This is the kind of question where you can spiral fast if you don't anchor on what you're actually optimizing for.
Start by clarifying the goal: maximize long-term user value and revenue, not just short-term engagement. Then propose a framework that balances user experience, advertiser value, and platform health, using data and experimentation to determine the optimal mix. Finally, outline how you would measure success and iterate.
Pro tip: Acknowledge the trade-off between short-term revenue and long-term user retention, and emphasize that the right split depends on user segments and context. Show you would use holdout experiments to measure the causal impact of ads on engagement.
Define the primary goal (e.g., maximize long-term revenue while maintaining user engagement) and constraints (e.g., user satisfaction, advertiser demand, content diversity).
Select metrics that capture both user and business value: daily active users, time spent, ad revenue, user satisfaction, and ad quality metrics like click-through rate and relevance.
Recognize that the optimal split varies by user demographics, usage patterns, and time of day. Consider new vs. existing users, and high vs. low engagement users.
Run controlled experiments (A/B tests) with different ad-to-suggestion ratios to measure causal impact on key metrics. Use holdout groups to assess long-term effects.
Use experiment results to build a model that dynamically allocates space based on predicted user response and advertiser value. Continuously monitor and adjust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.