Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model

2021 ◽  
Vol 14 (2) ◽  
pp. 174-186
Author(s):  
Sivasankar Karuppaiah ◽  
N. P. Gopalan
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Churn ◽  
Proposed Model ◽  
Customer Churn Prediction ◽  
Effective Models

In a rapidly growing industry like telecommunications, customer churn prediction is a crucial challenge affecting the sustainability of the business as a whole. The fact that retaining a customer is more profitable than acquiring new customers is important to predict potential churners and present them with offers to prevent them from churning. This work presents a stacked CLV-based heuristic incorporated ensemble (SCHIE) to enable identification of potential churners so as to provide them with offers that can eventually aid in retaining them. The proposed model is composed of two levels of prediction followed by a recommendation to reduce customer churn. The first level involves identifying effective models to predict potential churners. This is followed by result segregation, CLV-based prediction, and user shortlisting for offers. Experimental results indicate high efficiencies in predicting potential churners and non-churners. The proposed model is found to reduce the overall loss by up to 50% in comparison to state-of-the-art models.

Feature-selection-based dynamic transfer ensemble model for customer churn prediction

2014 ◽  
Vol 43 (1) ◽  
pp. 29-51 ◽  
Author(s):  
Jin Xiao ◽  
Yi Xiao ◽  
Anqiang Huang ◽  
Dunhu Liu ◽  
Shouyang Wang
Keyword(s):  
Feature Selection ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

scholarly journals A Hybrid Swarm Intelligent Neural Network Model for Customer Churn Prediction and Identifying the Influencing Factors

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 288 ◽  
Author(s):  
Hossam Faris
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Churn Prediction ◽  
Hybrid Swarm ◽  
Customer Churn ◽  
The Neural Network ◽  
Proposed Model ◽  
Telecommunication Companies ◽  
Technique Evaluation ◽  
Imbalanced Class Distribution

Customer churn is one of the most challenging problems for telecommunication companies. In fact, this is because customers are considered as the real asset for the companies. Therefore, more companies are increasing their investments in developing practical solutions that aim at predicting customer churn before it happens. Identifying which customer is about to churn will significantly help the companies in providing solutions to keep their customers and optimize their marketing campaigns. In this work, an intelligent hybrid model based on Particle Swarm Optimization and Feedforward neural network is proposed for churn prediction. PSO is used to tune the weights of the input features and optimize the structure of the neural network simultaneously to increase the prediction power. In addition, the proposed model handles the imbalanced class distribution of the data using an advanced oversampling technique. Evaluation results show that the proposed model can significantly improve the coverage rate of churn customers in comparison with other state-of-the-art classifiers. Moreover, the model has high interpretability, where the assigned feature weights can give an indicator about the importance of their corresponding features in the classification process.

Large-scale Ensemble Model for Customer Churn Prediction in Search Ads

2018 ◽  
Vol 11 (2) ◽  
pp. 262-270 ◽  
Author(s):  
Qiu-Feng Wang ◽  
Mirror Xu ◽  
Amir Hussain
Keyword(s):  
Large Scale ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

Distributed model for customer churn prediction using convolutional neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Negative ◽  
Accuracy Score ◽  
Churn Prediction ◽  
Data Set ◽  
Content Type ◽  
Customer Churn ◽  
Proposed Model ◽  
Customer Churn Prediction

Purpose The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly. Design/methodology/approach The proposed model uses the 2-D convolutional neural network (CNN; a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn. Findings The proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective. Originality/value This paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.

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