I jumped straight into diagnosis mode, which felt right, but I think I spent too long on the metric itself before clarifying what 'down' even meant.
Start by clarifying the metric definition and segmenting the 10% drop to identify where and when it occurred. Then form hypotheses about root causes across internal and external factors, prioritize the most likely ones, and propose data-driven next steps to validate and address the issue.
Pro tip: Demonstrate a structured, hypothesis-driven approach while emphasizing the importance of understanding the 'why' behind the metric before jumping to solutions. Show that you can balance quick wins with long-term strategic thinking.
Ask clarifying questions to understand the metric: How is 'friend requests' defined? Is it requests sent, accepted, or both? What is the time frame and baseline? Are there any recent changes or events?
Break down the metric by dimensions such as user demographics, geography, platform, and time to identify which segments are driving the decline.
Brainstorm potential root causes: internal (product changes, bugs, algorithm updates) and external (seasonality, competition, privacy concerns, market trends).
Prioritize hypotheses based on impact and likelihood, then outline how to validate them using data analysis, user research, or experiments.
Propose immediate fixes if a clear cause is found, and suggest longer-term strategies to improve friend request metrics, such as product improvements or growth initiatives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.