I went straight to engagement metrics and open rates, which felt logical but in hindsight was too narrow.
Start by defining the goal of notifications—driving engagement without harming user experience—and identify key metrics like CTR, opt-out rate, and retention. Then propose a data-driven framework to test different volumes and find the optimal point where marginal benefit equals marginal cost. Finally, discuss how to set thresholds and personalize based on user behavior.
Pro tip: Frame the answer around user value and long-term retention, not just short-term engagement. Mention that the 'right' volume is dynamic and should be continuously optimized via experimentation and user feedback.
Identify primary metrics (e.g., DAU, retention) and guardrail metrics (e.g., opt-out rate, uninstall rate, notification fatigue). Establish a clear trade-off between engagement and user experience.
Break down users by engagement level, notification preferences, and past responsiveness. Analyze how notification volume correlates with key metrics across segments.
Design A/B tests with varying notification frequencies (e.g., low, medium, high) and measure impact on both engagement and guardrail metrics. Use holdout groups to measure long-term effects.
Use experiment results to find the point where additional notifications no longer improve engagement or start to harm guardrail metrics. Set a threshold (e.g., max per day/week) and consider personalization.
Continuously track metrics, gather qualitative feedback, and adjust volume based on seasonality, user lifecycle, and product changes. Implement a feedback loop for ongoing optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.