Introduction
The sudden spike in failed transactions for Kredivo's e-commerce checkout integration is a critical issue that demands immediate attention. As we delve into this product root cause analysis, we'll systematically investigate the factors contributing to this unexpected performance drop. Our approach will involve a comprehensive examination of both internal and external elements, data-driven hypothesis generation, and a structured validation process.
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 performance issues. Expected answer: Yes, a new feature was deployed yesterday morning. Impact on approach: If confirmed, we'd prioritize investigating the new deployment.
Why it matters: Understanding the magnitude helps prioritize the severity of the issue. Expected answer: Normal failure rate is 0.5%, current rate is 5%. Impact on approach: A tenfold increase would indicate a severe problem requiring immediate action.
Why it matters: Segmentation can reveal patterns and narrow down potential causes. Expected answer: The issue appears to affect all user segments equally. Impact on approach: If uniform, we'd focus on system-wide issues rather than user-specific factors.
Why it matters: Unusual traffic patterns could indicate external factors or system capacity issues. Expected answer: Transaction volume is within normal range. Impact on approach: If volume is normal, we'd shift focus to internal system issues rather than capacity problems.
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