What factors are contributing to the unexpected 30% increase in member churn for Omada's diabetes prevention program this quarter?

Introduction

The unexpected 30% increase in member churn for Omada's diabetes prevention program this quarter is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for the product and business.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into product understanding, metric breakdown, and data analysis. From there, I'll form hypotheses, conduct root cause analysis, and propose validation methods and next steps.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be seasonal factors at play. Has this 30% increase been compared to the same quarter last year?

Why it matters: Seasonal trends could explain the churn spike if it's a recurring pattern. Expected answer: No, this is unprecedented for this quarter. Impact on approach: If seasonal, we'd focus on retention strategies during this period.

• Considering user segments, I'm curious about the churn distribution. Is the increased churn concentrated in any particular user cohort?

Why it matters: Identifying affected segments helps narrow down potential causes. Expected answer: Yes, it's primarily affecting users in their first 3 months. Impact on approach: We'd investigate onboarding and early engagement strategies.

• Thinking about recent changes, have there been any significant updates to the program or app in the last quarter?

Why it matters: Product changes often impact user behavior and could explain the churn increase. Expected answer: A new meal tracking feature was introduced. Impact on approach: We'd examine the feature's impact on user experience and engagement.

• Regarding measurement accuracy, has there been any change in how churn is calculated or tracked?

Why it matters: Ensures we're comparing apples to apples and not facing a data anomaly. Expected answer: No changes in churn calculation methods. Impact on approach: Confirms the issue is real and not a measurement error.