Introduction
The recent 15% drop in DiDi's ride-hailing service daily active users (DAU) over the past month is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for the product and business.
I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into product understanding, metric breakdown, and data analysis. From there, I'll form hypotheses, conduct root cause analysis, and propose validation methods and 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: Seasonal trends could explain the drop and influence our solution approach. Expected answer: Yes, this drop is unusual compared to last year's data. Impact on approach: If seasonal, we'd focus on year-over-year comparisons and seasonal strategies.
Why it matters: Uneven impact could point to specific user experience issues or changes affecting certain groups. Expected answer: The drop is more pronounced among occasional riders. Impact on approach: We'd investigate factors specifically affecting occasional riders' engagement.
Why it matters: Recent changes could directly correlate with the DAU drop. Expected answer: A new pricing algorithm was implemented 6 weeks ago. Impact on approach: We'd focus on analyzing the impact of this pricing change on user behavior.
Why it matters: External competitive factors could be drawing users away from DiDi. Expected answer: A new ride-sharing service launched in some key markets last month. Impact on approach: We'd analyze market share data and compare features with the new competitor.
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