Читать книгу Habit Machine. AI Product Management - Ар'лан ис'Дрекхэм - Страница 36

An evidence-driven culture doesn’t emerge because you buy analytics tools. It emerges because you change how decisions are made.

— Democratize access. If telemetry lives in one specialist silo, decisions will still be made subjectively. Give designers, engineers, and PMs direct access to the data that matters to their work.

— Embed experimentation into the workflow. Fake doors, staged rollouts, and lightweight A/B tests should be routine, not exceptional. Normalize learning over being right.

— Raise data literacy across disciplines. Every role should understand the metrics that govern their impact. Designers should read funnel drop-off. Engineers should track feature adoption. Marketers should tie campaigns to retention cohorts.

— Tie every meaningful change to a hypothesis. Before shipping, document what you expect to move, by how much, and how you’ll measure it. After shipping, compare reality to expectation. Close the loop.

Evidence-driven product management isn’t about building bigger dashboards. It’s about building tighter feedback cycles. When teams replace guesswork with telemetry, opinion with behavior, and output with outcome, they stop hoping for product-market fit. They engineer it.

Data tells you what’s happening. It doesn’t tell you why. The next layer of product leadership is behavioral intelligence: translating telemetry into motivation, mapping the psychology behind the clicks, and designing experiences that align with how humans actually decide. In the next chapter, we’ll break down how to run customer research that uncovers hidden drivers, not just surface complaints.