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This part took me way longer than I expected.
Start by summarizing the key findings from your SQL analysis, focusing on program profitability and advertiser spend efficiency. Then propose a budget allocation that shifts spend toward high-ROI programs and advertisers, while setting aside a portion for testing to manage risk. Finally, provide a back-of-the-envelope incremental profit estimate with clear assumptions.
Pro tip: Quantify the trade-off between expected profit and risk by using a simple metric like profit per dollar spent, and suggest a phased rollout to validate assumptions before full commitment.
Briefly state the most important insights from your SQL analysis, such as which programs and advertisers have the highest net profit margins and which have diminishing returns.
Propose specific percentage allocations for each program based on their historical ROI and strategic importance, ensuring diversification to mitigate risk.
Outline rules for allocating budget within programs, such as prioritizing advertisers with high incremental profit per dollar or capping spend for those with saturation.
Provide a back-of-the-envelope calculation of expected incremental profit from the proposed allocation, stating assumptions like constant marginal ROI or linear scaling.
Explain how the allocation manages risk, e.g., by limiting exposure to any single program or advertiser, and including a test budget for exploration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a geo holdout over the randomized advertiser split because advertiser-level randomization felt too noisy given how skewed spend tends to be.
Start by clarifying the budget allocation strategy and the business context (e.g., marketing spend, incentives). Then design a randomized controlled experiment with a clear unit of randomization (e.g., user or region), justify sample size via power analysis, define a primary KPI tied to the strategy's goal, and establish guardrail metrics. Finally, specify success criteria and a go/no-go decision framework based on statistical and practical significance.
Pro tip: Consider potential interference between units (e.g., budget shifts affecting multiple users) and propose mitigation like cluster randomization or switchback tests. Also, pre-register your analysis plan to avoid p-hacking and ensure stakeholder alignment on decision criteria.
Understand the proposed budget allocation strategy, its intended impact, and formulate a testable hypothesis (e.g., new allocation increases incremental profit). Identify the population and constraints.
Select the unit of randomization (e.g., user, region, or time-based switchback) based on interference risks and operational feasibility. Ensure randomization is unbiased and groups are comparable.
Conduct power analysis using expected effect size, variance, significance level (α=0.05), and desired power (80%). Account for multiple testing if needed and estimate required sample size and duration.
Select a primary KPI (e.g., incremental revenue per user) aligned with the strategy's goal. Add guardrail metrics (e.g., customer satisfaction, long-term retention) to detect unintended harm.
Predefine what success looks like: statistically significant improvement in primary KPI without degrading guardrails. Specify go/no-go thresholds (e.g., p<0.05 and lift > X%) and consider practical significance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Segmentation by advertiser size and new vs.
Start by clarifying the experiment's goal and metrics, then outline a structured analysis plan that covers segmentation, seasonality adjustments, regression to the mean, additional data needs, and confounder mitigation. Emphasize a rigorous, step-by-step approach that balances statistical validity with practical business insights.
Pro tip: Always pre-specify your analysis plan and segmentations before looking at the data to avoid p-hacking and ensure your findings are credible. Additionally, consider using holdout groups or pre-period data to validate your adjustments for seasonality and regression to the mean.
Confirm the experiment's primary and secondary metrics, the hypothesis, and the expected effect size. This ensures your analysis aligns with business goals and statistical power.
Identify key segments such as user demographics, behavior, platform, and geography. Predefine these segments to avoid data dredging and to uncover heterogeneous treatment effects.
Use historical data to model seasonal patterns and adjust for them. For regression to the mean, consider using difference-in-differences, propensity score matching, or including pre-period covariates.
Ask for data on user attributes, exposure, and external factors like promotions or holidays. This helps control for confounders and enrich your analysis.
List potential confounders such as selection bias, novelty effects, and external events. Address them through randomization checks, stratification, or sensitivity analyses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.