Why has Smallcase's Discovery feature seen a 30% drop in daily active users over the past week?

Introduction

The recent 30% drop in daily active users for Smallcase's Discovery feature is a significant concern that requires immediate attention and a thorough root cause analysis. As we delve into this issue, we'll systematically examine potential factors, generate data-driven hypotheses, and develop a comprehensive plan to address the problem and prevent future occurrences.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent product change. Has there been any significant update to the Discovery feature in the past two weeks?

Why it matters: Recent changes could directly impact user behavior. Expected answer: Yes, a UI redesign was implemented. Impact on approach: If confirmed, we'd focus on UI/UX issues in our analysis.

• Considering user segments, I'm curious about the distribution of the drop. Is the 30% decrease uniform across all user segments, or are certain groups more affected?

Why it matters: Helps identify if the issue is global or segment-specific. Expected answer: The drop is more pronounced among newer users. Impact on approach: We'd investigate onboarding and new user experience if this is the case.

• Thinking about external factors, have there been any major market events or competitor actions in the past week that could influence user behavior?

Why it matters: External events could explain the sudden drop. Expected answer: No significant market events, but a competitor launched a similar feature. Impact on approach: We'd need to assess our competitive positioning and value proposition.

• Regarding data integrity, has there been any change in how we measure or define daily active users for the Discovery feature?

Why it matters: Ensures we're not dealing with a measurement issue rather than an actual drop. Expected answer: No changes in measurement or definition. Impact on approach: If confirmed, we can focus on actual usage patterns rather than data anomalies.