Predicting customer churn in mobile industry using data mining technology

Lee, Eui-Bang; Kim, Jinwha; Lee, Sang-Gun

doi:10.1108/imds-12-2015-0509

Cited by 51 publications

(35 citation statements)

References 42 publications

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“…This study uses predictor variables such as devices used by customers, billing and charging customers, both voice and SMS data and internet usage. This is in general also consistent with research conducted by Govindaraju et al and Lee et al [6,7]. However, there are predictor variables that are not used in the Govindaraju et al such as number of days of service and data in each month, customer USIM usage, operating system of device used by customer and customer location for the first time [7].…”

Section: Resultssupporting

confidence: 86%

“…However, there are predictor variables that are not used in the Govindaraju et al such as number of days of service and data in each month, customer USIM usage, operating system of device used by customer and customer location for the first time [7]. In addition there are predictor variables that cannot enter into Lee et al such as the length of time the customer, the number of days of service and data in each month, the purchase of credit by the customer, the duration of voice usage and the amount of SMS usage [6].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Telecom Customer’s Segmentation Using Decision Tree to Increase Active Electronic Money Subscribers

Noor¹,

Ariyanti²,

Alamsyah³

2019

Proceedings of the 1st International Conference on Economics, Business, Entrepreneurship, and Finance (ICEBEF 2018)

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The ABC telecommunication company as one of electronic money providers has more than 100 million customers. If it is compared to the number of electronic money customers which have growing potential. In December 2017, the number of electronic customers owned by ABC was on the third rank of electronic money ownership. Some efforts to increase the number of electronic money customers have been conducted but it has not achieved the expected target. Based on the aforementioned problem so that identifying future customers from potential telecommunication customers to be active electronic customers thus campaign activity can be done effectively and controllably. Therefore, a customer predicting model is needed to predict potential customers to be active electronic customers. This research creates model which can be used to predict future customers using telecommunication transaction act at ABC Company. The analysis used was telecommunication transaction data for all electronic money customers with 32 variables. Those variables were formed from variables such as voice, SMS and internet usage including other forming transaction such as customer's dominant location, operating system from device and device type used by customers. Forming method model used decision tree with accuracy (ACC) measuring evaluation, positive prediction value (PPV), negative prediction value (NPV), true positive rate (TPR) and true negative rate (TNR). Based on the evaluation result, this model can predict the future customers who will be the active electronic customers for 54,09%.

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Section: Resultssupporting

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Telecom Customer’s Segmentation Using Decision Tree to Increase Active Electronic Money Subscribers

Noor¹,

Ariyanti²,

Alamsyah³

2019

Proceedings of the 1st International Conference on Economics, Business, Entrepreneurship, and Finance (ICEBEF 2018)

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show abstract

“…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]. A churn model is also available to solve unbalanced, scatter and high dimensional problem in telecom datasets [24].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Anjum¹,

Usman²,

Zeb³

et al. 2017

ijacsa

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Abstract-Companies are investing more in analytics to obtain a competitive edge in the market and decision makers are required better identification among their data to be able to interpret complex patterns more easily. Alluring thousands of new customers is worthless if an equal number is leaving. Business Intelligence (BI) systems are unable to find hidden churn patterns for the huge customer base. In this paper, a decision support system has been proposed, which can predict the churning behaviour of a customer efficiently. We have proposed a procedure to develop an analytical system using data mining as well as machine learning techniques C5, CHAID, QUEST, and ANN for the churn analysis and prediction for the telecommunication industry. Prediction performance can be significantly improved by using a large volume and several features from both Business Support Systems (BSS) and Operations Support Systems (OSS). Extensive experiments are performed; marginal increases in predictive performance can be seen by using a larger volume and multiple attributes from both Telco BSS and OSS data. From the results, it is observed that using a combination of techniques can help to figure out a better and precise churn prediction model.

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“…This leads to the importance of managing customer churn. Customer churn prediction has already had fruitful results in the domains of banking, telecoms, insurance, and retail industries (Coussement, Lessmann, & Verstraeten, ; Hung, Yen, & Wang, ; Kumar & Ravi, ; Lee, Kim, & Lee, ; Wei & Chiu, ; Zhang, Liang, Li, Zheng, & Berry, ). For example, Glady, Baesens, and Croux () used customer lifetime value for the modelling and prediction of customer churn.…”

Section: Related Workmentioning

confidence: 99%

“…Data mining and machine learning techniques have been applied in various application domains for knowledge discovery and business intelligence (Gupta, Ahuja, Malhotra, Bala, & Kaur, 2017;Kim et al, 2017;Le, Gabrys, & Nauck, 2017;Lee et al, 2017;Márquez-Vera et al, 2016;Moro, Cortez, & Rita, 2018). Machine learning techniques have also been compared for customer churn prediction (Vafeiadis, Diamantaras, Sarigiannidis, & Chatzisavvas, 2015).…”

Section: Customer Churn Predictionmentioning

confidence: 99%

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

et al. 2019

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In virtual worlds (VWs), users have more VW games alternatives, whereas VW companies consequently suffer from high customer turnover rate and low customer loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is important. The churn behaviours and the impact of social neighbour influences to customer churn may be different for different types of users. Accordingly, we segment users into stable, unstable, and solitary user groups according to their social contact behaviours in VWs. Novel segmentation‐based churn prediction approaches are proposed for churn prediction in VWs by building prediction models for each type of user groups and considering the effect of social neighbour influences for different user groups. The proposed approaches are evaluated by conducting experiments with a dataset collected from a VW platform. The experimental results show different churn prediction performances under different user groups. The segmentation‐based churn prediction approaches perform better than do general approaches without considering user groups. Moreover, the results also reveal that social neighbour influences have a positive impact on stable and unstable users. The proposed work contributes to investigating the social neighbour influences on churn prediction for different types of user groups in VWs.

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Predicting customer churn in mobile industry using data mining technology

Cited by 51 publications

References 42 publications

Telecom Customer’s Segmentation Using Decision Tree to Increase Active Electronic Money Subscribers

Telecom Customer’s Segmentation Using Decision Tree to Increase Active Electronic Money Subscribers

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

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