Introduction
The sudden spike in customer support tickets related to the Lilium App's flight planning feature is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term implications for our product strategy.
I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into our product ecosystem. We'll break down the metric, gather relevant data, form hypotheses, and conduct a thorough root cause analysis. Finally, we'll develop a comprehensive plan to validate our findings and implement 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: Recent changes often correlate with sudden spikes in support tickets. Expected answer: Yes, a minor update was pushed last week. Impact on approach: If confirmed, we'd focus on changes in that update.
Why it matters: Helps identify if it's a localized or widespread issue. Expected answer: Mostly from frequent flyers using complex routes. Impact on approach: We'd dive deeper into advanced features used by power users.
Why it matters: Identifies specific pain points to address. Expected answer: Route optimization errors, saved routes disappearing, and integration issues with third-party weather services. Impact on approach: We'd prioritize investigating these specific areas of the feature.
Why it matters: Ensures we're not dealing with a measurement issue rather than a real problem. Expected answer: No changes to the ticketing system in the past 3 months. Impact on approach: If confirmed, we can trust the data and focus on actual user issues.
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