Evaluate causal power and paywall change
Problem Description: Data Scientist Analytics Cases
This interview presents two distinct analytics cases designed to assess a candidate's expertise in experimental design, causal inference, metric definition, and strategic business evaluation within a product context.
Case 1: Causal Inference with Limited Sample (Advertiser Product)
You are tasked with evaluating a new advertiser-facing optimization product called "Performance Plus." The challenge is that the eligible advertiser population for this product is small, yet leadership requires a credible causal estimate of its impact.
In your response, you should address the following:
- Randomization Unit: Specify the appropriate unit of randomization and provide a clear justification for your choice.
- Metrics: Define the primary success metric(s) and relevant guardrail metric(s). Examples include Return on Ad Spend (ROAS), Cost Per Acquisition (CPA), total spend, conversions, and advertiser retention.
- Statistical Power: Explain how you would compute statistical power or the Minimum Detectable Effect (MDE) for this experiment.
- Power Improvement Strategies: Detail methods to improve statistical power given a limited advertiser population. Consider techniques such as pre-period adjustment, blocking/stratification, extending the experiment duration, employing repeated-measures designs, or utilizing Bayesian/sequential approaches.
- Hypotheses: Formulate the null and alternative hypotheses for your study.
- Post-Experiment Checks: Describe surprising or counterintuitive findings you would investigate after the experiment concludes. This could include heterogeneous treatment effects, interference, Simpson's paradox, or survivorship bias.
Case 2: Paywall Threshold Evaluation (Subscription App)
On a subscription-based application, free users currently encounter a paywall after viewing 100 profiles within a defined quota period. The CEO proposes lowering this threshold to 80 profiles. Your task is to evaluate whether this change would be beneficial for the business.
Your discussion should cover:
- Decision Objective: Clearly define the primary business objective guiding this decision, considering potential trade-offs between short-term subscription conversion and long-term user lifetime value.
- Metrics & Trade-offs: Identify primary metrics, guardrail metrics, and discuss potential trade-offs inherent in this change.
- Experiment Design & Rollout: Outline your experiment design, including segmentation strategies and a proposed rollout plan.
- Risks: Discuss potential risks associated with this change, such as increased churn, reduced matching success, marketplace/network effects, and differential impacts on new users versus power users.
- Interpretation & Recommendation: Explain how you would interpret the experimental results and formulate a clear ship/no-ship recommendation.
AI interview prep
Core dilemma
Balancing the rigor of causal inference and statistical power with business urgency and the inherent risks of product changes impacting user experience and long-term value.
3 tradeoffs and 2 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Evaluate causal power and paywall change
Problem Description: Data Scientist Analytics Cases
This interview presents two distinct analytics cases designed to assess a candidate's expertise in experimental design, causal inference, metric definition, and strategic business evaluation within a product context.
Case 1: Causal Inference with Limited Sample (Advertiser Product)
You are tasked with evaluating a new advertiser-facing optimization product called "Performance Plus." The challenge is that the eligible advertiser population for this product is small, yet leadership requires a credible causal estimate of its impact.
In your response, you should address the following:
- Randomization Unit: Specify the appropriate unit of randomization and provide a clear justification for your choice.
- Metrics: Define the primary success metric(s) and relevant guardrail metric(s). Examples include Return on Ad Spend (ROAS), Cost Per Acquisition (CPA), total spend, conversions, and advertiser retention.
- Statistical Power: Explain how you would compute statistical power or the Minimum Detectable Effect (MDE) for this experiment.
- Power Improvement Strategies: Detail methods to improve statistical power given a limited advertiser population. Consider techniques such as pre-period adjustment, blocking/stratification, extending the experiment duration, employing repeated-measures designs, or utilizing Bayesian/sequential approaches.
- Hypotheses: Formulate the null and alternative hypotheses for your study.
- Post-Experiment Checks: Describe surprising or counterintuitive findings you would investigate after the experiment concludes. This could include heterogeneous treatment effects, interference, Simpson's paradox, or survivorship bias.
Case 2: Paywall Threshold Evaluation (Subscription App)
On a subscription-based application, free users currently encounter a paywall after viewing 100 profiles within a defined quota period. The CEO proposes lowering this threshold to 80 profiles. Your task is to evaluate whether this change would be beneficial for the business.
Your discussion should cover:
- Decision Objective: Clearly define the primary business objective guiding this decision, considering potential trade-offs between short-term subscription conversion and long-term user lifetime value.
- Metrics & Trade-offs: Identify primary metrics, guardrail metrics, and discuss potential trade-offs inherent in this change.
- Experiment Design & Rollout: Outline your experiment design, including segmentation strategies and a proposed rollout plan.
- Risks: Discuss potential risks associated with this change, such as increased churn, reduced matching success, marketplace/network effects, and differential impacts on new users versus power users.
- Interpretation & Recommendation: Explain how you would interpret the experimental results and formulate a clear ship/no-ship recommendation.