An Intelligent Customer Analytics Framework Using RFM Segmentation and Hybrid Recommendation Systems

DOI

10.2991/978-94-6239-713-2_41How to use a DOI?

Keywords

K-Means Clustering; RFM Analysis; PCA Visualization; Hybrid Filtering

Abstract

Personalized recommendation systems and customer segmentation contribute significantly to enriching user experience and optimizing marketing strategies in the retail and e-commerce sectors. Traditional recommendation approaches usually suffer from issues of limited personalization and scalability. This paper presents an integrated system that combines customer segmentation based on Recency–Frequency–Monetary (RFM) analysis with customized product recommendations using content-based, collaborative, and hybrid filtering techniques. Customers are divided into four classes-Loyal Customers, Potential Loyalists, New Customers and At-Risk Customers by the implementation of K-Means clustering. Principal Component Analysis (PCA) is utilized to visualize multidimensional data and analyze the dispersion of customer behaviors across clusters. Personalized recommendations are produced by examining both product features and customer interactions, whereas the hybrid model combines these analyses to gain better accuracy and relevance. The system proposed has been implemented as an interactive Streamlit dashboard that allows real-time analysis, visualization and generation of recommendations. Empirical analysis shows that the hybrid model consistently generates more contextually appropriate recommendations than single filtering methods, demonstrating real-world usefulness for data-driven, customer-centric business approaches.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Nisha Pal
AU  - Anupma
AU  - Kajal Chaudhary
AU  - Sanjay Kumar
PY  - 2026
DA  - 2026/06/25
TI  - An Intelligent Customer Analytics Framework Using RFM Segmentation and Hybrid Recommendation Systems
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 549
EP  - 566
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_41
DO  - 10.2991/978-94-6239-713-2_41
ID  - Pal2026
ER  -