Feature intersection for agent-based customer churn prediction

2019 ◽  
Vol 53 (3) ◽  
pp. 318-332
Author(s):  
Sandhya N. ◽  
Philip Samuel ◽  
Mariamma Chacko
Keyword(s):  
Service Providers ◽  
Mobile User ◽  
Telecommunication Service ◽  
Agent Based Model ◽  
Churn Prediction ◽  
Content Type ◽  
Agent Based ◽  
User Behaviour ◽  
Customer Churn ◽  
Customer Churn Prediction

Purpose Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue. Design/methodology/approach The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider. Findings The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. Research limitations/implications The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour. Practical implications This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit. Originality/value This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.

scholarly journals Customer Churn Prediction using Predictive Analytics in Telecommunication Market: A Review

2019 ◽  
pp. 97-105
Keyword(s):  
Performance Metrics ◽  
Prediction Models ◽  
Service Providers ◽  
Predictive Analytics ◽  
Churn Prediction ◽  
Customer Churn ◽  
Telecommunication Market ◽  
Company Limited ◽  
The Face ◽  
Customer Churn Prediction

In the face of extreme competitive telecommunication market, the cost of acquiring new customer is much more expensive than to retain the existing customer. Therefore, it has become imperative to pay much attention towards retaining the existing customers in order to get stabilize in market comprised of vibrant service providers. In current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. This article reviews the customer churn prediction problem, factors escalating the phenomena, prediction through predictive analytics, steps for processing of predictive analytics and evaluation of performance metrics for various churn prediction models are surveyed. Moreover, the CRM data from Pakistan Telecommunication Company limited as case study to discuss the process of data mining and predictive analytics for customer churn prediction.

Hybrid ensemble learning approaches to customer churn prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sara Tavassoli ◽  
Hamidreza Koosha
Keyword(s):  
Binary Classification ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Bootstrap Sample ◽  
Learning Approaches ◽  
Ensemble Classifiers ◽  
Churn Prediction ◽  
Content Type ◽  
Customer Churn ◽  
Customer Churn Prediction

PurposeCustomer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction.Design/methodology/approachIn this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm.FindingsTo examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results.Originality/valueIn this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms.

scholarly journals Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Rodan ◽  
Ayham Fayyoumi ◽  
Hossam Faris ◽  
Jamal Alsakran ◽  
Omar Al-Kadi
Keyword(s):  
Negative Correlation ◽  
Service Providers ◽  
Multilayer Perceptrons ◽  
Comparison Study ◽  
Churn Prediction ◽  
Customer Churn ◽  
Negative Correlation Learning ◽  
Telecommunication Companies ◽  
Correlation Learning ◽  
Customer Churn Prediction

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

scholarly journals Performance Comparison of Neural Network Training Algorithms for Modeling Customer Churn Prediction

2018 ◽  
Vol 7 (2.15) ◽  
pp. 35 ◽  
Author(s):  
Mohd Khalid Awang ◽  
Mohammad Ridwan Ismail ◽  
Mokhairi Makhtar ◽  
M Nordin A Rahman ◽  
Abd Rasid Mamat
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Telecommunication Service ◽  
Churn Prediction ◽  
Data Set ◽  
Customer Churn ◽  
The Neural Network ◽  
Customer Churn Prediction

Predicting customer churn has become the priority of every telecommunication service provider as the market  is becoming more saturated and competitive. This paper presents a comparison of neural network learning algorithms for customer churn prediction.  The data set used to train and test the neural network algorithms was provided by one of the leading telecommunication company in Malaysia. The Multilayer Perceptron (MLP) networks are trained using nine (9) types of learning algorithms, which are Levenberg Marquardt backpropagation (trainlm), BFGS Quasi-Newton backpropagation (trainbfg), Conjugate Gradient backpropagation with Fletcher-Reeves Updates (traincgf), Conjugate Gradient backpropagation with Polak-Ribiere Updates (traincgp), Conjugate Gradient backpropagation with Powell-Beale Restarts (traincgb), Scaled Conjugate Gradient backpropagation (trainscg), One Step Secant backpropagation (trainoss), Bayesian Regularization backpropagation (trainbr), and Resilient backpropagation (trainrp). The performance of the Neural Network is measured based on the prediction accuracy of the learning and testing phases. LM learning algorithm is found to be the optimum model of a neural network model consisting of fourteen input units, one hidden node and one output node. The best result of the experiment indicated that this model is able to produce the performance accuracy of 94.82%. 

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.

scholarly journals Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector

2021 ◽  
Author(s):  
Irina V. Pustokhina ◽  
Denis A. Pustokhin ◽  
Phong Thanh Nguyen ◽  
Mohamed Elhoseny ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Optimization Algorithm ◽  
Service Providers ◽  
Sampling Rate ◽  
Optimal Sampling ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Multi Objective ◽  
Customer Churn ◽  
Customer Churn Prediction

AbstractCustomer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.

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