Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

TL;DR

This paper explores semi-supervised learning using neural networks to improve customer classification in CRM, leveraging both labeled and unlabeled data for better customer management.

Contribution

It introduces a semi-supervised neural network approach for customer data analysis in CRM, utilizing unlabeled data to enhance predictive accuracy.

Findings

Effective classification of potential customers using semi-supervised learning.

Integration with Rapid Miner facilitates practical application.

Improved prediction performance over traditional supervised methods.

Abstract

Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data.