NLP-driven customer segmentation: A comprehensive review of methods and applications in personalized marketing
Gourab Nicholas Rodrigues, Mustahsan Mir, Maniruzzaman Bhuiyan, Mohamed Rafi, Ajnabiul Hoque, Jannatul Maua, M. F. Mridha · Data Science and Management · 2025
In an era of digital interactions and data proliferation, understanding customer behavior and preferences has become crucial for businesses that aim to enhance brand loyalty and optimize marketing strategies. Natural language processing (NLP) has emerged as a transformative technology for customer segmentation, offering sophisticated techniques for analyzing unstructured data and deriving actionable insights. This review examines 170 peer-reviewed studies exploring NLP approaches applied to customer segmentation. It critically evaluates methodologies ranging from traditional techniques, such as topic modeling, sentiment analysis, and feature extraction, to advanced deep learning and transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), and other state-of-the-art frameworks. This review categorizes these approaches based on supervised, unsupervised, and semi-supervised learning paradigms, offering an in-depth analysis of their applicability across diverse industries such as retail, finance, and e-commerce. It also discusses the practical implications of NLP-driven segmentation for developing personalized marketing strategies, fostering customer engagement, and creating tailored experiences. Furthermore, the study evaluates the performance of these methodologies against key metrics such as accuracy, scalability, and computational efficiency while also addressing challenges related to data privacy, interpretability, and ethical considerations. By highlighting emerging trends and future directions in the field, such as multilingual NLP and multimodal segmentation, this review aims to provide researchers and practitioners with a roadmap for leveraging NLP to effectively drive personalized marketing efforts. Ultimately, this study seeks to bridge the gaps between NLP methodologies and their real-world applications, contributing to the development of more nuanced and customer-centric marketing strategies.