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Product Management Root Cause Analysis Question: Investigating sudden drop in Uber ride volume

There's a 50% drop in the number of Uber rides per day. Diagnose the problem.

Problem-Solving Data Analysis Strategic Thinking Transportation Technology Gig Economy
Data Analysis Root Cause Analysis User Behavior Ride-Sharing Pricing Strategy

Introduction

A 50% drop in daily Uber rides is a critical issue that demands immediate attention and a thorough analysis. This significant decrease could have far-reaching implications for Uber's business model, user satisfaction, and market position. I'll approach this problem systematically, considering various factors that could contribute to such a drastic decline in ride volume.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

  • What's the timeframe for this 50% drop? Is it sudden or gradual?

  • Are all user segments equally affected, or is the drop concentrated in specific groups?

  • Have there been any recent changes to the app, pricing structure, or driver policies?

  • Are there any geographical patterns to the decline?

  • Has there been any significant negative press or social media activity recently?

  • Are we seeing similar trends in competitor ride-sharing services?

Why these questions matter: Understanding the context and specifics of the decline is crucial for accurate diagnosis. The answers will guide our hypothesis formation and data analysis.

Hypothetical answers:

  • The drop occurred over the past week, relatively suddenly.
  • The decline seems to be across all user segments but more pronounced in urban areas.
  • A new surge pricing algorithm was implemented two weeks ago.
  • No significant geographical patterns have been identified yet.
  • There's been some negative social media chatter about longer wait times.
  • Competitors haven't reported similar declines.

Impact on solution approach: These answers would focus our investigation on recent changes, urban-specific factors, and potential issues with the new pricing algorithm.

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