Why has Electric's IT ticketing system seen a 30% drop in response times over the past month?

Introduction

Electric's IT ticketing system has experienced a 30% drop in response times over the past month, indicating a significant decline in service efficiency. This analysis will systematically identify, validate, and address the root cause while considering both immediate and long-term implications for the product and its users.

I'll approach this issue by first clarifying key details, ruling out external factors, and then diving deep into the product's user journey and metrics. From there, I'll form data-driven hypotheses, conduct root cause analysis, and propose validation methods and solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent system update. Has there been any significant change to the ticketing system in the last 1-2 months?

Why it matters: System changes often impact performance metrics. Expected answer: Yes, there was a major update. Impact on approach: If yes, I'd focus on post-update issues; if no, I'd look at gradual degradation factors.

• Considering user segments, I'm curious about the distribution of the slowdown. Is the 30% drop consistent across all user groups or more pronounced in specific segments?

Why it matters: Helps identify if the issue is systemic or user-specific. Expected answer: The slowdown varies across user groups. Impact on approach: If varied, I'd investigate segment-specific factors; if uniform, I'd focus on system-wide issues.

• Thinking about workload, has there been a significant change in ticket volume or complexity over the past month?

Why it matters: Changes in demand can impact response times. Expected answer: Ticket volume has increased by 20%. Impact on approach: If volume increased, I'd look at scaling issues; if not, I'd focus on internal efficiency factors.

• Regarding measurement accuracy, has there been any change in how response times are calculated or tracked?

Why it matters: Ensures we're comparing apples to apples. Expected answer: No changes in measurement methods. Impact on approach: If changed, I'd reassess the metric; if not, I'd proceed with current data.