Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
Crm at the Speed of Light: Capturing and Keeping Customers in Internet Real Time
Crm at the Speed of Light: Capturing and Keeping Customers in Internet Real Time
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
An introduction to variable and feature selection
The Journal of Machine Learning Research
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Expert Systems with Applications: An International Journal
Accelerating customer relationships: using crm and relationship technologies™
Accelerating customer relationships: using crm and relationship technologies™
Expert Systems with Applications: An International Journal
Mining the customer credit using hybrid support vector machine technique
Expert Systems with Applications: An International Journal
Customer churn prediction by hybrid neural networks
Expert Systems with Applications: An International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Computational Statistics & Data Analysis
Comparing classification techniques for predicting essential hypertension
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Combined rough set theory and flow network graph to predict customer churn in credit card accounts
Expert Systems with Applications: An International Journal
An extended support vector machine forecasting framework for customer churn in e-commerce
Expert Systems with Applications: An International Journal
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Customer churn prediction in telecommunications
Expert Systems with Applications: An International Journal
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
Credit card churn forecasting by logistic regression and decision tree
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Customer event history for churn prediction: How long is long enough?
Expert Systems with Applications: An International Journal
Computers and Electrical Engineering
| Hi-index | 12.05 |
The profit resulting from customer relationship is essential to ensure companies viability, so an improvement in customer retention is crucial for competitiveness. As such, companies have recognized the importance of customer centered strategies and consequently customer relationship management (CRM) is often at the core of their strategic plans. In this context, a priori knowledge about the risk of a given customer to mitigate or even end the relationship with the provider is valuable information that allows companies to take preventive measures to avoid defection. This paper proposes a model to predict partial defection, using two classification techniques: Logistic regression and Multivariate Adaptive Regression Splines (MARS). The main objective is to compare the performance of MARS with Logistic regression in modeling customer attrition. This paper considers the general form of Logistic regression and Logistic regression combined with a wrapper feature selection approach, such as stepwise approach. The empirical results showed that MARS performs better than Logistic regression when variable selection procedures are not used. However, MARS loses its superiority when Logistic regression is conducted with stepwise feature selection.