Classic metric drop question but the 100% part throws you off a bit.
Start by clarifying the metric definition and scope (e.g., 'events created' across all platforms, regions, and user segments) to ensure you're solving the right problem. Then systematically rule out data/telemetry issues, internal product changes, and external factors before proposing a response plan that includes immediate mitigation and long-term prevention.
Pro tip: Always check for data pipeline or logging issues first—a 100% drop is almost never a real user behavior change but rather a measurement failure. Also, consider whether this is a leading indicator of a broader platform issue (e.g., authentication, permissions) that could affect other features.
Define exactly what 'events created' means (e.g., public vs. private, recurring vs. one-time) and confirm the time window, platforms, and user segments affected. Ask if the drop is global or isolated to certain regions, app versions, or user cohorts.
Check if the drop is due to logging errors, pipeline failures, or dashboard bugs. Validate with raw data sources and cross-check with other related metrics (e.g., event views, invites sent) to see if the anomaly is consistent.
Review recent code deployments, A/B tests, feature flags, or policy changes that could impact event creation. Look for errors in event creation flow, permission changes, or UI modifications that might block users.
Consider external events like holidays, competitor launches, or platform outages (e.g., app store issues) that could cause a sudden drop. Check social media and support channels for user reports.
If a bug is found, roll back or hotfix immediately and communicate to stakeholders. If it's a real user behavior shift, conduct user research and consider experiments to re-engage users. Set up alerts to prevent future silent failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.