Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods

Abstract

Purpose – Churn management is a fundamental process in firms to keep their customers. Therefore, predicting the customer’s churn is essential to facilitate such processes. The literature has introduced data mining approaches for this purpose. On the other hand, results indicate that performance of classification models increases by combining two or more techniques. The purpose of this paper is to propose a combined model based on clustering and ensemble classifiers. Design/methodology/approach – Based on churn data set in Cell2Cell, single baseline classifiers, ensemble classifiers are used for comparisons. Specifically, self-organizing map (SOM) clustering technique, and four other classifier techniques including decision tree, artificial neural networks, support vector machine, and K-nearest neighbors were used. Moreover, for reduced dimensions of the features, principal component analysis (PCA) method was employed. Findings – As results 14 models are compared with each other regarding accuracy, sensitivity, specification, F-measure, and AUC. The results showed that combination of SOM, PCA, and heterogeneous boosting achieved the best performance comparing with other classification models. Originality/value – This study examined the performance of classifier ensembles in predicting customers churn. In particular, heterogeneous classifier ensembles such as bagging and boosting are compared.

Customers' loyalty to tablet commerce in Nigeria

“…In this study, the authors focus on tablet commerce in Nigeria context and not Africa nor globally, but the proposed framework can be adopted in other countries due to rapid diffusion of tablet device. The study employed quantitative method with structural equation modelling but the future researchers should look into the connectedness of a secured online shopping platform with security, trust, and ease of use using a mixed methodology (Ozturk et al 2016, Pappas 2016, Fathian, Hoseinpoor and Minaei-Bidgoli 2016. This mixed-methodology will enrich the study and pave the way for theoretical parsimony.…”

Optimizing Coverage of Churn Prediction in Telecommunication Industry

“…It is complex for implementation but it is a benchmark for random forecast [1, 18, 19, 20, and 21]. Both types of classifiers: single and ensemble have been used for churn dataset classification [22] and it was found that selforganizing map, Principal Component Analysis, and Heterogeneous Boosting outperform other classification methods. A study based on the text of customers for the consideration of their positive and negative influences is presented for churn analysis on a macro level but not on an individual level [23].…”

“…There is no single hybrid approach instead multiple hybrid approaches are used to find more accurate results [28]. Available work in literature is based on a single data mining techniques; classification or clustering for the prediction of customer churn and mining of retention data of customer [9,22], however, some studies have been conducted which apply more than one technology [2,30].…”

A Hybrid Prediction Model for E-Commerce Customer Churn Based on Logistic Regression and Extreme Gradient Boosting Algorithm

“…Carver et al [13] empirically demonstrated that XGBoostbased prediction model outperforms logistic regression, the SVM and random forest model, and the prediction performance can be further improved after incorporating social network variables. Focusing on customer behavior, Fathian et al [14] set up a decision tree model with three feature attributes, i.e. recency, frequency and monetary value in the past half a year, and successfully predicted 90% of the customer churn with the model.…”