Why has EliseAI's AI-powered chatbot seen a 15% drop in customer satisfaction scores over the past month?

Introduction

EliseAI's AI-powered chatbot has experienced a 15% drop in customer satisfaction scores over the past month, signaling a critical issue that demands immediate attention. To address this problem, 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 update. Has there been any significant change to the chatbot's algorithms or training data in the past 1-2 months?

Why it matters: Recent changes could directly impact performance. Expected answer: Yes, there was an update to improve efficiency. Impact on approach: If confirmed, we'd focus on the update's impact.

• Considering user segments, I'm curious about the distribution. Are we seeing this drop across all user types, or is it concentrated in specific segments?

Why it matters: Helps identify if the issue is universal or segment-specific. Expected answer: The drop is more pronounced in new users. Impact on approach: We'd investigate onboarding and first-time user experience.

• Thinking about the metric itself, has there been any change in how customer satisfaction is measured or collected?

Why it matters: Ensures we're comparing apples to apples. Expected answer: No changes to the measurement system. Impact on approach: We'd focus on actual performance issues rather than measurement discrepancies.

• Considering external factors, have there been any significant changes in customer support processes or team structure that interact with the chatbot?

Why it matters: External changes could indirectly affect chatbot performance. Expected answer: Some recent changes in support team structure. Impact on approach: We'd investigate the interplay between human and AI support.