Introduction
The recent 15% drop in average order value (AOV) for GoFood over the past month is a critical issue that demands immediate attention. As we delve into this problem, we'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.
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: Seasonal trends can significantly impact food ordering patterns. Expected answer: No significant seasonal events during this period. Impact on approach: If seasonal, we'd focus on historical data comparison; if not, we'd look more closely at recent changes.
Why it matters: Identifying affected segments can help pinpoint specific issues or changes in user behavior. Expected answer: The drop is more pronounced among frequent users. Impact on approach: If segment-specific, we'd tailor our investigation and solutions to those groups.
Why it matters: Recent changes can often lead to unexpected consequences in user behavior. Expected answer: A new UI was rolled out for the ordering process. Impact on approach: If recent changes occurred, we'd focus on before-and-after analysis of those specific features.
Why it matters: Competitive actions can influence user behavior and pricing strategies. Expected answer: A competitor launched an aggressive discount campaign. Impact on approach: If competition is a factor, we'd need to consider market positioning and pricing strategies.
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