This one sprawled in ways I didn't expect.
Start by clarifying the hypothesis and defining brand advertising success metrics, then propose a randomized controlled experiment (e.g., geo-based or user-level) to isolate social media's causal impact on brand outcomes. Combine survey-based brand lift metrics with behavioral data and apply appropriate statistical methods to measure significance and effect size.
Pro tip: Emphasize the importance of pre-registering the analysis plan and using holdout groups to avoid bias, and discuss how to balance statistical power with business constraints like budget and timeline.
Define what 'weak channel for brand advertising' means by specifying brand metrics (e.g., aided recall, brand favorability) and the expected direction of effect. Align with leadership on success criteria and constraints.
Propose a randomized controlled trial: either user-level randomization (if feasible) or geo-based matched markets. Ensure control group receives no social media brand ads while treatment does, and consider spillover effects.
Choose primary metrics (brand lift via surveys) and secondary metrics (engagement, conversion, reach). Collect data from ad platforms, surveys, and CRM systems, ensuring proper tracking and attribution.
Determine sample size and power, use appropriate tests (e.g., t-tests, ANOVA, or Bayesian methods) to compare treatment vs. control. Control for confounders and consider sequential testing if needed.
Analyze effect sizes and confidence intervals, assess practical significance, and provide actionable recommendations. Discuss limitations and potential next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.