Читать книгу Habit Machine. AI Product Management - Ар'лан ис'Дрекхэм - Страница 32

At this stage, the goal isn’t to ship. It’s to test whether the interaction model holds. Without data, teams routinely build features that solve imaginary problems. The Lean Validation Loop demands evidence before engineering commits.

— Test demand before writing production code. Landing pages, waitlists, concierge flows, and fake-door prompts measure intent without heavy build cost.

— Run lightweight A/B or multivariate tests before committing expensive infrastructure. If a change doesn’t move the target metric, kill it fast.

— Segment by cohort. Not all users behave identically. Compare activation paths, retention curves, and drop-off patterns across traffic sources, device types, and intent levels.

Example: Modern AI-native coding tools validated demand by releasing early conversational prototypes to developer communities. They didn’t measure clicks. They measured daily active usage, prompt completion rates, and code acceptance velocity. The data proved workflow shift before full platform build.

Common mistakes: launching an MVP without testing intent first, ignoring early telemetry, dismissing a ninety percent first-session drop-off as “normal for early stage.” If users disappear immediately, that isn’t noise. It’s a hard stop signal.