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Product Management Root Cause Analysis Question: Investigating sudden drop in Twitter Spaces video views

What caused the sudden 30% drop in video views on Twitter Spaces yesterday?

Data Analysis Problem-Solving Technical Understanding Social Media Live Streaming Digital Entertainment
Social Media User Engagement Root Cause Analysis Technical Troubleshooting Video Streaming

Introduction

The sudden 30% drop in video views on Twitter Spaces yesterday is a critical issue that demands immediate attention and thorough analysis. As we delve into this problem, we'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

Our analysis will follow a structured framework, beginning with clarifying questions to establish context, ruling out external factors, understanding the product and user journey, breaking down the metric, gathering and prioritizing data, forming hypotheses, conducting root cause analysis, and finally, proposing validation methods and next steps.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

  • I'm noticing the specificity of "yesterday" in the problem statement. Would you say this drop was indeed sudden, occurring within a 24-hour period?

Why it matters: This helps determine if we're dealing with an acute issue or a gradual decline. Expected answer: Yes, it was a sudden drop within 24 hours. Impact on approach: A sudden drop would point towards a specific event or change rather than a gradual shift in user behavior.

  • Given the 30% drop, I'm assuming we have baseline metrics for comparison. Can you confirm if this drop is compared to the previous day, week average, or another timeframe?

Why it matters: Establishes the context for normal performance and helps identify any cyclical patterns. Expected answer: The drop is compared to the 7-day rolling average. Impact on approach: This would help us rule out day-of-week variations and focus on significant deviations.

  • I'm thinking about user segments. Has this drop been observed across all user groups, or is it more pronounced in specific demographics or regions?

Why it matters: Helps narrow down potential causes related to specific user groups or geographical issues. Expected answer: The drop is more significant among iOS users in North America. Impact on approach: This would focus our investigation on iOS-specific issues or regional factors affecting North American users.

  • Considering the nature of Spaces, I'm curious about content types. Have you noticed if the drop is consistent across all types of Spaces content, or is it more severe for certain categories?

Why it matters: Identifies if the issue is content-specific or a platform-wide problem. Expected answer: The drop is more pronounced in live Spaces compared to recorded ones. Impact on approach: This would direct our attention to potential issues with live streaming technology or user engagement during live events.

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