Marketing Analytics
Interview Questions

Look for structured thinking: goal setting, audience research, channel selection, creative brief, launch, and iteration.

Strong candidates go beyond vanity metrics — look for ROAS, CAC, LTV, attribution windows.

Tests analytical thinking and ability to iterate based on data.

Should mention testing budgets, scaling winners, diminishing returns, and portfolio thinking.

Official blogs, beta features, community forums, testing — look for proactive learning.

Audience expansion, creative diversification, bid strategy evolution, incremental testing.

Assess depth of hands-on experience with Attribution Modeling — look for specific metrics and outcomes.

Assess depth of hands-on experience with Conversion Rate Optimization — look for specific metrics and outcomes.

Assess depth of hands-on experience with Customer Journey Analytics — look for specific metrics and outcomes.

Assess depth of hands-on experience with Marketing Mix Modeling — look for specific metrics and outcomes.

Assess depth of hands-on experience with Cohort Analysis — look for specific metrics and outcomes.

Tests strategic thinking and understanding of channel/feature trade-offs.

UTM parameters, pixel placement, event tracking, attribution models, cross-device challenges.

Sample size, confidence intervals, test duration, single-variable testing, and when to call a winner.

Look for Google Analytics, Looker Studio, Excel/Sheets, or third-party tools — focus on actionable insights.

Communication skills, data storytelling, and the ability to deliver unwelcome truths constructively.

First-touch, last-touch, linear, time-decay, data-driven models — look for nuanced understanding.

Persona development, behavioral data, lookalikes, retargeting layers, exclusions.

Funnel stages, frequency capping, creative rotation, and exclusion of converters.

Frequency metrics, CTR decline, CPC inflation, creative refresh cadence.

Source audience quality, percentage ranges, performance comparison vs. interest-based targeting.

Time management, prioritization frameworks, and ability to context-switch without dropping quality.

Data storytelling, visual reports, focus on business outcomes vs. platform metrics.

Constructive pushback, data-driven reasoning, and knowing when to align vs. escalate.

Brief writing, feedback loops, performance data sharing, creative testing frameworks.

Async communication, documentation, proactive updates, and timezone management.

Practical experience with Google Analytics 4 — look for workflow integration and data flow understanding.

Practical experience with Mixpanel — look for workflow integration and data flow understanding.

Practical experience with Amplitude — look for workflow integration and data flow understanding.

Practical experience with Heap — look for workflow integration and data flow understanding.

ROI justification, trial period, team adoption, data integration capabilities.