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I spent way too long on the profit formula and not enough time being precise about the estimand.
Start by clearly defining the causal estimand as the incremental profit attributable to the loyalty program, then outline a difference-in-differences or synthetic control design using pre- and post-launch data. Finally, present the exact profit formula, breaking down each component and explaining how to estimate them from the data.
Pro tip: Emphasize that the estimand must be precisely defined (e.g., average treatment effect on the treated) and that the profit formula should account for both direct and indirect effects, including cannibalization and fraud, to avoid overstating program value.
Specify the target quantity as the incremental profit per household due to the loyalty program, using a potential outcomes framework (e.g., ATT). Clarify the time horizon and population.
Propose a quasi-experimental design such as difference-in-differences or synthetic control, leveraging the staggered enrollment dates and pre-launch data to control for confounders.
Express incremental profit as: ΔProfit = (Incremental Gross Margin) - (Cannibalization Loss) - (Coupon Funding) - (Acquisition Cost) - (Servicing Cost) - (Fraud/Breakage Loss). Define each term mathematically.
Describe how to estimate incremental gross margin (using treated vs. control), cannibalization (via diversion ratios), coupon funding (from redemption data), acquisition/servicing costs (from cost data), and fraud/breakage (from historical rates).
Discuss robustness checks (e.g., placebo tests, sensitivity analysis) and how to interpret the final estimate for business decisions.
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DiD is my comfort zone so I felt okay here.
Start by framing the loyalty program as a treatment and define the target estimand (e.g., ATT). Then present a difference-in-differences (DiD) specification with two-way fixed effects, clearly stating the outcome, treatment indicator, and fixed effects. Finally, outline the key assumptions and how you would test each one, emphasizing robustness checks and potential threats to validity.
Pro tip: Acknowledge that staggered adoption of loyalty programs can bias standard two-way fixed effects DiD; mention modern estimators like Callaway & Sant'Anna or Sun & Abraham as a robustness check. This shows you're up-to-date with causal inference best practices.
Clarify the treatment (loyalty program enrollment) and the outcome (e.g., monthly spend, retention). Specify the target estimand, such as the average treatment effect on the treated (ATT).
Present a two-way fixed effects model: Y_it = α + β*(Treated_i * Post_t) + γ_i + δ_t + ε_it, where Y_it is the outcome, Treated_i is the treatment group indicator, Post_t is the post-period indicator, γ_i are unit fixed effects, and δ_t are time fixed effects.
Discuss parallel trends (test via pre-trend event study), composition stability (check for differential attrition or entry), seasonality (include time fixed effects or seasonal controls), and event timing (ensure treatment timing is exogenous and not anticipated).
Consider staggered adoption, heterogeneous treatment effects, and spillovers. Propose robustness checks like alternative control groups, synthetic control, or modern DiD estimators.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining guardrail metrics that ensure the loyalty program doesn't harm key business health indicators, then outline a robust detection and mitigation plan for selection bias due to voluntary enrollment. Emphasize the use of causal inference methods and sensitivity analyses to validate findings.
Pro tip: Leverage techniques like propensity score matching or instrumental variables to adjust for selection bias, and always pre-register your analysis plan to avoid p-hacking. Demonstrating awareness of the trade-offs between statistical rigor and practical constraints will set you apart.
Select metrics that capture potential negative side effects of the loyalty program, such as customer satisfaction (NPS), churn rate, average order value, and support ticket volume. Ensure they are aligned with long-term business goals and are sensitive to changes.
Compare enrolled vs. non-enrolled customers on observable characteristics (demographics, purchase history) using statistical tests (e.g., t-tests, chi-square) and visualize differences. Also, check for unobserved confounding by assessing pre-treatment trends and using placebo tests.
Apply methods like propensity score matching, stratification, or inverse probability weighting to create comparable groups. Consider instrumental variables or regression discontinuity if a natural experiment exists. Conduct sensitivity analysis to assess robustness.
If possible, run a randomized controlled trial (e.g., encourage enrollment randomly) to eliminate selection bias. Otherwise, use quasi-experimental designs (difference-in-differences, synthetic control) and triangulate findings.
Continuously track guardrail metrics and bias indicators over time. Set up alerts for significant deviations and be prepared to adjust the program or analysis as new data emerges.
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Honestly the part I was least prepared for.
Start by clarifying the experiment's goal and primary metric, then outline the inputs needed for sample size calculation: baseline conversion rate, significance level, power, and minimum detectable effect (MDE). Explain how you would derive MDE based on business impact and practical constraints, and finally describe how you would compute sample size and power using standard formulas or tools.
Pro tip: Always discuss the trade-offs between MDE, sample size, and experiment duration, and emphasize the importance of aligning MDE with business relevance, not just statistical significance.
Identify the key metric (e.g., conversion rate) and the expected baseline value. Confirm whether the test is one-sided or two-sided and the desired significance level (alpha) and power (1-beta).
List required inputs: baseline conversion rate, minimum detectable effect (absolute or relative), significance level (typically 0.05), power (typically 0.8), and possibly variance for continuous metrics. Discuss how to estimate these from historical data or pilot studies.
Explain that MDE should be the smallest effect that is practically meaningful for the business. Consider factors like revenue impact, cost of implementation, and stakeholder expectations. If unsure, start with a relative lift (e.g., 5%) and adjust based on feasibility.
Use formulas (e.g., for proportions: n = (Z_alpha/2 + Z_beta)^2 * (p1(1-p1) + p2(1-p2)) / (p1-p2)^2) or tools like Python's statsmodels. Explain how to compute power given sample size, or vice versa. Mention that for sequential testing, adjustments may be needed.
Check if the required sample size is feasible given traffic and duration. If not, revisit MDE or consider alternative designs. Communicate assumptions and trade-offs to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the goal: validate the evaluation framework's ability to detect true nulls (A/A) and to recover known effects (placebo DiD) before launch. Then detail the design: time windows (e.g., 2-week A/A, 4-week pre-launch placebo with a 2-week pre-period and 2-week post-period), cohort definitions (random split of eligible users, matched placebo cohorts), and decision rules (e.g., false positive rate ≤5%, power ≥80%, effect estimate within ±10% of true effect). Finally, specify ship/rollback criteria based on these metrics.
Pro tip: Use a placebo treatment that you know has zero effect (e.g., a fake feature flag) to test for false positives, and pre-register your analysis plan to avoid p-hacking. Also, simulate the entire pipeline with synthetic data to stress-test edge cases before running the live tests.
Clearly state what you aim to validate: false positive rate under the null (A/A) and sensitivity to a known effect (placebo DiD). Specify metrics like Type I error, power, and bias in effect estimation.
Randomly split a homogeneous cohort into two groups (e.g., 50/50) over a fixed window (e.g., 2 weeks). Analyze the difference in primary metric using the same statistical test as the real experiment; repeat multiple times to estimate false positive rate.
Select a pre-period (e.g., 2 weeks before placebo) and post-period (e.g., 2 weeks after). Assign a placebo treatment (e.g., a sham feature) to a random cohort; compare changes in outcomes between placebo and control groups using DiD, checking for spurious effects.
Set thresholds: e.g., A/A false positive rate ≤5% across simulations, placebo DiD effect not statistically significant (p>0.05) and within ±10% of zero. If violated, roll back and debug the framework; else, proceed to launch.
If validation fails, diagnose issues (e.g., randomization, metric definition, confounding) and rerun. Document all design choices, analyses, and outcomes for transparency and future reference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.