Analyze Call Drop Rates Pre- and Post-Update Implementation

Q: Analyze Call Drop Rates Pre- and Post-Update Implementation

This question evaluates a data scientist's competency in causal inference and quasi-experimental analysis for observational telemetry, emphasizing time-series reasoning and handling confounding in product metrics.

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Question

Evaluate a Non-Experimental Release: Google Meet Call Drops

Scenario

Engineers shipped a new Google Meet version intended to reduce call drops. A traditional A/B test was not possible.

Task

Describe analytical methods you would use to determine whether the new version is effective without a traditional experiment.

You may consider

Pre–post (interrupted time series)
Synthetic controls / Bayesian structural time series
Difference-in-differences (including staggered adoption, event studies)
Hold-out markets or geo-based quasi-experiments
Regression discontinuity in time

Assume access to call-level logs (drop outcome, timestamp), version/adoption timestamps, device/network/geo covariates, and that rollout timing varied slightly across users/regions due to normal app-store waves or enterprise policies.

Analyze Call Drop Rates Pre- and Post-Update Implementation

Evaluate a Non-Experimental Release: Google Meet Call Drops

Scenario

Task

You may consider

Solution

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