Diagnosing a Listings Jump and an Employer Response-Time Shift in Facebook Jobs

Facebook Jobs, a marketplace where employers post openings and job seekers apply, has recently launched. Since then, job listings are up 20%, while employer response time to applicants is down 20%. How would you investigate these two movements, and what would you do about them? ```hint Pin down the metric before the cause Decide first whether a lower response time is good or bad news here, and how the metric is computed. Then ask whether a sudden wave of new listings could move that number on its own. ``` ### Clarifying Questions - Does "response time down 20%" mean employers now reply faster, or that their responsiveness has worsened? - How is response time computed: mean or median time to first employer reply, counted only over applications that already got a reply, or including those still waiting? - What comparison is each 20% measured against (week over week, since launch), and did the change appear suddenly or gradually? - Is the listings growth spread across markets and employer types, or concentrated in a few employers or one posting channel? ### What a Strong Answer Covers - Resolving the direction and definition of the response-time metric before diagnosing - Data and logging checks, then internal and external causes, cut by employer cohort, market and listing source - Hypotheses that link the two movements, each paired with the evidence that would confirm or rule it out - A judgment on whether each movement is good or bad, and fixes tied to the confirmed cause - Metrics and guardrails to watch after acting, covering both employers and applicants ### Follow-up Questions - The slowdown turns out to come entirely from employers who posted for the first time in the last two weeks. How does your plan change? - If the change was caused by a logging update, how would you keep the team from being misled by it again? - Would you nudge employers to reply faster, and how would you test that without driving them off the product?