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@article{181661,
        author = {Dr. Arpana Chaturvedi and Praveen Malik and Deepankar Jugran},
        title = {A Data-driven Approach for Customer Segmentation Using RFM Analysis and K-means Clustering},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4878-4888},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181661},
        abstract = {The process of customer segmentation involves the division of a customer base into smaller groups or segments, based on shared characteristics such as age, gender, interests, and spending habits. The use of machine learning algorithms offers an automated and improved means of customer segmentation by training on vast datasets of customer data and using the resulting models to predict the appropriate segment for a new customer. This technology enables businesses to customize their marketing and sales strategies to target specific segments, resulting in more focused and effective communication with customers and potentially higher conversion rates. In this paper, a model based on unsupervised clustering algorithm, specifically the k-means method, in conjunction with Recency, Frequency, and Monetary (RFM) was employed to segment customers into distinct clusters based on their specific traits. Banking transactions data was used in this study, resulting in the identification of four clusters with unique characteristics that can be utilized for effective marketing purposes.
Further, this study successfully demonstrated a real-world application of machine learning in the realm of customer segmentation, with tremendous potential for implementation in the banking industry. The deployment of machine learning algorithms to automate the process of customer segmentation holds the promise of improving the accuracy and efficiency of this critical function, leading to better targeting of specific customer segments and increased effectiveness of marketing and sales efforts. The significance of this study lies in its ability to showcase the transformative power of machine learning in driving innovation and optimization in the banking sector, thereby enhancing the overall customer experience and driving sustainable business growth.},
        keywords = {Customer Segmentation, Machine Learning, Unsupervised Clustering, k-means Algorithm, RFM Analysis, Banking Industry, Targeted Marketing},
        month = {June},
        }