Journal article
Customer Churn Prediction with Feature Embedded Convolutional Neural Network: An Empirical Study in the Internet Funds Industry
International journal of computational intelligence and applications, Vol.18(1), p.1950003
03/2019
DOI: 10.1142/S1469026819500032
Abstract
In this paper, we investigated the customer churn prediction problem in the Internet funds industry. We designed a novel feature embedded convolutional neural networks (FE-CNN) method that can automatically learn features from both the dynamic customer behavioral data and static customer demographic data and can utilize the advantage of convolutional neural networks to automatically learn features that capture the structured information. Our results show that our FE-CNN model outperforms the other traditional machine learning models with hand-crafted features, such as logistic regression (LR), support vector machines (SVM), random forests (RF) and neural networks (NN) in terms of accuracy, area under the receiver operating characteristics curve (AUC) and top-decile lift. Furthermore, we found that after adding the demographic data feature to the basic CNN model, the performance of the FE-CNN model improved. Overall, we found that the FE-CNN is the most powerful way to solve the problem of customer churn prediction in the Internet funds industry. Our FE-CNN method can also be applied to other fields that have both dynamic data and static data.
Details
- Title: Subtitle
- Customer Churn Prediction with Feature Embedded Convolutional Neural Network: An Empirical Study in the Internet Funds Industry
- Creators
- Chongren Wang - School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, ChinaDongmei Han - School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, ChinaWeiguo Fan - Department of Management Sciences, Tippie College of Business, University of Iowa, 108 John Pappajohn Business Building, 5262, Iowa City, IA52242-1994, USAQigang Liu - SHU-UTS SILC Business School, Shanghai University, Shanghai 201899, China
- Resource Type
- Journal article
- Publication Details
- International journal of computational intelligence and applications, Vol.18(1), p.1950003
- DOI
- 10.1142/S1469026819500032
- ISSN
- 1469-0268
- eISSN
- 1757-5885
- Language
- English
- Date published
- 03/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083846402771
Metrics
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