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Building a Product Metrics Framework
One thing I’ve learned about product metrics is that the hardest part isn’t finding metrics to track. It’s deciding which metrics actually matter. Most products already have plenty of data. There are dashboards for acquisition, engagement, retention, revenue, feature usage, customer satisfaction, and dozens of other things. Yet teams can still struggle to answer a…
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Sample vs Population
Early in my product career, I often treated user data as if it represented all users. If 500 customers responded to a survey, I’d look at the results and think, “This is what our customers want.” But 500 responses don’t necessarily represent every customer. That’s the fundamental difference between a sample and a population, and…
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P-Values Explained for Product Managers
When I first started working with product experiments, statistical terms like p-values felt more complicated than they needed to be. I’d see statements like, “The experiment has a p-value of 0.03,” and the immediate question was: “So, did the experiment work?” Not necessarily. Understanding p-values isn’t about becoming a statistician. For Product Managers, it’s about…
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Tracking the Right User Actions
One mistake I’ve seen product teams make repeatedly is tracking everything. Every click, page view, button press, hover, scroll, and interaction gets captured. The analytics dashboard looks impressive, but when it comes time to answer a product question, nobody knows which events actually matter. More data doesn’t automatically create better insights. The goal isn’t to…
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Here’s a practical PM-focused version that focuses on how to actually interpret a retention curve, not just define it.
How to Read a Retention Curve Early in my product career, I used to look at retention as a single percentage. “30-day retention is 25%.” It sounded useful, but it didn’t tell me much about what was actually happening to users. Over time, I realized that the shape of the retention curve often tells a…