My first instinct was to go straight to engagement numbers, which felt a bit shallow in hindsight.
Start by clarifying the product vision and target user problems for ChatGPT Search, then propose a metrics framework that balances user engagement, answer quality, and business impact. Prioritize metrics based on the product's stage and strategic goals, and suggest how to use A/B testing to validate improvements.
Pro tip: Emphasize that for an AI product like ChatGPT Search, traditional search metrics like CTR can be misleading; instead, focus on user satisfaction and task completion, and consider counter-metrics to guard against optimizing for engagement at the expense of accuracy.
Articulate the core problem ChatGPT Search solves and its target users, aligning metrics with the product vision and OpenAI's mission.
Propose metrics in categories such as user engagement (e.g., DAU, queries per user), answer quality (e.g., accuracy, relevance), and business impact (e.g., retention, conversion to paid).
Explain how priorities shift over time: early on, focus on quality and engagement; later, focus on retention and monetization.
Describe how to validate metric improvements through controlled experiments, ensuring changes drive meaningful gains without harming user experience.
Identify potential negative side effects (e.g., increased misinformation) and propose counter-metrics to ensure balanced optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.