Analyze Call Drop Rates Pre- and Post-Update Implementation
Quick Overview
Evaluates non-experimental causal analysis for a Google Meet update intended to reduce call drops. Strong answers build a counterfactual using interrupted time series, synthetic control, difference-in-differences, event studies, or rollout variation, and validate assumptions with placebo and pre-trend checks.
Analyze Call Drop Rates Pre- and Post-Update Implementation
Company: Google
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
Engineers shipped a new Google Meet version aimed at reducing call drops, but no A/B test was possible.
##### Question
Describe analytical methods you would use to determine whether the new version is effective without a traditional experiment.
##### Hints
Consider pre-post analysis, synthetic controls, difference-in-differences, hold-out markets, or regression discontinuity.
Quick Answer: Evaluates non-experimental causal analysis for a Google Meet update intended to reduce call drops. Strong answers build a counterfactual using interrupted time series, synthetic control, difference-in-differences, event studies, or rollout variation, and validate assumptions with placebo and pre-trend checks.
Analyze Call Drop Rates Pre- and Post-Update Implementation
Engineers shipped a new Google Meet version intended to reduce call drops, but a traditional A/B test was not possible. You need to estimate whether the new version was effective.
Constraints & Assumptions
Treat this as a non-experimental causal-inference problem.
Assume call-level logs include drop outcome, timestamp, app version, adoption time, device, network, geography, and relevant covariates.
Build a counterfactual for what call-drop rate would have been without the release.
Account for rollout timing, seasonality, and user or device mix changes.
Clarifying Questions to Ask Guidance
Was the rollout gradual by market, device, enterprise policy, or app-store wave?
Are users self-selecting into the update?
Were other call-quality or infrastructure changes shipped at the same time?
What is the exact call-drop definition and denominator?
Part 1 - Analytical Methods
What methods would you use to determine whether the new version is effective without a traditional experiment?
What This Part Should Cover Guidance
Pre-post interrupted time series, synthetic control, Bayesian structural time series, difference-in-differences, event studies, holdout markets, and regression discontinuity in time.
When each method is appropriate and its assumptions.
Part 2 - Data and Metrics
What data and metrics would you use?
What This Part Should Cover Guidance
Drop rate, reconnect rate, call duration, latency, device/network covariates, app version, rollout timing, meeting size, region, and enterprise account.
Guardrails such as crashes, CPU/battery impact, latency, and user complaints.
Part 3 - Validate the Result
How would you validate that the observed change is caused by the update?
A strong answer builds a credible counterfactual, uses rollout variation when available, checks assumptions carefully, and reports uncertainty rather than relying on a naive pre-post comparison.
Follow-up Questions Guidance
What if early adopters have better devices and lower baseline drop rates?
How would you handle a concurrent infrastructure change?
Which analysis would you present to leadership and why?