The supply/demand imbalance framing clicked for me pretty fast.
Start by clarifying the metric definitions and the relationship between the two metrics, then hypothesize potential root causes across the funnel (employer, applicant, and platform factors). Prioritize hypotheses based on impact and ease of testing, and propose a data-driven plan to validate and address the issue.
Pro tip: Acknowledge that the 20% drop in response time might be a natural consequence of the 20% increase in listings—more applicants per employer could overwhelm them. This shows you understand system dynamics and avoid jumping to conclusions.
Define what 'response time' means (e.g., time to first response, time to any response) and confirm the 20% drop is statistically significant and not due to seasonality. Also, clarify the goal: is it to improve response time while maintaining listing growth, or to optimize overall marketplace health?
Break down the data by employer size, industry, job type, and applicant volume to see if the drop is concentrated in specific segments. Check if the increase in listings has led to a disproportionate increase in applications per employer, causing response delays.
List potential causes: (a) employers are overwhelmed by more applicants, (b) lower-quality listings attract less serious employers, (c) applicants are applying to more jobs, reducing employer selectivity, (d) platform changes or bugs, (e) external factors like labor market shifts.
Prioritize hypotheses by impact and feasibility. For example, if employer overload is likely, test interventions like applicant filtering, response reminders, or employer education. Use A/B tests to measure impact on response time and listing growth.
Based on findings, recommend a solution (e.g., improve matching algorithms, set response expectations, or provide employer tools). Define success metrics and iterate based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.