This is the kind of question where you think you know what they want and then halfway through your answer you realize you've been talking about the wrong thing.
Frame the problem as optimizing ad load to maximize long-term user value and revenue, not just short-term gains. Segment users and use data to find the optimal ad frequency that balances engagement and monetization. Propose a test-and-learn approach to validate the recommendation.
Pro tip: Emphasize that the goal is to maximize long-term revenue by maintaining user trust and engagement; avoid sacrificing user experience for short-term ad revenue. Mention that ad load should be dynamic and personalized based on user behavior and content type.
Define the goal: maximize long-term revenue while maintaining a healthy user experience and engagement. Consider both advertiser and user perspectives.
Select metrics such as ad revenue per user, user engagement (time spent, DAU/MAU), and user satisfaction (surveys, churn). Balance short-term and long-term indicators.
Recognize that optimal ad load varies by user demographics, usage patterns, and content type. Consider personalization and dynamic ad load.
Use historical data and experiments to model the relationship between ad load and key metrics. Identify diminishing returns and potential negative thresholds.
Propose a specific ad load strategy (e.g., start with X ads per session, adjust based on user feedback) and outline an A/B test to validate and refine.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.