Interview a marketing analyst by testing analytical rigor and the ability to turn data into decisions marketers act on. Assess SQL and spreadsheet modeling, campaign and channel performance measurement, attribution, funnel and conversion analysis, A/B test interpretation, and dashboarding in tools like GA4, Looker, or Tableau. Strong candidates pair sound analysis with clear data storytelling that guides where to invest and where to cut spend.
Run this interview to confirm both technical analysis skills and the judgment to translate findings into action. Use real or hypothetical campaign data to test attribution, funnel, and experiment reasoning. The strongest marketing analysts are rigorous with SQL and modeling, skeptical about attribution limits, and excellent at storytelling, giving marketers a clear, honest read on what is working, what is not, and where to move budget.
Walk me through how you measure campaign and channel performance and report marketing ROI.
What to look for: Defining the right metrics, connecting spend to outcomes, and honest ROI calculation rather than surface metrics.
How do you approach attribution, and what are its limitations across channels?
What to look for: Understanding of attribution models and their trade-offs, and intellectual honesty about what attribution can and cannot prove.
How comfortable are you with SQL, and how do you use it in your analysis?
What to look for: Real SQL fluency for pulling and joining marketing data, not just relying on prebuilt dashboards.
How do you analyze the funnel and conversion paths to find optimization opportunities?
What to look for: Identifying drop-off points, segmenting, and prioritizing opportunities by potential impact.
How do you analyze an A/B test to decide whether a result is real and actionable?
What to look for: Awareness of significance, sample size, and confounders, avoiding calling noise a winner.
How do you build dashboards and reports in tools like GA4, Looker, or Tableau that people actually use?
What to look for: Designing for the audience, surfacing decisions not just data, and keeping reports trustworthy and maintainable.
Tell me about an analysis that changed a marketing decision or where budget was spent.
What to look for: A real analysis, a clear recommendation, and an outcome where data drove the call.
Describe a time your data told a story stakeholders did not want to hear. How did you handle it?
What to look for: Intellectual honesty, clear communication, and standing behind the analysis tactfully.
Give an example of translating complex multi-channel data into a recommendation marketers acted on.
What to look for: Strong data storytelling that simplified without distorting and led to action.
Tell me about a time you found a data quality or tracking issue. What did you do?
What to look for: Attention to detail, skepticism about clean-looking numbers, and fixing instrumentation at the source.
A channel looks like the top performer in last-click but you suspect it is over-credited. How do you investigate?
What to look for: Probing attribution assumptions, comparing models, and reaching a defensible read rather than taking last-click at face value.
A marketer asks which channel to cut to save budget. How do you analyze it?
What to look for: Marginal ROI thinking, incrementality awareness, and a clear, honest recommendation.
An A/B test shows a small lift with limited data. How do you advise the team?
What to look for: Honest assessment of significance and risk rather than overclaiming a win.
Two reports disagree on the same metric. How do you reconcile them?
What to look for: Tracing definitions, data sources, and methodology to find the truth and prevent recurrence.
A campaign launched without proper UTM tagging, so the channel data is unreliable. How do you handle the analysis?
What to look for: Being honest about the data gap, using proxies cautiously, and fixing instrumentation so future campaigns are measurable.
How do you partner with marketers to instrument campaigns and define the right metrics up front?
What to look for: Getting involved before launch so data is measurable, not just analyzing after the fact.
How do you tailor a data story to a non-technical marketing audience?
What to look for: Clear, jargon-free communication that drives a decision rather than overwhelming with charts.
How do you push back when asked to make the data say something it does not?
What to look for: Integrity, honesty, and finding a constructive way to present the real picture.
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