As tools such as AI agents become more integrated with the instrumentation, governance, and centralization of product analytics data, product managers (PMs) still own the meaning of those events and the connected outcomes. Knowing when to trust the data, forming strong hypotheses, and being able to act on the insights requires an expert in the loop. Product analytics isn’t hard because reading a dashboard is hard, but because extracting meaning from a graph is often tricky: Data evolves, humans bring bias, and definitions change. Great PMs treat their data and their product equally: well designed, well documented, and connected to clear outcomes.
In this post, we’ll build upon an example of how two well-meaning PMs can come to different conclusions by looking at the same data. We’ll unpack what’s going on under the hood, which best practices can be used to extract meaning, and how modern tools and personal expertise go hand in hand with the way product teams work with their data.
When a simple funnel can tell different stories
We’ve all been there: You launched a feature and you start tracking its adoption via a funnel graph, one of the most common visualizations. You set an overall conversion goal of 50%, and now that your feature is live, your funnel conversion is at 42%—slightly below your original goal. It looks like the biggest drop happens at the “Enter payment details” step.
