Improving voice of the customer analysis with generative AI
Abstract
This paper explores the integration of generative artificial intelligence (GenAI) in voice of the customer (VoC) analysis to provide deeper understanding of prospects and customers. GenAI has enormous potential to enhance customer satisfaction, refine products and services and improve the customer experience. This speculative paper illustrates how GenAI can keep pace with increasing customer expectations and the volume of feedback by uncovering nuanced sentiments, trends and customer needs through context comprehension and its conversational query capabilities. The paper explores the power of GenAI in VoC analysis for improving customer satisfaction, accelerating troubleshooting and resolution and upgrading products and services. Additionally, this paper addresses the role of GenAI in advanced communication routing, agent support, multilingual support and sentiment analysis, showcasing its ability to provide comprehensive and context-aware insights.
The full article is available to subscribers to the journal.
Author's Biography
Jim Sterne sold business computers to companies that had never owned one in the 1980s, consulted and keynoted about online marketing in the 1990s, founded the Marketing Analytics Summit in 2002 and co-founded the Digital Analytics Association in 2004. His 12th book is ‘Artificial Intelligence for Marketing: Practical Applications’.
Thomas H. Davenport is the President's Distinguished Professor of Information Technology and Management at Babson College, co-founder of the International Institute for Analytics, fellow at the MIT Initiative on the Digital Economy, and senior adviser to Deloitte Analytics. He teaches analytics/big data in executive programmes at Babson, Harvard Business School and School of Public Health and MIT Sloan School.
Citation
Sterne, Jim and Davenport, Thomas H. (2024, June 1). Improving voice of the customer analysis with generative AI. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 10, Issue 1. https://doi.org/10.69554/VYPB2186.
Publications LLP