You are a PM at a food delivery firm. Your data analyst comes up to you and tells you that there is a spike at breakfast, dinner and lunch time. However, at lunch, the conversion is 2% when compared to dinner and breakfast which is 10%. How would you go about solving this problem?

Introduction

As a PM at a food delivery firm, we're facing a significant conversion rate discrepancy during lunch hours compared to breakfast and dinner. This trade-off scenario requires us to analyze why our lunch conversion rate is only 2% versus 10% for other meal times and develop strategies to improve it.

Step 1

Clarifying Questions (3 minutes)

• What's our current market share for each meal time?

• Why: Helps understand competitive landscape and growth potential
• Hypothetical answer: 15% breakfast, 5% lunch, 20% dinner
• Impact: May indicate untapped lunch market or fierce competition

• Are there differences in user demographics or behaviors across meal times?

• Why: Could reveal targeting or product-market fit issues
• Hypothetical answer: Lunch users skew younger, more price-sensitive
• Impact: Might need to adjust pricing or offerings for lunch crowd

• How does our restaurant selection differ for lunch compared to other times?

• Why: Could indicate supply-side issues affecting conversion
• Hypothetical answer: 30% fewer restaurants available during lunch
• Impact: May need to focus on expanding lunch restaurant partnerships

• What's the average order value (AOV) for each meal time?

• Why: Helps understand economic impact of improving lunch conversion
• Hypothetical answer: Breakfast $15, Lunch $12, Dinner $25
• Impact: Improving lunch conversion could have significant revenue impact

• Are there any technical issues or app performance differences during lunch hours?

• Why: Could reveal underlying technical problems affecting conversion
• Hypothetical answer: 20% slower app response time during lunch peak
• Impact: May need to prioritize technical optimizations for lunch traffic