Customer churn prediction in telecommunication industry using data certainty

Abstract

With the terrific growth of digital data and associated technologies, there is an emerging trend, where industries become rapidly digitized. These technologies are providing great opportunities to identify and resolve different problems. In particular, the telecommunication industry is facing a serious problem of customer churn relating to, the customers who are going to abandon their established relation with the business/network in the near future. This problem cannot only affect the rapid growth of the business but can also affect the revenues. Therefore, many customer churn prediction (CCP) models have been introduced but not yielding the desired performance in CCP. This is because there can be many factors, that contribute to customer churn which are still unexplored. In this paper, we focus on determining the effectiveness of the factors, i.e. lower and upper distance between the samples, are considered by the proposed model for the CCP. Further, we demonstrate a novel solution pertaining to the telecommunication sector showing the hidden factors considered for predicting the customer churn. Finally, we investigate the effects of both types of samples: those samples that are low distance and the upper distance (in terms of relevance) to the majority samples in given publicly available dataset. As a result of the study, we found that lower distance test set (LDT) samples have obtained best performance as compare to upper distance test set (UDT) samples in term of increased in the accuracy, f-measures, precision and recall when the uncertain sample size increases. Because the classification performance on upper distance samples remain almost the same when the size of samples increased in the test set.

Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms

“…Most of the literature focused more on data mining algorithms, but only a few of them focused on distinguishing the important input variables for churn prediction and on enhancing the data samples through efficient pre-processing to be used for data mining algorithms implementation [8] [9]. Amin, A., et al [10] presented a novel churn prediction approach based on the classifier's certainty estimation using distance factor where they grouped the dataset into different zones based on the distance which are then divided into two categories with high and low certainty, they used 4 datasets with different samples and they have been discretized by size, the values that exists in each attribute of the dataset, and then assigned certain labels and at the end produced specific list of values in different number of groups of an attribute. They used Naïve Bayes as classifier and it obtained high accuracy in the zone with greater distance factor's value (i.e., customer churn and non-churn with high certainty) than those placed in the zone with smaller distance factor's value (i.e., customer churn and non-churn with low certainty).…”

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

“…In 2017, Long Zha et al proposed a new KLMM algorithm for feature selection for high dimensional issue and used leave one out method as cross validation to evaluate the hyper parameter .In 2017, E. Sivasankar et al used many clustering algorithms like K-Means, FCM, PFCM and reported that decision tree combined with K-Means gives higher accuracy when compared to all the combination [37]. In 2018, Adnan Amin et al [40] developed a method based on the distance factor of classifiers. They applied this method on four different datasets and Naive Bayes was used as a baseline classifier.…”

Improving the Reliability of Churn Predictions in Telecommunication Sector by Considering Customer Region

“…The most commonly used ones are SVM, decision tree, artificial neural networks (ANN), logistic regression, and so on. For instance, Archaux et al used SVM and ANN to detect churners among prepaid mobile users and compared the results [11]. Hung et al [12] employed a hybrid approach by combining decision tree and neural network as classification methods and kmeans as cluster to predict churn in a wireless telecommunication company.…”