Duolingo's Growth Analytics team has identified a complex churn pattern in the new onboarding flow for its premium subscription product. The finding combines cohort retention, device-level segmentation, and a model-based estimate showing that a recent product change likely improved 7-day conversion but increased 30-day churn for a specific user segment. You are the program manager coordinating the launch decision with product, engineering, and analytics.
The team includes 1 product manager, 2 data scientists, 4 engineers, 1 designer, and 1 lifecycle marketing lead. Leadership wants a go/no-go recommendation in 10 business days because the onboarding flow is scheduled for rollout to 40% of new users before the next quarterly planning review.
The product manager wants a simple recommendation and clear trade-offs, not a deep statistical explanation. The data science lead wants the nuance preserved because the result is directionally positive overall but risky for Android users in LATAM. Engineering wants a stable scope and no last-minute instrumentation changes. Marketing wants the rollout to proceed to support a paid acquisition campaign already booked for next month.
You have a $35,000 analysis and experimentation budget remaining this quarter, no additional headcount, and only one analyst available at 50% capacity. The decision memo must be reviewed by the Director of Product by next Friday at 3 PM. Any instrumentation changes require 4 engineering days and would delay launch by 1 week.