Why has the average order value for Gojek's GoFood decreased by 15% in the past month?

Introduction

The recent 15% decrease in average order value (AOV) for Gojek's GoFood service over the past month is a significant concern that requires immediate attention and a thorough root cause analysis. As we delve into this issue, we'll systematically examine potential factors, generate data-driven hypotheses, and develop a comprehensive plan to address and reverse this trend.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal effect. Has this decrease coincided with any particular holiday or event in the regions where GoFood operates?

Why it matters: Seasonal trends can significantly impact consumer behavior and spending patterns. Expected answer: No significant holidays or events during this period. Impact on approach: If confirmed, we'd need to look more closely at internal factors or market changes.

• Considering the specificity of the decrease, I'm wondering about recent changes to the app or service. Have there been any notable updates or feature launches in the past 1-2 months?

Why it matters: Product changes can directly influence user behavior and order values. Expected answer: A new UI for the ordering process was implemented 6 weeks ago. Impact on approach: This would shift our focus to user experience and potential friction points in the new interface.

• Given the competitive nature of food delivery, I'm curious about market dynamics. Have there been any significant moves from competitors or new entrants in the past month?

Why it matters: Competitive pressures can lead to changes in user behavior or pricing strategies. Expected answer: A new competitor launched with aggressive discounting 3 weeks ago. Impact on approach: We'd need to analyze our pricing and promotion strategies in response to market changes.

• Thinking about the breadth of the issue, I'm interested in user segmentation. Is this decrease uniform across all user segments, or are certain groups more affected?

Why it matters: Understanding which users are most affected can help target our solutions more effectively. Expected answer: The decrease is more pronounced among occasional users rather than frequent ones. Impact on approach: We'd focus on retention strategies for occasional users and analyze what's keeping frequent users engaged.