Introduction
The sudden 30% increase in customer support tickets related to Simulink Real-Time 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 our product and users.
I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into our product, metrics, and user journey. From there, I'll form data-driven hypotheses, conduct root cause analysis, and propose a comprehensive plan for validation and resolution.
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 support ticket spikes. Expected answer: Yes, a major update was released. Impact on approach: If yes, we'll focus on the update's features and potential issues.
Why it matters: Helps identify if it's a targeted or widespread issue. Expected answer: Tickets are primarily from new users. Impact on approach: If concentrated, we'll investigate that specific user segment more closely.
Why it matters: Identifies common themes or potential systemic issues. Expected answer: Performance slowdowns, compatibility issues, and error messages. Impact on approach: Will guide our technical investigation and hypothesis formation.
Why it matters: Ensures we're comparing apples to apples in our metrics. Expected answer: No changes to the support process. Impact on approach: If yes, we'd need to re-evaluate our baseline metrics.
Practice similar questions
Subscribe to access the full answer