Introduction
The recent 15% decrease in average trip distance for Cabify Lite over the past month is a significant shift that warrants thorough investigation. This analysis will systematically explore potential root causes, generate data-driven hypotheses, and propose actionable solutions to address this change in user behavior.
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 can significantly impact travel behavior. Expected answer: No significant seasonal changes noted. Impact on approach: If confirmed, we'll focus more on internal factors.
Why it matters: Identifying specific affected segments can narrow down potential causes. Expected answer: The decrease is more pronounced among occasional users. Impact on approach: We'll investigate factors that might be deterring occasional users from longer trips.
Why it matters: Product changes can directly influence user behavior. Expected answer: A minor UI update was implemented, but no significant feature or pricing changes. Impact on approach: We'll examine if the UI change could have inadvertently affected trip distance selection.
Why it matters: Competitor actions can impact user choices and trip patterns. Expected answer: A major competitor launched an aggressive marketing campaign for short trips. Impact on approach: We'll consider how to differentiate Cabify Lite for longer trips.
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