Start by clarifying the program's goal (e.g., improving customer satisfaction, retention, or reducing costs) and then define success metrics across customer, operational, and financial dimensions. Propose a rigorous evaluation design, such as a randomized controlled trial or quasi-experimental methods, and outline how to measure and interpret the impact.
Pro tip: Emphasize the importance of measuring long-term customer lifetime value (CLV) and retention, not just immediate satisfaction, because gift cards may drive repeat purchases but could also attract deal-seeking behavior. Also, consider the cost-effectiveness compared to traditional resolutions.
Clarify what the program aims to achieve (e.g., increase customer satisfaction, retention, or reduce costs) and formulate testable hypotheses. For example, H1: Gift cards increase repeat purchase rate compared to refunds.
Choose primary and secondary metrics (e.g., CSAT, repeat purchase rate, CLV, resolution cost) and design an experiment (e.g., randomized controlled trial) or quasi-experimental study if randomization isn't possible.
Gather data on treatment and control groups, ensuring comparability. Use statistical methods (e.g., t-tests, regression, difference-in-differences) to estimate the program's causal impact, checking for significance and effect size.
Assess whether the program improved key outcomes and calculate ROI, considering both short-term costs and long-term benefits. Analyze heterogeneous effects across customer segments.
Based on findings, recommend scaling, modifying, or discontinuing the program. Suggest further experiments to optimize gift card value or targeting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current setup and the causal question you want to answer, then outline a randomized experiment design that isolates the effect of the key change. Structure your answer around hypothesis, unit of randomization, metrics, and analysis plan, while addressing practical constraints like interference and sample size.
Pro tip: Acknowledge that a perfect randomized experiment may not be feasible in practice, and propose a pragmatic design (e.g., switchback or cluster randomization) that balances internal validity with operational constraints—this shows you understand real-world experimentation at Amazon scale.
Clearly state what change you want to test and the expected impact, ensuring the hypothesis is specific, measurable, and tied to a business metric.
Decide whether to randomize at the user, session, or cluster level, and explain how you would assign treatments to avoid bias and contamination.
Identify the key success metric (e.g., conversion, revenue) and guardrail metrics (e.g., latency, customer satisfaction) to monitor unintended consequences.
Perform power analysis to calculate required sample size and experiment duration, accounting for expected effect size, variance, and traffic.
Outline statistical tests, handling of multiple comparisons, and strategies for common issues like network effects, novelty effects, and non-compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.