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I went with engagement metrics first, things like review submission rate and return visit frequency, then tried to tie them back to the core value prop of the platform.
Start by clarifying Glassdoor's core value proposition as a two-sided marketplace for job seekers and employers. Then, select a balanced set of metrics that capture user engagement, content growth, and monetization, and propose improvements using a hypothesis-driven experimentation approach.
Pro tip: Tie every metric to a specific business goal and user segment, and always suggest A/B tests to validate improvements. This shows you understand that metrics are not just numbers but tools for decision-making.
Briefly restate Glassdoor's mission and key user segments (job seekers, employers) to ensure alignment. Identify the primary business objectives, such as user growth, engagement, and revenue.
Choose 3-5 metrics that cover different aspects: acquisition (e.g., new registered users), engagement (e.g., reviews submitted, job views), retention (e.g., monthly active users), and monetization (e.g., employer subscription revenue). Prioritize metrics that reflect the health of the marketplace.
For each metric, hypothesize why it might be underperforming based on common marketplace challenges (e.g., low review volume due to lack of incentives). Use data to identify gaps and opportunities.
Suggest specific, actionable ideas to improve each metric, such as gamifying reviews, improving search relevance, or personalized job recommendations. Prioritize ideas by impact and effort.
Outline how you would test improvements (e.g., A/B tests, cohort analysis) and define success criteria. Emphasize continuous learning and iteration based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.