How Finch.ai Cut Onboarding Drop-off by 32% with Progressive Disclosure

AI · 6 min read

When Finch.ai launched its visual model inspector in late 2025, the team overloaded the first-run experience with tooltips, full-screen modal tours, and a 12-step checklist. Although engagement with the inspector feature was high for power users, new signups abandoned the product before their first model upload, citing cognitive overload and setup friction. The product team ran a funnel analysis and identified a 62% drop between account creation and first successful model inference.

The redesign replaced the one-size-fits-all tour with progressive disclosure tied to user intent: only showing the next micro-interaction needed to complete the current task (upload, run, view results). They added lightweight inline examples (preloaded sample datasets) and deferred advanced interface options behind a “More” affordance. To surface relevance, Finch used a single contextual prompt at the top of the inspector that dynamically changed based on whether the user uploaded a CSV, image, or connected to S3.

A/B tests over eight weeks showed a 32% reduction in onboarding drop-off and a 21% increase in weekly active use among new accounts. The team documented an important design decision: it sacrificed immediate discoverability of advanced controls to maximize activation, accepting that power-user education could be handled by later-stage in-product learning and targeted email sequences. The case remains a practical lesson in prioritizing the first core task above feature completeness for early-stage ML products.