I went straight to engagement stuff: click-through rate, sessions driven by the notification, maybe bookings if the app had that.
Start by clarifying the feature's goal—driving engagement with travel recommendations—and define primary success metrics that directly measure that goal, such as click-through rate and bookings. Then, identify guardrail metrics that capture potential negative side effects, like uninstalls, unsubscribes, and notification disablement, ensuring a balanced evaluation of the feature's impact.
Pro tip: Emphasize that guardrail metrics should be monitored with non-inferiority tests to ensure the feature doesn't harm key health metrics, and consider segmenting by user engagement levels to detect heterogeneous effects.
Understand that the push notification feature aims to increase user engagement with travel recommendations, ultimately driving bookings or other core actions.
Select metrics that directly measure the feature's success in achieving its objective, such as notification click-through rate, conversion rate to booking, and frequency of app opens attributed to notifications.
Choose metrics that capture potential negative consequences, including uninstall rate, notification unsubscribe/disable rate, and user-reported satisfaction or spam reports.
Include additional metrics like overall app engagement, retention, and long-term user value to ensure the feature doesn't cannibalize other interactions.
Design an A/B test to measure these metrics, with proper sample size and duration, and plan to analyze results with statistical rigor, checking for novelty effects and segment-level impacts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the challenge of network effects in A/B testing, then propose a cluster-based randomization approach to minimize interference. Explain how you would define clusters based on sharing behavior, choose appropriate metrics, and analyze results with methods that account for clustering.
Pro tip: Consider using a switchback or time-based randomization if clusters are hard to define, but be prepared to discuss trade-offs. Also, mention that you would run a power analysis to determine the number of clusters needed.
Analyze how push notifications might affect users beyond the individual, such as through shared itineraries or social connections. Determine the potential spillover effects that could bias the experiment.
Choose a unit that groups interacting users, such as a cluster of friends, a social network component, or a geographic region. Explain why this unit minimizes interference while maintaining statistical power.
Outline the A/B test setup: how clusters are assigned to treatment/control, the duration, and the metrics (e.g., notification engagement, itinerary shares, retention). Mention any guardrail metrics to monitor.
Use statistical methods that account for cluster randomization, such as cluster-robust standard errors or mixed-effects models. Discuss how to interpret results and check for interference.
Suggest running a holdout or a follow-up experiment to confirm findings. Consider if the cluster design introduces biases and how to mitigate them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.