How can we explain the sudden 30% increase in customer support tickets related to ZS's AI-enabled forecasting tool in the past two weeks?

Introduction

The sudden 30% increase in customer support tickets related to ZS's AI-enabled forecasting tool over the past two weeks 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 the context, then ruling out external factors before diving deep into product understanding, metric breakdown, and hypothesis generation. We'll then conduct a thorough root cause analysis, propose validation methods, and outline a comprehensive resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent update. Has there been any significant change or update to the AI-enabled forecasting tool in the last month?

Why it matters: Recent changes often correlate with support ticket spikes. Expected answer: Yes, a minor update was released three weeks ago. Impact on approach: If confirmed, we'd focus on the update's features and potential bugs.

• Considering user segments, I'm curious about the distribution. Are these support tickets coming from a specific user segment or spread across all users?

Why it matters: Helps identify if the issue is widespread or segment-specific. Expected answer: Tickets are primarily from enterprise users. Impact on approach: We'd investigate enterprise-specific features or usage patterns.

• Regarding the nature of support tickets, I'm wondering about the content. What are the top 3 issues reported in these support tickets?

Why it matters: Identifies common themes or potential systemic issues. Expected answer: Accuracy concerns, slow performance, and integration problems. Impact on approach: We'd prioritize investigating these specific areas in our analysis.

• Thinking about external factors, has there been any significant change in the data sources or market conditions that our AI model relies on?

Why it matters: External data changes could affect forecast accuracy. Expected answer: No major changes reported in data sources or market conditions. Impact on approach: We'd focus more on internal factors if external conditions are stable.