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A proposed churn prediction model.
| Content Provider | CiteSeerX |
|---|---|
| Author | Shaaban, Essam Helmy, Yehia Khedr, Ayman Nasr, Mona |
| Abstract | Churn prediction aims to detect customers intended to leave a service provider. Retaining one customer costs an organization from 5 to 10 times than gaining a new one. Predictive models can provide correct identification of possible churners in the near future in order to provide a retention solution. This paper presents a new prediction model based on Data Mining (DM) techniques. The proposed model is composed of six steps which are; identify problem domain, data selection, investigate data set, classification, clustering and knowledge usage. A data set with 23 attributes and 5000 instances is used. 4000 instances used for training the model and 1000 instances used as a testing set. The predicted churners are clustered into 3 categories in case of using in a retention strategy. The data mining techniques used in this paper are Decision Tree, Support Vector Machine and Neural Network throughout an open source software name WEKA. Keywords:-Churn prediction, classification, clustering, data mining, prediction model 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Churn Prediction Model Churn Prediction Data Mining Near Future Decision Tree New Prediction Model Possible Churners Neural Network Service Provider Open Source Software Name Weka Data Selection Predictive Model Problem Domain Data Mining Technique Correct Identification Retention Strategy Knowledge Usage Support Vector Machine Prediction Model Predicted Churners Data Set Testing Set Retention Solution |
| Content Type | Text |
| Resource Type | Article |
