An extended support vector machine forecasting framework for customer churn in e-commerce

Authors:
Xiaobing Yu;Shunsheng Guo;Jun Guo;Xiaorong Huang
Affiliations:
School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, PR China;School of Mechanic and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China;School of Mechanic and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China;School of Mechanic and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 8
Cited 1

Support vector machine active learning for image retrieval

MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets

The Journal of Machine Learning Research
Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department

Expert Systems with Applications: An International Journal
Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services

Expert Systems with Applications: An International Journal
A novel decision rules approach for customer relationship management of the airline market

Expert Systems with Applications: An International Journal
Customer churn prediction using improved one-class support vector machine

ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
A novel evolutionary data mining algorithm with applications to churn prediction

IEEE Transactions on Evolutionary Computation

Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

In order to accurately forecast and prevent customer churn in e-commerce, a customer churn forecasting framework is established through four steps. First, customer behavior data is collected and converted into data warehouse by extract transform load (ETL). Second, the subject of data warehouse is established and some samples are extracted as train objects. Third, alternative predication algorithms are chosen to train selected samples. Finally, selected predication algorithm with extension is used to forecast other customers. For the imbalance and nonlinear of customer churn, an extended support vector machine (ESVM) is proposed by introducing parameters to tell the impact of churner, non-churner and nonlinear. Artificial neural network (ANN), decision tree, SVM and ESVM are considered as alternative predication algorithms to forecast customer churn with the innovative framework. Result shows that ESVM performs best among them in the aspect of accuracy, hit rate, coverage rate, lift coefficient and treatment time. This novel ESVM can process large scale and imbalanced data effectively based on the framework.