Customer Segmentation for Telecom with the k-means Clustering Method

Luo, Ye; Cai, Qi; Xi, Huan; Liu, Yijun; Zhu, Guangming

doi:10.3923/itj.2013.409.413

Cited by 17 publications

(7 citation statements)

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“…Actually, many of these efforts can be noticed in everyday events such as energy management [1], telecommunications [2], pollution [3], bioinformatics [4], earthquakes [5], and so forth. Accurate predictions are essential in economical activities as remarkable forecasting errors in certain areas may involve large loss of money.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

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Section: Introductionmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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“…K-means methodology is one of the most common methods used for customer clustering (Figueiredo et al, 2003;Niyagas et al, 2006;Windorto et al, 2019;Maheshwari et al, 2019;Rojlertjanya, 2019;Gustriansyah et al, 2020;Mousavi et al, 2020;Nugraha, 2020). The primary purpose of the k-means clustering is to form clusters that "minimize the squared error criterion" using the predetermined number of k values, which represents the number of clusters (Ye et al, 2013). To obtain an optimal number of clusters, the Elbow Method's interpretation would be appropriate before applying k-means clustering (Bholowalia and Kumar, 2014;Syakur et al, 2018;Anuşlu and Fırat, 2019;Nainggolan et al, 2019;Cui, 2020;Liu and Deng, 2020;Umargono et al, 2020).…”

Section: Data Mining and Customer Segmentationmentioning

confidence: 99%

Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

Serper¹,

Şen²,

Uslu³

2022

ibr

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Optimization models enable organizations to find the best solution and respond to the demand from an uncertain environment and stochastic process promptly and with less engineering effort. This study aims to optimize the number of seasonal agents and customer prioritization needed for a call center system using big data analytics and discrete event simulations to improve customer satisfaction. The study was carried out based on data from a leading heating and ventilation company's call center. The K-means clustering technique was used to determine customer segmentation on 6-million-customer data. For prioritization, the making of a Recency-Frequency-Monetary (RFM) analysis was applied. The system was modeled using ARENA simulation software, and performance parameters were measured depending on the segments obtained. The results show that the simulation model performed with data analytics gives better results for a beneficial financial impact with numerical values in customer prioritization, reducing the average waiting time of the most prioritized customers by more than 90%, and for the least prioritized customers, it increased the average waiting time by approximately just 40%. However, with the company segments, the increase in the average waiting time of the least prioritized customers was approximately 300%.

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“…A customer segmentation method was proposed by [22] for Jiangsu Changzhou Telco by using K-means clustering and commercial automated tool KXEN. The customer segmentation was done within small business customers in two dimensions, namely values and behaviours.…”

Section: B Customer Segmentationmentioning

confidence: 99%

Integrated Churn Prediction and Customer Segmentation Framework for Telco Business

et al. 2021

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In the telco industry, attracting new customers is no longer a good strategy since the cost of retaining existing customers is much lower. Churn management becomes instrumental in the telco industry. As there is limited study combining churn prediction and customer segmentation, this paper aims to propose an integrated customer analytics framework for churn management. There are six components in the framework, including data pre-processing, exploratory data analysis (EDA), churn prediction, factor analysis, customer segmentation, and customer behaviour analytics. This framework integrates churn prediction and customer segmentation process to provide telco operators with a complete churn analysis to better manage customer churn. Three datasets are used in the experiments with six machine learning classifiers. First, the churn status of the customers is predicted using multiple machine learning classifiers. Synthetic Minority Oversampling Technique (SMOTE) is applied to the training set to deal with the problems with imbalanced datasets. The 10-fold cross-validation is used to assess the models. Accuracy and F1-score are used for model evaluation. F1-score is considered to be an important metric to measure the models for imbalanced datasets since the premise of churn management is to be able to identify customers who will churn. Experimental analysis indicates that AdaBoost performed the best in Dataset 1, with accuracy of 77.19% and F1-score of 63.11%. Random Forest performed the best in Dataset 2, with accuracy of 93.6% and F1-score of 77.20%. Random Forest performed the best in Dataset 3 in terms of accuracy, at 63.09%, while Multi-layer Perceptron performed the best in terms of F1-score, at 42.84%. After implementing churn prediction, Bayesian Logistic Regression is used to conduct the factor analysis and to figure out some important features for churn customer segmentation. Churn customer segmentation is then carried out using K-means clustering. Customers are segmented into different groups, which allows marketers and decision makers to adopt retention strategies more precisely.

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Customer Segmentation for Telecom with the k-means Clustering Method

Cited by 17 publications

References 0 publications

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

Integrated Churn Prediction and Customer Segmentation Framework for Telco Business

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