A Multi-Layer Perceptron Approach for Customer Churn Prediction

Ismail, Mohammad Ridwan; Awang, Mohd Khalid; Rahman, Mohd Nordin Abdul; Makhtar, Mokhairi

doi:10.14257/ijmue.2015.10.7.22

Cited by 37 publications

(11 citation statements)

References 6 publications

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“…Customer acquisition and retention can be improved by applying customer relationship management (CRM) tools for increasing profit and for supporting analytical tasks [15]. The association of CRM [16]- [18] further helps in capturing data and satisfying needs of soon to be noncustomers in future. Understanding churn using data mining also helps these companies to employ effective marketing strategies [19]- [24].…”

Section: Introductionmentioning

confidence: 99%

Customer churn analysis using XGBoosted decision trees

Muthupriya

Narayanan

Fatima

et al. 2022

IJEECS

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Customer relationship management (CRM) is an important element in all forms of industry. This process involves ensuring that the customers of a business are satisfied with the product or services that they are paying for. Since most businesses collect and store large volumes of data about their customers; it is easy for the data analysts to use that data and perform predictive analysis. One aspect of this includes customer retention and customer churn. Customer churn is defined as the concept of understanding whether or not a customer of the company will stop using the product or service in future. In this paper a supervised machine learning algorithm has been implemented using Python to perform customer churn analysis on a given data-set of Telco, a mobile telecommunication company. This is achieved by building a decision tree model based on historical data provided by the company on the platform of Kaggle. This report also investigates the utility of extreme gradient boosting (XGBoost) library in the gradient boosting framework (XGB) of Python for its portable and flexible functionality which can be used to solve many data science related problems highly efficiently. The implementation result shows the accuracy is comparatively improved in XGBoost than other learning models.

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Section: Introductionmentioning

confidence: 99%

Customer churn analysis using XGBoosted decision trees

Muthupriya

Narayanan

Fatima

et al. 2022

IJEECS

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“…Prinzie and Van den Poel (2005) used constrained optimization to evaluate an individual-level response model for direct marketing, as it focuses on a classification problem applied to direct marketing instead of customer satisfaction, a direct comparison is not possible. Other works such as Xia and Jin (2008), Zhang et al (2012), Vafeiadis et al (2015 and Ismail et al (2015) used techniques such as SVM, ANN, logistic regression, decision trees and rule-based decision process to estimate customer churns. Also, Park and Gates (2009) and Blockhorst et al (2017) applied machine learning techniques to the field of customer relationship to estimate customer satisfaction by analyzing call transcripts using techniques such as decision tree, logistic regression, SVM, ridge regression, lasso regression and random forest.…”

Section: Resultsmentioning

confidence: 99%

Predicting customer satisfaction for distribution companies using machine learning

Siebert

Filho

Júnior

et al. 2019

IJESM

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Purpose This study aims to support electricity distribution companies on measuring and predicting customer satisfaction. Design/methodology/approach The developed methodology selects and applies machine learning techniques such as decision trees, support vector machines and ensemble learning to predict customer satisfaction from service data, power outage data and reliability indices. Findings The results on the predicted main indicator diverged only by 1.36 per cent of the results obtained by the survey with company customers. Research limitations/implications Social, economic and political conjunctures of the regional and national scenario can influence the indicators beyond the input variables considered in this paper. Practical implications Currently, the actions taken to increase customer satisfaction are based on the track record of a yearly survey; therefore, the methodology may assist in identifying disturbances on customer satisfaction, enabling decision-making to deal with it in a timely manner. Originality/value Development of an intelligent algorithm that can improve its performance with time. Understanding customer satisfaction may improve companies’ performance.

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“…Based on the conducted review, it can be noticed that Artificial Neural Networks (ANNs) are among the most applied models in the literature for churn prediction. For example, in [35], a Multilayer Perceptron (MLP) neural network approach is proposed to predict customer churn in one of the major Malaysian telecommunication companies. Its results are compared to the results obtained by Multiple Regression Analysis and Logistic Regression Analysis.…”

Section: Previous Workmentioning

confidence: 99%

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

Faris

2018

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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.

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A Multi-Layer Perceptron Approach for Customer Churn Prediction

Cited by 37 publications

References 6 publications

Customer churn analysis using XGBoosted decision trees

Customer churn analysis using XGBoosted decision trees

Predicting customer satisfaction for distribution companies using machine learning

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

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