Google·Software Engineer·Hiring Manager Screen·Intermediate

Intermediate

Apr 2026

Summary

Interviewed for a business analyst role at Google, got a case-style question about diagnosing a marketing campaign that missed its targets. Pretty standard for this type of role but it still tripped me up a bit.

Questions Asked (1)

A marketing campaign underperformed against its goals. Walk me through how you'd investigate and respond.

Root Cause AnalysisProduct Analytics & MetricsGo-to-Market (GTM)

Author's notes

I went straight to metrics without really scoping the problem first, which I think was the wrong move.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the campaign's goals, metrics, and timeline to establish a baseline. Then systematically investigate potential causes across data, technical implementation, and external factors, and propose data-driven fixes with clear success metrics. Emphasize collaboration with cross-functional teams and a blameless post-mortem culture.

Pro tip: Frame your answer around Google's data-driven culture: mention specific tools like Google Analytics or A/B testing, and highlight how you'd use statistical significance to avoid false conclusions.

1. Clarify Goals and Metrics

Confirm the campaign's objectives, target KPIs, and expected performance to establish a clear benchmark for evaluation.

2. Gather and Validate Data

Collect data from analytics platforms, ad servers, and backend logs, and verify data quality and tracking accuracy.

3. Identify Root Causes

Analyze the data to pinpoint where performance diverged, considering factors like targeting, creative, channel, timing, and technical issues.

4. Propose and Prioritize Fixes

Develop actionable recommendations based on root causes, prioritize by impact and effort, and define success metrics for each.

5. Implement, Monitor, and Learn

Execute the fixes, monitor results, and conduct a blameless post-mortem to document learnings and prevent recurrence.

Key Points to Mention

Define clear success metrics and baseline before investigation
Check for tracking or instrumentation errors that could skew data
Segment data by audience, channel, and creative to isolate issues
Consider external factors like seasonality, competition, or platform changes
Use A/B testing or holdout groups to validate hypotheses
Collaborate with marketing, product, and data teams for holistic view
Document findings and share learnings across teams

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.