I went straight to volume of reviews and average rating, which in hindsight was pretty shallow.
Start by clarifying the goal of Yelp reviews (e.g., helping users make informed decisions, driving business value) and then define success metrics across user, business, and platform dimensions. Structure your answer by mapping metrics to the review lifecycle: creation, consumption, and impact, and tie them to DoorDash's context if relevant.
Pro tip: Emphasize that metrics should be actionable and balanced—avoid vanity metrics and consider counter-metrics to prevent unintended consequences like spam or bias.
Identify the primary goals of Yelp reviews: helping consumers choose businesses, providing feedback to businesses, and generating content for the platform.
Break down the review lifecycle into stages: writing, reading, and acting on reviews, and consider metrics for each stage.
Propose metrics for quantity (e.g., number of reviews), quality (e.g., helpfulness votes, sentiment), and impact (e.g., conversion, business engagement).
Select a few key metrics that align with business goals and include counter-metrics to monitor trade-offs (e.g., review volume vs. spam).
Relate metrics to DoorDash's ecosystem, such as how reviews influence restaurant orders or dasher performance, if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.