Start by clarifying the feature's goal and the marketplace context, then define success metrics that tie to business value. Structure your answer around estimating potential impact, assessing feasibility and cost, and designing a test to validate the hypothesis before full investment.
Pro tip: Emphasize the importance of running a low-cost experiment (e.g., A/B test) to measure incremental lift in key metrics like buyer engagement and GMV, rather than relying solely on intuition or surveys.
Ask questions to understand the marketplace: what types of listings, buyer interests, current notification channels, and the overarching business objective (e.g., increase buyer retention or GMV).
Identify primary and secondary metrics that reflect value, such as click-through rate, conversion rate, repeat purchase rate, and incremental GMV, while also considering guardrail metrics like notification opt-outs.
Use data to size the opportunity: how many buyers have expressed interests, how many new listings match, and what uplift in metrics is needed to justify development and infrastructure costs.
Propose an A/B test or holdout group to measure the causal effect of the feature on key metrics, ensuring proper randomization and sufficient power to detect meaningful differences.
Based on estimated impact, cost, and experimental results, recommend whether to build, iterate, or abandon the feature, and outline next steps for scaling if successful.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with user-level randomization pretty quickly and explained spillover risk if you randomize by listing instead.
Start by clarifying the notification feature's goal and the metric it aims to move, then systematically walk through the design choices: randomization unit (user-level), eligibility criteria, duration, and sample size calculation. Emphasize trade-offs and practical considerations like interference and novelty effects.
Pro tip: Always discuss how you'd handle potential interference between users (e.g., social notifications) and propose mitigation like cluster randomization or measuring spillover. Also, mention that you'd run a power analysis upfront and consider sequential testing to allow early stopping if needed.
Clarify the primary metric (e.g., engagement, click-through rate) and guardrail metrics (e.g., unsubscribe rate, user satisfaction). Ensure the notification feature's success is measurable and aligned with business goals.
Decide whether to randomize at user, session, or device level. For notifications, user-level is typical to avoid contamination, but consider cluster randomization if social spillover is likely.
Specify who is eligible (e.g., active users, new users) and any stratification variables (e.g., platform, geography) to ensure balanced groups and increase power.
Calculate required sample size using power analysis (effect size, alpha, power). Set duration to cover at least one full business cycle and account for novelty effects, typically 1-2 weeks.
Pre-register analysis plan, including how to handle multiple comparisons and sequential testing. Monitor for SRM and guardrail metrics during the experiment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The proxy metrics part was fine, I talked through saves and messages sent as leading signals.
Start by acknowledging the data limitation and proposing a hierarchy of proxy metrics that capture the user value and business impact, such as engagement, retention, and downstream conversions. Then, for the conflicting signals, advocate for a holistic evaluation using guardrail metrics and a trade-off analysis, potentially segmenting users to understand the drivers and making a decision based on net impact and long-term goals.
Pro tip: Frame your answer around the concept of a 'metric tree' where proxy metrics are leading indicators of the ultimate goal, and emphasize that no single metric should drive decisions; instead, use a combination of metrics with predefined thresholds for success and failure.
Select proxy metrics that are leading indicators of purchase, such as add-to-cart rate, product page views, or time spent on purchase-related pages. Ensure they are sensitive to changes and available in real-time.
Establish guardrail metrics that should not degrade, such as opt-out rate, spam reports, user satisfaction scores, and retention. Set acceptable thresholds for these metrics based on historical data or business rules.
Evaluate the trade-off between the primary proxy metric (e.g., clicks) and guardrail metrics. Quantify the net impact by assigning weights or using a decision framework like cost-benefit analysis.
Segment the data to see if the negative effects are concentrated in a specific user group or if they are widespread. Investigate root causes, such as ad quality or targeting issues.
Decide based on the overall net impact and strategic priorities. If guardrails are breached, consider pausing or iterating; if the trade-off is acceptable, launch with monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.