AI-Driven Customer Feedback Analytics: Unlocking Latent Insights for Strategic Business Agility

Abstract

Customer feedbacks are a rare strategic asset in the modern day and age of the digital world. The volumes and unstructured format of feedback through a myriad of sources, including and not limited to social media and online reviews, direct surveys, and call center transcript data, exceed the capacity of more conventional manual analysis techniques. In this paper, the researcher discusses the disruptive power of Artificial Intelligence (AI) and more sophisticated Natural Language Processing (NLP) practices in reinventing the way companies gather, evaluate, and respond to customer intelligence. In this paper, the review of AI applications in customer feedback analysis goes over sentiment analysis, topic modeling, emotion detection, and text summarization. By undertaking a comparative random sample using a free dataset of customer satisfaction reviews, the researcher will show you how AI can reveal macro/micro segregated details, unearth arising problems in realtime, and correlate qualitative feedback to quantifiable business results. Technical, ethical (e.g., bias, privacy), and organizational issues and challenges that are critical to the effective AI implementation are also discussed with the proposed strategic managerial implications and best practices on how to make AI work. The paper ends by underlining the importance of AI in adopting a proactive but
customer-centered business strategy, which would lead to the development of competitiveness based on intelligent augmentation and not complete automation.