• Sukesh DR
• G Sasikala

Customer segmentation is essential in the modern competitive environment to allow creating custom marketing campaign strategies and increase the level of customer engagement. RFM, by standing for Recency, Frequency, Monetary – analysis is one of the strong foundations in this field that allows classifying customers based on their transactional patterns and behaviors. The proposed method aims to present RFM analysis in detail, including available methodologies, challenging aspects, and possible applications. The first aspect of RFM analysis is described in the evaluation of customers based on the recency of their last purchase, frequency of purchases, and monetary value of their interactions. By use of these three parameters, organizations can identify high-value customers, create specific marketing messages, and distribute resources effectively. However, RFM analysis is not free of challenges and limitations. Some of the most common include problems with the quality and availability of data, challenges with understanding the results of segmentation, and the dynamic nature of customer behavior. Today’s businesses are overcoming these challenges by applying more advanced analytical methods such as machine learning on top of traditional RFM approaches. Another rising methodology in the market is real-time dynamic RFM analysis, allowing organizations to address customer patterns constantly changing. By addressing limitations and extending its application field, businesses can gain deeper cognizance of customer behavior, use it to target strategies, and gain higher levels of customer satisfaction and loyalty.

RFM, Machine learning, Customer patterns, Model, Processing

"Enhancing Customer Segmentation: A Comprehensive Review of RFM Analysis and Advanced Methodologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.c415-c422, July-2024, Available :http://www.jetir.org/papers/JETIR2407253.pdf

"Enhancing Customer Segmentation: A Comprehensive Review of RFM Analysis and Advanced Methodologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppc415-c422, July-2024, Available at : http://www.jetir.org/papers/JETIR2407253.pdf