Churn prediction in Turkey's telecommunications sector: A proposed multiobjective–cost‐sensitive ant colony optimization

Abstract

Players in the telecommunications sector struggle against the competition to keep customers, and therefore they need effective churn management. Most classification algorithms either ignore misclassification cost or assume that the costs of all incorrect classification errors are equal. But as in real life, many classification problems have different misclassification costs and this difference cannot be ignored. For this reason, studies on cost‐sensitive classification approaches have gained importance in recent years. The characteristics of telecommunications datasets such as high dimensionality and imbalance are making it difficult to achieve the desired performance for churn prediction. By taking this into consideration, we propose a multiobjective–cost‐sensitive ant colony optimization (MOC‐ACO‐Miner) approach which integrates the cost‐based nondominated sorted genetic algorithm feature selection and multiobjective ACO based cost‐sensitive learning. MOC‐ACO‐Miner is applied to one of Turkey's top 100 information technology companies for customer churn‐prediction. Finally, experiments find out that the model performs quite well with the area under receiver operating characteristic curve values of 0.9998 for predicting churners and therefore it can be beneficial for the highly competitive telecommunications sector. This article is categorized under: Algorithmic Development > Association Rules Application Areas > Industry Specific Applications Technologies > Prediction Technologies > Data Preprocessing

Data Mining Applications in Banking Sector While Preserving Customer Privacy

“…Heuristic data mining applications such as clustering, association rule mining, decision trees are commonly used in banking and finance for risk scoring and churn analysis [19,20] Data mining applications provide more accurate results when more data is available for the same constituents [21,22]. Organizations often collaboratively use data mining applications while sharing data with each other.…”

Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions

“…Li et al [24] introduced an innovative collaborative prediction unit (CoPU) that combines the predictions from many collaborative predictors based on a collaborative graph. Özmen et al [25] developed ant colony optimization methods for optimization and genetic algorithm for feature extraction of data. ey applied the model to 100 companies of Turkey to detect customer churn.…”

“…e study of principal components is one of the outcomes of linear algebra mathematics because the nonparametric and straightforward method extracts relevant information from confusing sets. e transformation of the T can be obtained by minimizing the least-squares error, assuming that the CCPBI-TAMO, CPIO-FS Telecom Precision, recall, accuracy, F-Score, ROC [41] Xgboost, AdaBoost, catboost, decision trees, SVM, KNN Telecom Accuracy, AUC, precision, recall, F-Measures [37] Deep feed-forward networks Subscription companies Accuracy [38] Deep ANN, machine learning algorithms Telecom Accuracy, precision, recall, F1-score, and AUC [12] Neural network with bagging Telecom Accuracy, precision, recall, F-score, kappa, absolute error, relative error, and classi cation error [10] Transfer learning of ensemble Telecom Area under curve of ROC (AUC) and complexity [11] Ensemble algorithm Telecom Area under curve of ROC (AUC) [12] Begging and neural network Telecom Accuracy and precision of classi cation [42] Arti cial neural network (ANN) and self-organized map (SOM) Telecom Accuracy, recall, F-score, and precision [15] Pro t tree Telecom Accuracy, cost, and pro t [16] Minimax probability machines Telecom AUC and EMPC [17] similarity forests Telecom AUC, and tenlift AUPR [21] Temporal point processes (TPP) and recurrent neural networks (RNN) Telecom MAE and MRE [22] Cross-company just-in-time approach Telecom Accuracy, Kappa, and Recall [25] Multiobjective and colony optimization Telecom AUC [27] graph theory Telecom Top decile lift [31] Boosted…”

“…Each of them is related to a specific industry. Some papers have been conducted on the finance sector, including banking, insurance, and funds [2,48,49], retail industry [50,51], game [6], music [52], e-commerce [53], and telecommunication [15,22,25,[54][55][56]. It is worth noting that there are many datasets in the vertical of telecommunication due to the vast number of customers and available data.…”

Hybrid ensemble learning approaches to customer churn prediction

“…They also compared the results with those of a symbolic regression-based approach. Özmen et al . (2020) proposed a multi-objective–cost-sensitive ant colony optimization (MOC-ACO-Miner) approach which integrates the cost-based non-dominated sorted genetic algorithm feature selection and multi-objective ACO-based cost-sensitive learning.…”