Introduction
A sudden 30% increase in returns of Levi Strauss & Co.'s Trucker jackets in the European market this month is a significant issue that requires immediate attention. To address this problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term and long-term implications.
My analysis will follow a structured framework, beginning with clarifying questions, ruling out external factors, understanding the product and user journey, breaking down the metric, gathering data, forming hypotheses, conducting root cause analysis, and finally proposing validation methods and next steps.
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: Recent changes could directly impact return rates. Expected answer: Information about recent product or marketing changes. Impact on approach: If changes occurred, we'd focus on those specific areas first.
Why it matters: Helps identify if the issue is market-specific or widespread. Expected answer: Data on return rates by country or region. Impact on approach: Regional differences would guide our investigation towards local factors.
Why it matters: Direct customer input can quickly point to potential issues. Expected answer: Common themes in return reasons, if available. Impact on approach: Clear patterns would help prioritize certain hypotheses.
Why it matters: Production issues could lead to increased returns. Expected answer: Information on recent supply chain or manufacturing changes. Impact on approach: If changes occurred, we'd focus on quality control and production processes.
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