I spent too long on the algorithm part and barely touched metrics until they nudged me.
Start by clarifying the goal of ad ranking in the Play Store—likely to maximize user engagement and revenue while maintaining a positive user experience. Then propose a ranking algorithm that balances relevance, ad quality, and user experience, and define primary and secondary metrics that align with Google's business objectives and user-centric principles.
Pro tip: Emphasize the trade-off between short-term revenue and long-term user trust, and suggest guardrail metrics to prevent degradation of user experience. Show awareness of Google's existing ad systems (e.g., AdSense, AdMob) and how Play Store ads differ.
Ask clarifying questions to understand the goal: is it to maximize revenue, user engagement, or app discovery? Consider constraints like user experience, advertiser fairness, and platform policies.
Outline key factors: ad relevance (based on user context, app category, search query), ad quality (click-through rate, conversion rate), bid amount, and user experience signals (e.g., ad load time, dismissals). Propose a weighted scoring model or machine learning approach.
Choose a primary metric that directly reflects the core objective, such as total ad revenue or revenue per user, while ensuring it doesn't sacrifice user experience. Justify why this metric is most important.
Select secondary metrics that support the primary metric and capture other dimensions: user engagement (e.g., click-through rate, conversion rate), user retention, ad quality score, and advertiser satisfaction. Include guardrail metrics like user churn or negative feedback.
Explain how you would balance competing metrics, test the algorithm (e.g., A/B testing), and iterate based on performance. Mention potential long-term impacts and how to monitor them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.