A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation

Tianyuan, Zhang; Moro, Sérgio; Ramos, Ricardo Filipe

doi:10.3390/fi14030094

Cited by 39 publications

(51 citation statements)

References 37 publications

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“…This study enabled banking administrators to analyze client behavior, which might lead to appropriate methods for improving consumer engagement and strengthening administrator conducts. Through customer segmentation, Zhang et al (2022) intended to create a churn prediction model for telecom clients. A telecom customer churn prediction model was created using data gathered from three significant Chinese telecom providers, fisher discriminant equations, and logistic regression analysis.…”

Section: Churn Prediction Using Machine Learning Combines Customer Se...mentioning

confidence: 99%

Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models

Tran

Nguyen

2023

IJIKM

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Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

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Section: Churn Prediction Using Machine Learning Combines Customer Se...mentioning

confidence: 99%

Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models

Tran

Nguyen

2023

IJIKM

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“…Customer loss is experienced in companies whose service starts to change and does not meet the demands and expectations of customers. The biggest cause of customer loss is that the company does not actually know the customer's exact demands (Zhang et al, 2022). Every customer has a historical data, which gives a signal of when and how they are buying that product.…”

Section: Customer Churnmentioning

confidence: 99%

Churn Customer Management in Retail Industry: A Case Study

Börühan

2022

İzmir İktisat Dergisi

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Perakende sektörü, küresel olarak gelişmekte olan endüstriler arasında yer almakta, uygulayıcılar ve akademisyenler tarafından artan bir ilgi görmektedir. Perakende çevresi hızla değişmekte ve hem yerli hem de yabancı şirketlerden gelen büyük rekabet ile karakterize edilmektedir. Firmaların çoğu özdeş mallar üretmekte ve bunları rekabetçi fiyatlarla satmaya çalışmaktadır. Bu bağlamda yeni müşteriler bulmak ve onları sadık kılmak perakende sektörünün en zor işlerinden biridir. Firmalar için yeni müşteri bulmak eski müşteriyi elde tutmaktan beş kat daha pahalıya mal olmaktadır. Bu nedenle müşteriyi elde tutma kavramı akademik literatürde yeni bir terim olan “Kayıp Müşteri Yönetimi” nin ortaya çıkmasına neden olmuştur. Bu çalışmanın amacı, Perakendeci X'in İzmir'in farklı bölgelerinde bulunan düşük ve yüksek verimli mağazalarını veri zarflama analizi yaparak analiz etmek ve ardından bu mağazalardaki müşteri kaybının nedenlerini hem müşteriler hem de mağaza yöneticileri açısından incelemektir. Düşük ve yüksek verimli mağazaları bulmak için veri zarflama analizi yapmak üzere Perakendeci X'ten veriler toplanmıştır. Bir sonraki aşamada, her iki tarafın algılarını karşılaştırabilmek için hem mağaza yöneticileri hem de müşterilerle yarı yapılandırılmış görüşmeler yapılmıştır. Bu görüşmeler sonucunda müşteri kaybı nedenleri ürün ve stok düzeyi, fiyat, promosyonlar, fiziksel mağaza atmosferi, satış personelinin etkileşimi, satış sonrası hizmetler ve rakipler olmak üzere 7 grupta sınıflandırılmıştır.

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“…It illustrates the empirical evaluation of the proposed method that reliably outperforms the discrete AdaBoost approach concerning telecom churn prediction. Zhang et al [13] attempted to develop a churn prediction method for predicting telecom client churn via customer segmentation. Information is gathered from three Chinese telecom corporations, and Fisher discriminatory equation and logistic regression analysis have been utilized for building a telecom CCP method [14].…”

Section: Introductionmentioning

confidence: 99%

Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

Haridasan¹,

Muthukumaran²,

Hariharanath³

2023

Intelligent Automation &Amp; Soft Computing

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Customer retention is one of the challenging issues in different business sectors, and various firms utilize customer churn prediction (CCP) process to retain existing customers. Because of the direct impact on the company revenues, particularly in the telecommunication sector, firms are needed to design effective CCP models. The recent advances in machine learning (ML) and deep learning (DL) models enable researchers to introduce accurate CCP models in the telecommunication sector. CCP can be considered as a classification problem, which aims to classify the customer into churners and non-churners. With this motivation, this article focuses on designing an arithmetic optimization algorithm (AOA) with stacked bidirectional long short-term memory (SBLSTM) model for CCP. The proposed AOA-SBLSTM model intends to proficiently forecast the occurrence of CC in the telecommunication industry. Initially, the AOA-SBLSTM model performs pre-processing to transform the original data into a useful format. Besides, the SBLSTM model is employed to categorize data into churners and non-churners. To improve the CCP outcomes of the SBLSTM model, an optimal hyperparameter tuning process using AOA is developed. A widespread simulation analysis of the AOA-SBLSTM model is tested using a benchmark dataset with 3333 samples and 21 features. The experimental outcomes reported the promising performance of the AOA-SBLSTM model over the recent approaches.

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A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation

Cited by 39 publications

References 37 publications

Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models

Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models

Churn Customer Management in Retail Industry: A Case Study

Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

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