This is a follow-up from an earlier post on the limit of metrics.

Product managers often wrestle with a familiar question: Should I trust the numbers, or should I trust my instincts? The truth is, both matter — but their weight changes depending on where your product is in its lifecycle. Intuition plays a bigger role early, while metrics take over later. Knowing when to lean on which can be the difference between chasing noise and driving real impact.

Early-Stage Decisions: The Signal Is Too Weak

In the early stages of a product, metrics are either nonexistent or misleading. Low adoption rates, scattered usage, and noisy data can make even promising ideas appear to be failures. If you rely only on metrics at this stage, you’ll abandon good ideas too quickly.

This is where intuition matters most. Intuition, in a product context, isn’t guesswork. It’s pattern recognition shaped by exposure to customers, markets, and adjacent products. It helps teams imagine what might work before there’s hard evidence.

  • Consumer example: Airbnb’s early signups looked underwhelming, and the data suggested limited demand. Intuition about the emotional side of travel — specifically, belonging and community — drove the founders to push forward until the model clicked.

  • B2B example: An internal workflow API may show little usage at first, but interviews with developers might reveal that it saves hours of manual integration work. Metrics would have labeled it a failure, but intuition from those conversations signals hidden demand.

Late-Stage Decisions: Metrics as Guardrails

Once a product gains traction and scale, the role of intuition shifts. With a larger user base and more activity, you now have reliable signals. Metrics become the way to validate decisions, expose bottlenecks, and optimize flows.

At this stage, intuition without evidence is risky. Small missteps compound at scale, eroding trust and performance.

  • Example: Amazon famously tests everything in its retail experience. With millions of users, even a tiny change in checkout flow can move the revenue needle significantly. Metrics provide the guardrails to experiment safely and refine relentlessly.

  • Enterprise example: A CRM with tens of thousands of active users must rely on usage data to decide whether to streamline certain workflows or add new ones. Metrics on adoption, completion rates, and error frequency are more trustworthy than opinion at this stage.

The Overlap Zone

The best product managers don’t treat intuition and metrics as binary choices. They know how to use them together:

  • Intuition to frame the right hypotheses.
  • Metrics to validate and refine those hypotheses.

For example, a company exploring AI copilots might start with intuition about where users feel workflow pain. Perhaps sales reps spend too much time writing follow-up emails. That intuition guides the prototype. But once launched, adoption metrics — how many reps use the copilot daily, how often they edit its drafts — determine where to double down.

Takeaway for PMs

Ask yourself: Are we in exploration or optimization?

  • If exploration, lean on intuition and user empathy. Don’t kill ideas just because early metrics look weak.

  • If optimization, lean on metrics. At scale, even small gains have a big business impact.

Intuition and metrics aren’t opposites. They are tools for different moments.

Conclusion

Intuition is a compass, metrics are a map. Early on, you need the compass to know where to head. Later, you need the map to navigate precisely. The best PMs know how to switch between the two — and when to use them together.