Product Metrics Decoded: Revealing Insights for Discovery

Most companies today compile a set of metrics for their product teams to regularly report on to the company management. This includes a variety of product performance metrics(usage frequency, churn rate, NPS, etc.). But a lot of them struggle a bit with product discovery activities. So how do you track discovery?

Here are a few ideas that you can try out:

1. Tracking number of validated hypotheses
2. Tracking number of invalidated hypotheses
3. Problem-Solution Fit
4. Product-Market Fit
5. Iterations tested with users (a better spin on releases/deployments/shipped commits, etc), the thinking being that it takes multiple releases to validate or invalidate some hypotheses.
6. Customer exposure hours
7. Forgo NPS. Replace it with things customers actually have done, not something they might do in the future.

Do also remember that metrics are intended to indicate if things are going well or not. When you start giving numbers, people will try to associate that with some sort of outcome. So if number is high/low is that good or bad? My issue with some of these numbers is it isn’t clear to people outside of the organization as to what they mean. For example: If number of invalidated hypotheses is higher this time vs last time, does that mean you’re doing worse? I don’t think so, but it could be interpreted that way.

Metrics like customer experience hours, or interviews completed, or onsite visits become an easy thing to inherit as team or individual OKRs and an easy way to drive positive behavior. There are very few downsides and pretty high likelihood of positive cultural change when you increase customer touchpoints.

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