End-to-End Analysis Plan: Investigating Frequent Google Meet Call Drops

Context

A major enterprise customer reports frequent Google Meet call drops. As a Data Scientist in a technical screen, outline a rigorous plan to diagnose, quantify impact, prioritize action, and recommend a decision under uncertainty.

Tasks

1. Define a drop precisely (e.g., unexpected disconnect requiring a rejoin within 60s). Clarify exclusions (user hang-up, host ends meeting) and edge cases.
2. Specify client- and server-side instrumentation needed, including (but not limited to):
   • Session start/stop, reconnect attempts, ICE state changes
   • WebRTC metrics (RTT, jitter, packet loss, bitrate, frame drops)
   • Device/OS/app version, CPU/memory, background/foreground
   • Network type (Wi‑Fi/cellular), carrier/ASN, NAT type/VPN, region
   • Error codes/crash logs; server capacity/errors
3. Propose a deduplication strategy to avoid overcounting correlated failures across layers (client/network/server) and multiple reconnects.
4. Quantify business impact in three layers:
   • Meeting-minutes lost
   • User productivity loss
   • Account-level retention risk
5. Estimate marginal impact per 1 percentage point (pp) increase in drop rate using matched controls and/or hierarchical models.
6. Prioritize fixing the bug vs. building a new feature via expected value: expected value = impact × likelihood × duration ÷ effort. Include opportunity cost and guardrail metrics (e.g., crash rate, CPU, startup latency).
7. Check for regression: analyze by-version and by-region using canaries/holdbacks.
8. Produce a decision memo that states assumptions, sensitivity analysis, and specific observations that would invalidate your recommendation.