What question did this study set out to answer?

This research aims to enhance customer segment classification by integrating deep learning with RFM analysis.

February 28, 2026Applied Sciences0 citationsOpen Access

RFM-Net: A Convolutional Neural Network for Customer Segment Classification

KBKadriye Filiz BalbalDokuz Eylül University DBDerya Birant

Puntos clave

This research aims to enhance customer segment classification by integrating deep learning with RFM analysis.
Designed RFM-Net, a convolutional neural network for classification tasks.
Utilized RFM features from transactional data as input.
Applied rule-based logic to generate labeled customer segments for supervised learning.
Conducted experiments on a real-world dataset to validate the model's effectiveness.
Achieved an accuracy of 94.33% in customer segment classification.
Showed a 13.17% average increase in performance over previous methods on the same dataset.

Resumen

Customer Segment Classification is a machine learning task in marketing analytics that involves assigning customers to predefined categories using features derived from historical transactional data. However, conventional approaches, such as statistical and clustering-based algorithms, may face challenges in fully capturing the nonlinear relationships in customer data, which can lead to limited insights and suboptimal segmentation outcomes. This paper introduces RFM-Net, an approach that integrates Deep Learning with Recency, Frequency, and Monetary (RFM) analysis for customer segment classification. By leveraging RFM features as input and labeled customer segments as output, we designed a specialized Convolutional Neural Network (CNN) model tailored for classification tasks. In the proposed method, labels are generated by a rule-based logic from RFM scores and then used as supervised ground truth. Accordingly, learning an expert-defined mapping is employed to model customer segmentation, rather than discovering a new segmentation structure. The proposed method enables businesses to classify customers into strategically meaningful segments such as Champions, Loyal Customers, At Risk, and Hibernating, thereby facilitating effective and targeted marketing strategies. Unlike traditional CNN architectures, RFM-Net offers a more compact, lightweight, and computationally efficient model with fewer layers and parameters, supporting improved interpretability and reduced risk of overfitting. Experimental results conducted on a real-world dataset demonstrated the effectiveness of RFM-Net with an accuracy of 94.33%. The results of this study showed a relative average increase of 13.17% compared to the results reported in previous studies on the same dataset. The core contribution of this research lies in combining the powerful generalization capabilities of deep learning with the effectiveness of RFM analysis, offering a robust solution for data-driven customer relationship management.

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo