Start by defining success metrics that capture both direct revenue and network effects, then outline an experimentation strategy that addresses interference through cluster randomization or other designs. Incorporate variance reduction, power calculations, sequential monitoring, and a decision framework for rollout.
Pro tip: Proactively discuss the trade-off between cluster randomization and individual randomization, and propose a method to measure and adjust for interference, such as using a exposure-based analysis or a causal inference technique.
Identify primary metrics (e.g., revenue per user, conversion rate) and guardrail metrics (e.g., user engagement, churn). Consider network effects by including metrics like virality or sharing rate.
Choose a randomization unit (e.g., user, cluster) that minimizes interference. Consider cluster randomization (e.g., by geography or social graph clusters) or switchback designs. Plan to measure interference and adjust analysis.
Use techniques like CUPED or stratification to reduce variance. Conduct power calculations accounting for cluster randomization and interference, ensuring adequate sample size.
Set up sequential testing with alpha spending or group sequential boundaries to allow early stopping for efficacy or futility while controlling Type I error.
Define decision criteria based on statistical significance, practical significance, and guardrail metrics. Consider a phased rollout to monitor long-term effects and interference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.