Introduction
The increase in cancelled rides during peak hours presents a significant challenge for Uber's service quality and reliability. This issue directly impacts user satisfaction, driver earnings, and overall platform efficiency. To address this problem, I'll employ a systematic approach to identify the root cause, validate hypotheses, and develop both short-term and long-term solutions.
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: Understanding the timeline helps identify potential triggers or changes that coincide with the increase. Expected answer: Yes, there's been a noticeable increase over the last 2-3 months. Impact on approach: A recent change would narrow our focus to recent product updates or market shifts.
Why it matters: This helps us determine if the issue is universal or specific to certain user behaviors or expectations. Expected answer: Cancellations are higher among infrequent users and during surge pricing periods. Impact on approach: This would lead us to investigate pricing strategies and user education initiatives.
Why it matters: Changes in peak hour patterns could indicate broader shifts in user behavior or market conditions. Expected answer: Peak hours have remained consistent, typically during morning and evening commutes. Impact on approach: This would focus our attention on capacity issues during these specific time windows.
Why it matters: Recent changes could be directly or indirectly contributing to the increase in cancellations. Expected answer: We introduced a new surge pricing algorithm and updated the driver acceptance rating system. Impact on approach: This would lead us to closely examine these changes and their potential unintended consequences.
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