Data-Driven Insights in Consumer and Workforce Analytics Using AI and Natural Language Processing
Abstract
Artificial Intelligence (AI) and Natural Language Processing (NLP) have become major pillars in modern enterprise intelligence - transforming the way in which organizations analyze and act on large amounts of information. In this paper, data-driven methods to provide actionable insights through consumer behavior, workforce data by AI and Natural Language Processing models are reviewed. It explains how sentiment analysis, predictive modeling and machine learning enabled HR analytics plays an important role in improving HR decision making, engagement and productivity. The proposed framework addresses the structured and unstructured processing for strategic planning in the context of marketing and HR. A thorough review of the state-of-the-art available studies shows that real-time monitoring of the plume, feedback assimilation, and performance prediction are possible based on AI-based analytics. Recommendations for impacts suggest the development of more transparency and better operational efficiency and market competitiveness due to AI-NLP implementation. Future directions are multimodal forms of analytics that fuse text, speech and behavioral information to improve how a company understands and grammatical learning throughout omers and organizational ecosystems.