Why has the average time to first response for new issues increased by 30% in the last month for GitHub Enterprise customers?

Introduction

The recent 30% increase in average time to first response for new issues among GitHub Enterprise customers is a critical concern that demands immediate attention. This metric directly impacts customer satisfaction, productivity, and potentially, customer retention. In addressing this issue, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

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

Why it matters: Recent changes could directly impact response times. Expected answer: Yes, there was a major update. Impact on approach: If yes, we'd focus on the update's features and potential bugs.

• Considering user segments, I'm curious about the distribution. Is this increase uniform across all enterprise customers or concentrated in specific segments?

Why it matters: Helps identify if it's a global issue or specific to certain user groups. Expected answer: The increase is more pronounced in larger enterprise customers. Impact on approach: If segmented, we'd tailor our solutions to the most affected groups.

• Thinking about system health, I'm wondering about any changes in overall platform usage. Has there been a significant increase in the number of issues being created in the last month?

Why it matters: A surge in issues could strain the system, leading to slower response times. Expected answer: Yes, there's been a 20% increase in issue creation. Impact on approach: If yes, we'd need to consider scaling solutions.

• Considering potential data anomalies, I'm curious about our measurement systems. Have there been any changes to how we calculate or collect the "time to first response" metric?

Why it matters: Ensures we're dealing with a real issue, not a measurement error. Expected answer: No changes to the measurement system. Impact on approach: If changed, we'd need to validate the new measurement system first.