive follows)

I like going the "extra mile" when defining KPIs. Instead of shortcuts, I ask: What will I actually do with this data? It takes more effort upfront, but you'll love the clarity it provides.

#6 Correlated KPIs

Correlated KPIs show relationships between different metrics. Understanding correlations helps you identify patterns, but be careful not to confuse correlation with causation.

Here's my favorite example:

During summer, ice cream consumption increases. At the same time, doctors report more sunburns. Does eating ice cream increase your likelihood of getting sunburned? Of course not. Both are correlated with summer weather, but one doesn't cause the other.

Example Correlated KPIs:

• High NPS correlates with low churn (happy customers stay longer)
• Feature usage correlates with upgrades (engaged users see more value)
• Support tickets correlate with churn risk (frustrated users leave)

Warning: Correlation tells you variables move together. It doesn't tell you why. Before acting on a correlation, dig deeper to understand the relationship.

#7 Causal KPIs

Causal KPIs identify the root cause of outcomes. They answer "why" something happened, not just "what" happened.

Going back to the ice cream example: Sun exposure causes sunburn. That's the causality. People at the beach spend too much time in the sun without protection. Construction workers face similar risks. The root cause is always sun exposure, not ice cream.

Example Causal KPIs:

• Poor onboarding completion causes early churn (fix onboarding, reduce churn)
• Slow page load times cause drop-offs (improve performance, increase conversions)
• Confusing pricing pages cause abandoned carts (clarify pricing, boost sales)

When I define or review KPIs, I always think about causalities. If I want to prove or disprove a hypothesis, causal indicators are essential.

Note: If you're standing too long in the queue to buy ice cream, then the queue causes your sunburn. That's a causality!

How KPIs Connect to OKRs

KPIs don't exist in isolation. They're most powerful when connected to your broader goal-setting framework. This is where OKRs (Objectives and Key Results) come in.

"Using the PMF survey is so much better than looking at the NPS because it tells us so much more and helps us so much more to understand which area of your product, which feature is performing or not." — Konrad Heimpel

Konrad's insight about choosing better metrics applies to how you connect KPIs to OKRs. Not all KPIs belong in your OKRs. Only the ones that drive meaningful outcomes.

KPIs vs. OKRs: A Quick Comparison

The key insight: KPIs tell you where you are. OKRs tell you where you want to be.

KPIs become Key Results when you tie them to specific objectives with a target improvement. For example, "Churn rate" is a KPI. "Reduce churn from 8% to 5% by Q2" is a Key Result within an OKR.

For a deeper dive into goal-setting frameworks, check out my Practical Guide to OKRs and OKR best practices.

When to Use KPIs vs. OKRs

Here's a practical way to think about it:

• Use KPIs for ongoing monitoring. They're your dashboard lights. Churn rate, MRR, DAU—these should be tracked continuously.
• Use OKRs for focused improvement initiatives. When you want to move a KPI in a specific direction within a timeframe, wrap it in an OKR.

For example, if your churn rate KPI shows 8% monthly churn, that's a red flag. You'd then create an OKR like: "Objective: Improve customer retention. Key Result: Reduce monthly churn from 8% to 5% by implementing improved onboarding." The KPI monitors the health; the OKR drives the improvement.

This connection also works upward. Your team's KPIs should ladder up to company-level OKRs. If the company objective is "Become the market leader in customer satisfaction," your product team's KPIs (NPS, support resolution time, feature adoption) should directly support that objective.

Common KPI Mistakes (And How to Avoid Them)

Over the years, I've made every mistake in the book when it comes to KPIs. Here are the most common pitfalls I see:

Mistake #1: Tracking Too Many KPIs

When everything is a priority, nothing is. I've seen dashboards with 50+ metrics where teams can't focus on what actually matters. Start with 3-5 KPIs per team or product area. You can always add more later, but clarity comes from constraint.

Mistake #2: Not Defining Success Upfront

"We'll track signups" isn't a KPI. "We'll achieve 1,000 signups per month with a 20% activation rate" is. Without clear targets, you can't know if you're succeeding. Every KPI needs a goal.

Mistake #3: Ignoring Context

A 5% conversion rate might be excellent for one product and terrible for another. Always benchmark against your own history, your industry, and your specific stage. Context determines whether a number is good or bad.

Mistake #4: Measuring Output Instead of Outcome

"We shipped 10 features" is output. "Our feature adoption increased by 30%" is outcome. PMs often fall into the trap of measuring activity (what we built) rather than impact (what changed for users). Focus your KPIs on outcomes that matter to the business and customers.

Mistake #5: Not Reviewing KPIs Regularly

KPIs aren't "set and forget." Markets change, products evolve, and your metrics should evolve too. Schedule quarterly KPI reviews to ask: Are these still the right things to measure? Have our targets become too easy or too hard? Are there new metrics we should track?

How to Approach Your KPI Definition

Organizations of all sizes produce and consume data constantly. Whether you're setting up a new team, building a new product, or optimizing an existing one:

Good data helps you understand the past, make better decisions in the present, and forecast the future.

I define KPIs top-down, starting with the big picture. If you understand the "why," it's easier to derive your KPIs from that. Whenever I define KPIs, I focus on outcomes, not output. This approach connects to how you define your overall product strategy.

My KPI definition process:

1. Start with the North Star. What single metric represents your product's core value? This should be the metric your entire organization rallies around. If you can't articulate it in one sentence, keep refining until you can.
2. Define leading and lagging pairs. For each goal, identify what predicts success (leading) and what confirms it (lagging). I've found that having both creates a complete feedback loop. Leading indicators give you time to adjust; lagging indicators confirm your adjustments worked.
3. Avoid the vanity trap. For every metric, ask: "What decision will this help me make?" If the answer is "nothing specific," reconsider whether you need that metric at all.
4. Check for causality. Can you identify root causes, or are you only seeing correlations? This is where A/B testing becomes valuable. It's the best way to establish causality rather than just observing correlation.
5. Connect to OKRs. Ensure your KPIs ladder up to your objectives. Every team's KPIs should connect to the company's strategic goals. If a KPI doesn't connect, question whether it belongs in your dashboard.

Going from top to bottom and back up again takes time and brainpower. But at the end of the day, the whole organization benefits from this clarity.

A Real-World Example

Let me walk through how I applied this process at my previous role. We were building a new feature for a subscription product. Here's what our KPI framework looked like:

• North Star: Active subscriptions (the ultimate measure of value)
• Leading indicators: Feature adoption rate, time to first value
• Lagging indicators: Monthly churn rate, NPS score
• Vanity traps we avoided: Total signups (without activation), page views on the feature page
• Actionable version: "Users who complete onboarding within 24 hours" rather than "users who signed up"

This framework gave us clear signals. When leading indicators dropped, we knew to investigate before churn spiked. When we saw correlation between feature usage and retention, we ran experiments to confirm causality before investing heavily in that feature.

The Lean Startup movement has shown many ways to build, measure, and learn quickly. But there's one thing I must emphasize:

Don't ship a feature before you have the metrics in place.

"If you can't measure it, you can't improve it." — Peter Drucker