Let's say you notice a 50% increase in customer support tickets. What would you do?

Introduction

A 50% increase in customer support tickets is a significant shift that demands immediate attention and a thorough investigation. This surge could indicate underlying issues affecting user experience, product functionality, or communication channels. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this might be related to a recent product update. Has there been any significant release or change in the past week or two?

Why it matters: Recent changes often trigger unexpected issues. Expected answer: Yes, a major feature was released last week. Impact on approach: If confirmed, I'd focus on the new feature's impact.

• Considering user segments, I'm wondering if this increase is uniform across all user types. Are we seeing a disproportionate increase from any particular user segment?

Why it matters: Helps narrow down potential causes and affected areas. Expected answer: Enterprise users are reporting more issues than others. Impact on approach: I'd prioritize investigating enterprise-specific features or workflows.

• Given the scale of increase, I'm curious about the nature of these tickets. Has there been any change in the types of issues being reported?

Why it matters: Identifies specific problem areas or new issues. Expected answer: There's been a spike in login-related problems. Impact on approach: I'd focus on authentication systems and related components.

• Thinking about external factors, I'm considering if there have been any recent changes in our support system or processes. Has anything changed in how we collect or categorize support tickets?

Why it matters: Rules out false positives due to measurement changes. Expected answer: No recent changes to the support system. Impact on approach: Confirms the increase is genuine and not a data anomaly.