##### Scenario Enterprise clients complain that Google Meet frequently disconnects. ##### Question Outline an end-to-end analysis plan to diagnose why calls drop and quantify business impact. How would you estimate the effect of these drops on enterprise-contract renewal (retention)? Given your findings, how would you decide whether to prioritize fixing the bug or building a new solution? ##### Hints Think data sources, funnel/usage metrics, causal inference or back-of-envelope retention loss, cost-benefit for roadmap trade-offs.

Evaluates reliability analytics for Google Meet disconnections and enterprise renewal impact. Strong answers define call drops, use client and server logs to localize root causes, estimate account-level retention impact, and prioritize fixes using quantified business risk.

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This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Google.

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Diagnose Google Meet Disconnections and Assess Business Impact

Enterprise clients report that Google Meet calls frequently disconnect. You need to diagnose why calls drop, quantify business impact, and decide whether fixing the issue should be prioritized over new product work.

Constraints & Assumptions

Treat this as a reliability and business-impact diagnosis.
Define call drop consistently before measuring it.
Include both technical root-cause analysis and customer retention impact.
Use causal or quasi-causal methods where possible rather than raw correlations alone.

Clarifying Questions to Ask

What counts as a disconnect versus a user intentionally leaving?
Is the issue concentrated by customer, region, device, browser, app version, network, or meeting size?
When did complaints begin, and was there a release or infrastructure change?
What renewal or support data is available at the enterprise-account level?

Part 1 - Diagnose the Drops

Outline an end-to-end analysis plan to diagnose why calls drop.

What This Part Should Cover

Event definitions, client/server logs, media server errors, network type, device/browser, app version, meeting size, region, ISP, and incident data.
Funnel metrics for joining, connection, media establishment, reconnect, drop, and normal leave.
Segmentation and root-cause hypotheses.

Part 2 - Quantify Business Impact

How would you estimate the effect of call drops on enterprise contract renewal or retention?

What This Part Should Cover

Account-level reliability exposure, support tickets, usage, contract terms, renewal outcomes, and customer value.
Causal approaches such as matched accounts, panel regression, difference-in-differences, instrumental variation, or natural experiments.
Back-of-the-envelope impact and confidence intervals.

Part 3 - Prioritize Fix Versus New Work

Based on your findings, how would you decide whether to prioritize fixing the bug versus building a new solution?

What This Part Should Cover

Expected business impact, customer trust, severity, engineering cost, risk, SLA commitments, churn reduction, and opportunity cost.
Decision framework with guardrails and escalation criteria.

What a Strong Answer Covers

A strong answer defines the reliability metric, localizes technical causes, estimates contract impact credibly, and makes a product-roadmap recommendation using quantified business risk.

Follow-up Questions

How would you distinguish client-side network problems from Meet infrastructure problems?
What if high-value customers have fewer drops but more complaints?
How would you communicate the priority decision to engineering leadership?