Here’s Why You Shouldn’t Obsess Over Metrics

Here’s a fun thing that happens in product management. You wake up one morning and your daily active users are down 15%. Your conversion rate, though? Up 8%. Customer satisfaction scores just hit an all-time high, but your churn rate is creeping upward. Your metrics dashboard looks like a Jackson Pollock painting, except instead of paint splatters, it’s conflicting signals that make you question everything you thought you knew about your product.

The thing about metrics is that they’re like teenagers. Sometimes they’re telling you something important. Sometimes they’re just being dramatic. The trick is figuring out which is which before you make a decision you’ll regret.

Let’s start with a fundamental truth: Not all metrics deserve equal attention at all times. This sounds obvious until you’re in a meeting where someone is freaking out because time-on-page dropped by 12 seconds. Is that bad? Maybe. Or maybe you just made your product more efficient.

According to research from McKinsey, companies that excel at data-driven decision making are 23 times more likely to acquire customers. Great. But here’s what they don’t tell you: Being data-driven doesn’t mean reacting to every data point like it’s a fire alarm.

If you were driving a car and every warning light demanded immediate action, you’d never get anywhere. Some lights matter more than others. Some can wait. The same principle applies to product metrics. Yet somehow, we’ve created a culture where every metric fluctuation triggers a crisis meeting.

Related: 5 Steps to Creating Metrics That Matter for Your Company

Decision framework

So, when should you actually panic? Here’s a framework that’s served me well.

First, look for metric combinations that tell a story. Single metrics lie. When daily active users drop but session duration increases, that’s not necessarily bad. Maybe you’re shedding casual users while your core audience becomes more engaged. That could actually be progress.

The real warning signs come in clusters. Declining user growth plus increasing churn plus dropping engagement? Now you’ve got a pattern worth investigating. It’s like medical symptoms. A headache alone might mean nothing. A headache with fever and sensitivity to light? Time to see a doctor.

PayPal discovered this the hard way in its early days. They were obsessing over user acquisition metrics while missing the bigger picture: Their fraud rates were climbing faster than their legitimate transaction volume. The metrics were all there, but nobody was looking at them together.

Second, distinguish between leading and lagging indicators. Some metrics predict the future. Others just confirm what already happened. Confusing the two is like using your rearview mirror to navigate forward.

Customer support ticket volume? That’s often a leading indicator. When it spikes, something is broken. Revenue? Usually lagging. By the time revenue drops, the problem started months ago.

Netflix figured this out when it noticed password sharing complaints increasing before subscriber growth stalled. The complaints were the canary in the coal mine. The growth stall was just the inevitable result.

Third, understand your metric’s natural volatility. Some metrics are drama queens by nature. They fluctuate wildly as part of their normal behavior. Others are steady until something’s genuinely wrong.

Ecommerce conversion rates can swing 30% day to day based on traffic sources, time of month and even weather patterns. A single day’s dip means nothing. But if your enterprise software’s monthly recurring revenue suddenly drops? That’s not normal volatility. That’s a customer jumping ship.

Spotify learned this when it initially panicked over daily listening hour variations. Turns out, people just listen to less music on Tuesdays. Once they understood the natural patterns, they could spot actual anomalies.

Fourth, consider the cost of being wrong. What happens if you ignore this metric and you’re wrong? What happens if you panic and you’re wrong?

Sometimes the cost of overreacting exceeds the cost of waiting. Imagine redesigning your entire onboarding flow because new user activation dropped for a week. You spend months on the project, only to discover the drop was seasonal.

Other times, waiting is catastrophic. When security breach indicators spike, you don’t wait for statistical significance. You act immediately because the downside of being wrong is minimal compared to the downside of being right but slow.

Related: Use the Metrics That Really Matter in Your Business

Metric hierarchy

Here’s my advice. Build yourself a metric hierarchy. At the top, put the three to five numbers that genuinely predict your business’s health. These get daily attention. Everything else? Check weekly or monthly.

More importantly, train your team to think in stories, not statistics. When someone comes to you with a metric panic, ask them to tell you the user story behind the number. What’s actually happening to real people using your product?

The truth is, most metric movements are noise. The signal is rare, which is precisely why it’s so valuable when you find it. The best product managers I know have developed an almost intuitive sense for which metrics deserve attention.

Until you develop that intuition, remember this: Your metrics are tools, not masters. They should inform your decisions, not make them for you. Sometimes, the wisest decision is to close the dashboard and talk to an actual user.

Because at the end of the day, products succeed when they solve real problems for real people. No metric, no matter how sophisticated, changes that fundamental truth.

Related: Why Focusing on KPIs Too Much Can Backfire