Why has the average session length for Calm's guided meditations decreased from 15 minutes to 10 minutes this month?

Introduction

The recent decrease in average session length for Calm's guided meditations from 15 minutes to 10 minutes is a concerning trend that requires immediate attention. This shift could significantly impact user engagement, retention, and overall product success. To address this issue, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent product change. Have there been any updates to the meditation library or user interface in the past month?

Why it matters: Recent changes could directly impact user behavior. Expected answer: Yes, we updated the recommendation algorithm. Impact on approach: If confirmed, we'd focus on the algorithm's impact on session length.

• Considering user segments, I'm curious about the distribution. Is this decrease consistent across all user types, or are certain groups more affected?

Why it matters: Helps identify if it's a global issue or specific to certain users. Expected answer: New users show a more significant decrease. Impact on approach: We'd investigate onboarding and new user experience.

• Regarding the metric itself, has there been any change in how session length is calculated or tracked?

Why it matters: Ensures we're comparing apples to apples in our data. Expected answer: No changes to the metric calculation. Impact on approach: If changed, we'd need to recalibrate our analysis based on the new definition.

• Thinking about external factors, have there been any significant competitor actions or market trends in the past month?

Why it matters: External influences could be driving user behavior changes. Expected answer: A major competitor launched a "quick meditation" feature. Impact on approach: We'd need to consider market positioning and feature parity.