Why has the average shift fill time on Instawork increased from 2 hours to 6 hours for warehouse roles in the past two weeks?

Introduction

The recent increase in average shift fill time for warehouse roles on Instawork from 2 hours to 6 hours over the past two weeks is a critical issue that demands immediate attention. This significant slowdown in matching workers to shifts could impact both client satisfaction and worker engagement. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Given the sudden change, I'm wondering about recent platform updates. Have there been any significant changes to the matching algorithm or user interface in the past month?

Why it matters: Recent changes could directly impact shift fill times. Expected answer: Yes, a new matching algorithm was implemented. Impact on approach: If confirmed, we'd focus on algorithm performance and potential rollback options.

• Considering potential seasonal factors, has there been any notable change in the volume or type of warehouse shifts posted recently?

Why it matters: Shift volume fluctuations could strain the system. Expected answer: Holiday season has increased shift volume by 30%. Impact on approach: We'd need to assess system scalability and capacity planning.

• Thinking about worker behavior, has there been any change in the acceptance rate of offered shifts for warehouse roles?

Why it matters: Lower acceptance rates would naturally increase fill times. Expected answer: Acceptance rates have dropped from 80% to 60%. Impact on approach: We'd investigate factors affecting worker motivation and shift attractiveness.

• Regarding data integrity, can we confirm that the definition of "shift fill time" has remained consistent and that all systems measuring it are functioning correctly?

Why it matters: Ensures we're addressing a real issue, not a measurement error. Expected answer: No changes in definition or measurement systems. Impact on approach: If inconsistencies are found, we'd first address data accuracy before diving deeper.