Introduction
iMerit Technology's data annotation service for autonomous vehicles has experienced a 15% decrease in client satisfaction scores over the past quarter. This significant drop in a crucial metric requires a thorough investigation to identify the root cause and develop effective solutions. I'll approach this analysis systematically, examining both internal and external factors that could contribute to this decline.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Changes in processes can directly impact output quality and client satisfaction. Expected answer: Yes, we introduced a new annotation tool last month. Impact on approach: If confirmed, we'd focus on tool adoption and performance issues.
Why it matters: Helps identify if the issue is systemic or limited to certain client types. Expected answer: The decline is more pronounced among enterprise clients. Impact on approach: We'd investigate enterprise-specific requirements and expectations.
Why it matters: Changes in data characteristics could strain existing processes and impact quality. Expected answer: Yes, we've seen a 30% increase in complex urban environment data. Impact on approach: We'd focus on scaling annotation capabilities for complex scenarios.
Why it matters: Team changes can affect consistency and quality of annotations. Expected answer: We've onboarded 20% more annotators to handle increased demand. Impact on approach: We'd examine training and quality control processes for new hires.
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