AI for Voice of Customer and Feedback Analysis

Customer feedback is a goldmine of information, but reading thousands of reviews manually is an impossible task. Modern artificial intelligence allows quality assurance and support teams to process vast amounts of customer data efficiently and accurately. This course teaches you how to leverage Large Language Models (LLMs) and modern text analysis techniques to automate the heavy lifting of Voice of Customer (VoC) research. You will learn how to turn unstructured text into clear metrics, categorize feedback automatically, and spot emerging trends before they become major issues. What you'll learn: - Understand the fundamental concepts of AI and natural language processing for business data. - Apply modern prompt engineering techniques to extract sentiment and intent from complex reviews. - Analyze large datasets of CSAT surveys to identify key drivers of customer satisfaction. - Categorize unstructured feedback into actionable product and service improvements. - Explore foundational concepts like vector search to query your customer feedback databases efficiently. - Design automated workflows to summarize daily or weekly customer support interactions. The curriculum begins with essential AI terminology and foundational concepts before moving into practical, text-based exercises. You will read through real-world scenarios, examine data formatting strategies, and study prompt templates that illustrate how to structure feedback for AI analysis. Designed entirely for beginners, this text-based course requires no prior programming or data science experience—just a desire to understand your customers better. Start learning today to transform how your organization listens to its customers.