I jumped straight to solutions and had to walk it back.
Start by clarifying the problem scope and defining what 'generic' and 'personalized' mean for Yelp's users, then segment users and identify root causes through data. Propose a prioritized set of solutions, define success metrics, and outline a test-and-learn plan to validate impact.
Pro tip: Anchor your answer in a clear user problem statement and tie every proposed solution back to a measurable metric—this shows you think like a PM who balances user value with business impact.
Ask clarifying questions to understand the feedback source, user segments affected, and what 'personalized' means (e.g., based on history, location, preferences). Define the problem statement clearly.
Analyze data to identify why recommendations feel generic: lack of user data, over-reliance on popularity, insufficient signals, or poor algorithm. Segment users to see if the issue is universal or specific to certain groups.
Brainstorm potential solutions (e.g., collaborative filtering, contextual signals, explicit preferences) and prioritize using impact vs. effort, considering technical feasibility and user value.
Establish metrics like CTR, engagement, user satisfaction (NPS), and retention to measure improvement. Set a baseline and target for each metric.
Propose an A/B test or pilot to validate the solution, with clear hypotheses and success criteria. Outline a plan to iterate based on results and scale if successful.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.