How can we explain the sudden increase in cancellation rates for Maven Clinic's fertility treatment coaching sessions this quarter?

Introduction

The sudden increase in cancellation rates for Maven Clinic's fertility treatment coaching sessions this quarter is a concerning trend that requires immediate attention. To address this issue, I'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term and long-term implications for the product and business.

My analysis will follow a structured framework covering issue identification, hypothesis generation, validation, and solution development. This approach ensures we thoroughly examine all aspects of the problem and develop a comprehensive strategy to resolve it.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal component. Has this increase in cancellations occurred during the same quarter in previous years?

Why it matters: Identifying seasonal patterns helps distinguish between cyclical trends and unique issues. Expected answer: No, this is unprecedented for this quarter. Impact on approach: If seasonal, we'd focus on managing expectations; if not, we'd dig deeper into recent changes.

• Considering user segments, I'm curious about the demographics affected. Are cancellations concentrated in any particular age group or treatment stage?

Why it matters: Different user segments may have distinct needs or challenges. Expected answer: Cancellations are higher among users in the early stages of treatment. Impact on approach: We'd investigate factors specific to new users or early-stage treatments.

• Thinking about recent changes, have there been any updates to the coaching session format or scheduling system in the past quarter?

Why it matters: Product changes can have unintended consequences on user behavior. Expected answer: A new scheduling system was implemented last month. Impact on approach: We'd focus on potential issues with the new system and user adaptation.

• Regarding performance metrics, has the definition of a "cancellation" remained consistent, or have there been any changes in how it's measured?

Why it matters: Ensures we're comparing apples to apples in our data analysis. Expected answer: The definition has remained consistent. Impact on approach: If changed, we'd recalculate historical data; if consistent, we'd focus on actual behavior changes.