Introduction
The recent 25% increase in average time to fill open shifts through ShiftKey's app is a critical issue that demands immediate attention. This metric directly impacts our platform's efficiency and user satisfaction. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term and long-term solutions.
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Seasonal trends could explain temporary fluctuations in shift-filling time. Expected answer: Yes, it coincides with summer vacation season. Impact on approach: If seasonal, we'd focus on strategies to manage predictable fluctuations.
Why it matters: Identifying specific affected segments could point to targeted issues. Expected answer: Healthcare shifts are experiencing longer fill times. Impact on approach: We'd investigate healthcare-specific factors and tailor solutions accordingly.
Why it matters: Recent changes could have unintended consequences on shift-filling efficiency. Expected answer: A new matching algorithm was implemented last month. Impact on approach: We'd focus on analyzing and potentially reverting or tweaking the new algorithm.
Why it matters: Changes in supply or demand could affect shift-filling times. Expected answer: There's been a 15% increase in employers but only a 5% increase in workers. Impact on approach: We'd look into strategies to balance the worker-to-employer ratio.
Practice similar questions
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