How to Choose the Right Metrics for Early-Stage Products

When I worked on building a health tech product, one of the first challenges I faced was deciding what to measure. At the early stage, it’s tempting to track everything, from page views to downloads , but not everything truly reflects whether your product is moving in the right direction. Over time, I learnt that the right metrics depend on your product’s current stage and goal, not just what looks impressive on a dashboard.

For our health tech product, the goal was simple, to help local communities track and share health-related data like vaccination rates and common illnesses. This meant our focus wasn’t on vanity numbers like total sign-ups or app downloads, but rather on engagement and consistency of data input. Our early metric became: “How many users actively record data at least twice a week?” That single metric told us more about real adoption than any download count ever could.

At the early stage, it’s easy to chase what looks good instead of what matters. We realized that our success depended on how useful the product was to the community health workers and not just how many people signed up. So, we tracked active users per community to see if local groups were actually using it, data accuracy rate to measure how reliable the information was, and feedback frequency to understand user challenges and feature requests. These metrics helped us see whether we were solving a real problem or simply attracting curiosity.

Some metrics tell you what’s happening now, while others show results over time. For instance, the number of new health reports submitted daily is a leading metric because it signals immediate engagement. Improved accuracy of community health data after three months is a lagging metric because it reflects long-term impact. Tracking both would help us spot issues early and measure real progress later.

Just because we were lean we didn’t have the luxury of a complex analytics setup. We started with Google Analytics and simple in-app event tracking, focusing on just three core metrics. This simplicity kept us focused. As we matured, we added more metrics, but never lost sight of the few that mattered most to our product’s purpose.

Metrics aren’t static. As your product evolves, what you measure should evolve too. After launch, we shifted from tracking user activity to user retention and data sharing between communities. That shift reflected our product’s transition from early testing to real-world adoption.

Choosing the right metrics for an early-stage product is about clarity, not complexity. Ask yourself: what problem am I solving, who needs this solution most, and what behaviour shows that the problem is truly being solved? Once you answer these questions, your key metrics will reveal themselves.

Blessings!