Introduction
The sudden 30% increase in customer support tickets related to late deliveries on BigBasket in the past week is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for our delivery service.
I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into our product ecosystem, metrics, and potential internal causes. We'll generate data-driven hypotheses, conduct root cause analysis, and develop a comprehensive plan to resolve the issue and prevent future occurrences.
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: System changes often have unintended consequences on delivery performance. Expected answer: Yes, there was a minor update to route optimization. Impact on approach: If confirmed, we'd focus on technical issues related to the update.
Why it matters: This helps us narrow down whether it's a systemic issue or related to specific user groups or regions. Expected answer: The increase is more pronounced in urban areas. Impact on approach: We'd investigate urban-specific factors like traffic patterns or local events.
Why it matters: Changes in our delivery network could directly impact our ability to meet delivery promises. Expected answer: No major changes, but there's been some turnover in delivery partners. Impact on approach: We'd look into training and onboarding processes for new delivery partners.
Why it matters: A sudden increase in order volume could strain our delivery capacity. Expected answer: There was a flash sale last week. Impact on approach: We'd analyze our capacity planning and demand forecasting processes.
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