Why has Grubhub's order completion rate dropped by 15% for restaurants in Chicago over the past month?

Introduction

Grubhub's 15% drop in order completion rate for Chicago restaurants over the past month is a critical issue that demands immediate attention. This metric directly impacts customer satisfaction, restaurant partnerships, and overall platform performance. I'll approach this problem systematically, focusing on identifying potential root causes, validating hypotheses, and developing both short-term fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal factor. Has Chicago experienced any unusual weather patterns in the last month?

Why it matters: Extreme weather can significantly impact food delivery operations. Expected answer: No significant weather anomalies. Impact on approach: If weather is a factor, we'd need to consider operational adjustments.

• Considering potential system changes, have there been any recent updates to the order routing algorithm or restaurant partner onboarding process?

Why it matters: Technical changes could inadvertently affect order completion rates. Expected answer: A minor update to the order routing system was implemented three weeks ago. Impact on approach: This could narrow our focus to technical root causes.

• Regarding user segments, is this drop consistent across all types of restaurants, or is it more pronounced in certain categories?

Why it matters: Identifying patterns in affected restaurants could reveal specific issues. Expected answer: The drop is more significant for high-volume, fast-casual restaurants. Impact on approach: We'd need to investigate factors specific to these restaurant types.

• Thinking about performance metrics, has there been any change in how order completion rate is calculated or measured in the past month?

Why it matters: Ensures we're comparing apples to apples in our analysis. Expected answer: No changes to the metric calculation. Impact on approach: Confirms the issue is real and not a data anomaly.