I started with ARPU and retention which felt right, but I underweighted ad-load elasticity early on and the interviewer kept nudging me toward it.
Start by framing the trade-off as a multi-objective optimization problem, then outline a metrics framework that captures both user engagement and monetization. Propose an experiment design that measures long-term effects, and discuss business considerations like user experience and advertiser value.
Pro tip: Emphasize the importance of long-term holdout groups to measure delayed effects, as short-term experiments can mislead due to novelty effects or user fatigue.
Identify key metrics for DAU (e.g., daily active users, session frequency, time spent) and ad revenue (e.g., ad impressions, CTR, CPM, total revenue). Consider guardrail metrics like user satisfaction and retention.
Propose an A/B test with treatment groups varying ad load or targeting. Include a control group and measure both short-term and long-term effects using holdout groups.
Use statistical methods to quantify the impact on DAU and revenue. Calculate metrics like incremental revenue per incremental DAU, and assess statistical significance and confidence intervals.
Discuss factors like user experience degradation, advertiser demand, competitive landscape, and platform goals. Consider segment-level analysis (e.g., new vs. existing users).
Synthesize findings to recommend an optimal balance, and propose ongoing monitoring and iterative testing to adapt to changing conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.