Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network

Sivasankar, E.; Vijaya, J.

doi:10.1007/s00521-018-3548-4

Cited by 29 publications

(19 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cluster category reflects the employment of approaches based on the adoption of unsupervised algorithms to develop the segmentation of customers, to support the use of machine learning algorithms to predict the churn in each segment [5,25,112]. Jafari-Marandi et al [121] explored a similar approach that combine clustering methods parallel to classification methods with the aim of creating more control in the decision-making process of churn management, but at the least, expect to increase the accuracy by exploring the individuality of each customer to optimize the classification decision process.…”

Section: B Rq2 -What Algorithms Have Been Employed To Predict Dropout?mentioning

confidence: 99%

“…Vijaya and Sivasankar [112] suggested that works that adopt hybrid models combining more than one classifier can achieve increased performance compared with those using single classifiers. This idea is not new, and some studies have explored the combination of clusters with churn prediction [5,25,111], where if the customers are grouped into clusters, the prediction accuracy can be improved within each cluster. The hybrid approach using clustering and classification, which segments the customers before developing a classification, could be effective [121].…”

Section: B Rq2 -What Algorithms Have Been Employed To Predict Dropout?mentioning

confidence: 99%

“…By anticipating possible churners, companies can develop countermeasures, such as concessions, to retain as many customers as possible [25] and develop predictions regarding future intentions to facilitate their segmentation to support actions according to their likelihood of dropping out. The prevention of customer dropout is a problem that needs to be addressed that, according to [12], requires a retention action if the company wants to avoid dropout.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A SLR on Customer Dropout Prediction

et al. 2022

View full text Add to dashboard Cite

Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.

show abstract

Section: B Rq2 -What Algorithms Have Been Employed To Predict Dropout?mentioning

confidence: 99%

Section: B Rq2 -What Algorithms Have Been Employed To Predict Dropout?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A SLR on Customer Dropout Prediction

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Bayesian Binomial method test was used to evaluate the entire system. J. Vijaya et al [41] proposed a hybrid method of multi class clustering called PPFCM with ANN and reported an accuracy of 94%. They applied this novel hybrid method on tera duke dataset in 2017.…”

Section: Systematic Analysis Procedures For Electing Articlesmentioning

confidence: 99%

Exploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector

Pamina¹,

Rajkumar²,

Kiruthika³

et al. 2019

IJRTE

View full text Add to dashboard Cite

Most prominent challenges in all business is to retain and satisfy their valuable customers for sustain successfully in the market. Numerous Machine learning approaches are emerging to develop various customer retention models to solve this issue in many applications. This swing is more realized in telecom industry due its enormous significance. This article presents an elaborated survey on machine learning based churn prediction in telecom sector from the year 2000 to 2018. We also extracted the problems and challenges in Telecom Churn Prediction and reported suggestion and solutions. We believe this article helps the researches or data analysts in the telecom field to select optimal and appropriate methods and for designing improved novel model for churn prediction in future

show abstract

“…of the constructed model [3][4][5][6][7][8]. Recently, Sivasankar and Vijaya have presented [18] a hybrid method for building a churn prediction model which is based on the combination of classification and clustering. They claimed that the predictive accuracy of their method is high.…”

Section: Literature Survey and Related Workmentioning

confidence: 99%

An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers

Muneiah

Subbarao

2019

View full text Add to dashboard Cite

Enterprises often classify their customers based on the degree of profitability in decreasing order like C1, C2, ..., Cn. Generally, customers representing class Cn are zero profitable since they migrate to the competitor. They are called as attritors (or churners) and are the prime reason for the huge losses of the enterprises. Nevertheless, customers of other intermediary classes are reluctant and offer an insignificant amount of profits in different degrees and lead to uncertainty. Various data mining models like decision trees, etc., which are built using the customers’ profiles, are limited to classifying the customers as attritors or non-attritors only and not providing profitable actionable knowledge. In this paper, we present an efficient algorithm for the automatic extraction of profit-maximizing knowledge for business applications with multi-class customers by postprocessing the probability estimation decision tree (PET). When the PET predicts a customer as belonging to any of the lesser profitable classes, then, our algorithm suggests the cost-sensitive actions to change her/him to a maximum possible higher profitable status. In the proposed novel approach, the PET is represented in the compressed form as a Bit patterns matrix and the postprocessing task is performed on the bit patterns by applying the bitwise AND operations. The computational performance of the proposed method is strong due to the employment of effective data structures. Substantial experiments conducted on UCI datasets, real Mobile phone service data and other benchmark datasets demonstrate that the proposed method remarkably outperforms the state-of-the-art methods.

show abstract

Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network

Cited by 29 publications

References 35 publications

A SLR on Customer Dropout Prediction

A SLR on Customer Dropout Prediction

Exploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector

An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers

Contact Info

Product

Resources

About