Introduction
The sudden 20% drop in daily active users for Candy Crush Saga last week 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 and long-term implications for the product.
I'll begin by gathering essential information, formulating data-driven hypotheses, and conducting a comprehensive root cause analysis. Throughout this process, we'll consider various factors that could contribute to such a significant decline in user engagement, ranging from technical issues to changes in user behavior or external market forces.
Our goal is to not only uncover the underlying cause of this drop but also to develop a robust action plan that addresses the immediate concern and strengthens our product for the future.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Recent changes often correlate with sudden metric shifts. Expected answer: Yes, there was a major update. Impact on approach: If yes, we'd focus on the update's impact; if no, we'd look at other factors.
Why it matters: Helps identify if the issue is global or specific to certain user groups. Expected answer: The drop varies across segments. Impact on approach: If varied, we'd focus on affected segments; if uniform, we'd look at broader issues.
Why it matters: Platform-specific issues could indicate technical problems. Expected answer: The drop is more significant on one platform. Impact on approach: If platform-specific, we'd investigate technical issues; if consistent, we'd look at cross-platform factors.
Why it matters: Competitive actions can significantly impact user engagement. Expected answer: A competitor launched a new game or promotion. Impact on approach: If yes, we'd consider market positioning; if no, we'd focus more on internal factors.
Why it matters: Ensures we're addressing a real issue, not a data anomaly. Expected answer: No changes or issues in measurement. Impact on approach: If there are measurement issues, we'd first address data accuracy; if not, we proceed with our analysis.
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