Adaptive onboarding that detects where users get stuck and intervenes with contextual help.
Tracks user progress through onboarding steps via Supabase. When a user stalls on a step for longer than the median time (calculated from all users), the system triggers a contextual help message via Claude. The message is tailored to the specific step and the user's role (detected during sign-up). Help ranges from a tooltip to a step-by-step walkthrough depending on how long they've been stuck.
Live for Stackline since sprint delivery. Onboarding completion jumped from 40% to 78%. The biggest impact was on Step 3 (team invitation) — 60% of users previously abandoned here. The AI now proactively offers to send invites on behalf of the user, reducing friction. Average onboarding time dropped from 12 minutes to 7 minutes. Users who receive AI help are 2.3x more likely to complete onboarding than those who don't.
Shipped as part of the Stackline sprint. The onboarding flow pattern is now reusable — BRVO deploys it for any SaaS client with a multi-step setup process. The analytics data from Stackline informed the intervention timing thresholds.
Any multi-step product setup. The AI detects confusion, offers help at the right moment, and can complete complex steps (like API key generation) on behalf of the user.
A fintech app where KYC verification causes 45% drop-off. The AI guides users through document upload, explains what's needed in plain English, and troubleshoots rejected submissions.
A course platform where learners abandon during initial setup (profile, preferences, first lesson). The AI personalises the flow based on learning goals and suggests the best starting point.