Product managers love metrics. Dashboards, OKRs, funnel charts — these tools are everywhere. They give us a sense of control, objectivity, and accountability. But metrics have limits. They can only measure what already exists. They tell you how a current feature is performing, but they can’t tell you what to build next.

This is where intuition comes in.

What Intuition Really Means in Product Work

In product management, “intuition” often gets dismissed as gut feel. But good product intuition isn’t about hunches or ego. It’s the pattern recognition that comes from deep exposure to customers, market dynamics, and technology shifts.

Think of it as informed imagination: the ability to see possibilities before they can be quantified. Intuition draws from:

  • Customer empathy — observing pain points in context, not just in survey scores.

  • Analogies — connecting lessons from other industries or products.

  • Experience — knowing what has and hasn’t worked in past launches.

Metrics can confirm or disprove hypotheses, but intuition generates the hypotheses in the first place.

Why Metrics Alone Fall Short

Relying only on metrics creates two common traps:

  1. Local optimization: You improve the efficiency of what already exists (e.g., making a checkout flow one click shorter) but miss the chance to reimagine the flow entirely.

  2. Blind spots: Metrics can’t measure what hasn’t been built yet. If you only follow numbers, you only follow the past.

These patterns mirror the cognitive biases that derail product teams — tunnel vision that filters for validation rather than genuine discovery.

A/B testing is the classic example. It can tell you whether a blue or green button performs better, but no test would have invented Slack, Spotify, or Figma. Those products emerged because teams trusted their intuition that people wanted to collaborate in new ways. This is where hypothesis-driven thinking becomes crucial — treating product ideas as bets to be tested rather than certainties to be executed.

Examples of Intuition at Work

  • Consumer case: Netflix’s decision to invest in streaming. At the time, DVD rentals were still profitable. Metrics showed customers were satisfied with fast shipping and broad inventory. But leadership intuited that convenience would eventually mean no discs at all. Metrics couldn’t prove it; they had to make a leap.

  • B2B case: Atlassian’s early focus on self-serve enterprise software. Industry metrics suggested long sales cycles and heavy customization were the standard. Intuition told them that small teams wanted to adopt tools without procurement overhead. That insight created a wedge that later scaled to the enterprise.

  • Workflow/API case: Stripe’s early bet on developer experience. Payment processing was already “measurable” in uptime and transaction volume. But founders intuited that if APIs felt like consumer-grade products, developers would prefer Stripe — and that preference became market dominance.

Balancing Metrics and Intuition

The real skill isn’t choosing one over the other, but knowing when each applies.

  • Use metrics to refine: once a product has traction, metrics can expose bottlenecks, conversion gaps, and churn drivers.

  • Use intuition to invent: in early exploration, when the signal is too weak or the opportunity is undefined, intuition helps you generate and frame ideas worth testing.

A simple way to ask yourself: Am I solving an optimization problem or an imagination problem?

  • If optimization, lean on metrics.

  • If imagination, lean on intuition.

This framework complements other decision-making approaches that help product leaders choose the right level of analysis for each situation.

Takeaway for PMs

Metrics keep you honest about what’s working today. Intuition opens the door to what might work tomorrow. The best product managers don’t dismiss either. They cultivate intuition through user exposure, cross-industry learning, and reflection. And they validate intuition with metrics once a path is clear.

Conclusion

Metrics refine the present. Intuition invents the future. The danger lies in over-relying on one at the expense of the other. Great product managers balance both — knowing when to trust the numbers, and when to trust the deeper sense that customers are ready for something new.