Customer Churn Prediction Using Improved One-Class Support Vector Machine

Yu, Zhiwu; Li, Bing; Li, Xiu; Liu, Wenhuang; Ren, Shiquan

doi:10.1007/11527503_36

Cited by 74 publications

(40 citation statements)

References 3 publications

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“…In churn analysis, they are applied to find the churn prediction for a customer, or a set of customers. Among various approaches used in literature for predicting churners [12,24,27,29] heuristic based techniques are gaining more focus due to their ability of finding nearly optimal solutions with low time complexity [18]. In [12], for example, Neural Network (NN) based approach that estimates the possibility of churn based on customer complaints was examined.…”

Section: Related Workmentioning

confidence: 99%

“…In a nutshell, cellular service data was analyzed using a genetic algorithm based NN resulting in a better prediction than the z-score statistical model. Another attempt is [29] where authors investigated Support Vector Machine (SVM) for churn prediction. Their dataset contained different categories of input variables including: customers' demographic data, quality of service, and marketing-related options.…”

Section: Related Workmentioning

confidence: 99%

“…Among the various techniques used for predicting customer churn [11,18,24,27,29,30] Genetic Programming (GP) was not investigated. Given the problem described in this paper, we believe that GP features can significantly contribute to a better churn prediction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Initializing Genetic Programming using Fuzzy Clustering and its Application in Churn Prediction in the Telecom Industry

Al-Shboul

Faris

Ghatasheh

2015

MJCS

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Initializing Genetic Programming using Fuzzy Clustering and its Application in Churn Prediction in the Telecom Industry

Al-Shboul

Faris

Ghatasheh

2015

MJCS

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show abstract

“…Companies go this length because the cost of retaining an existing customer is far less than acquiring a new one [4]. The telecommunications industry gives special attention to this problem [5], [6], [7], [8], [9], [10], [11], [12]. This is due to the low barriers involved in switching service providers.…”

Section: B Studies On Churn Ratesmentioning

confidence: 99%

Modeling Attrition in Organizations from Email Communication

Patil

Long

Shen

et al. 2013

2013 International Conference on Social Computing

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Abstract-Modeling people's online behavior in relation to their real-world social context is an interesting and important research problem. In this paper, we present our preliminary study of attrition behavior in real-world organizations based on two online datasets: a dataset from a small startup (40+ users) and a dataset from one large US company (3600+ users). The small startup dataset is collected using our privacy-preserving data logging tool, which removes personal identifiable information from content data and extracts only aggregated statistics such as word frequency counts and sentiment features. The privacypreserving measures have enabled us to recruit participants to support this study. Correlation analysis over the startup dataset has shown that statistically there is often a change point in people's online behavior, and data exhibits weak trends that may be manifestation of real-world attrition. Same findings are also verified in the large company dataset. Furthermore, we have trained a classifier to predict real-world attrition with a moderate accuracy of 60-65% on the large company dataset. Given the incompleteness and noisy nature of data, the accuracy is encouraging.

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“…There exist different studies in various industries regarding developing computer assisted models for churn prediction (Coussement & Van den Poel, 2008a;Wei & Chiu, 2002;Yan, Fassino, & Baldasare, 2005;Coussement & Van den Poel, 2008b;Hung, Yen, & Wang, 2006;Zhao, Li, Li, Liu, & Ren, 2005).…”

Section: -Literature Reviewmentioning

confidence: 99%

A Dual-Step Multi-Algorithm Approach for Churn Prediction in Pre-Paid Telecommunications Service Providers

Jahromi¹,

Moeini²,

Akbari³

et al. 2010

RISUS

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Nowadays customer churn has become the main concern of companies which are active in different industries. Among all industries which suffer from this issue, telecommunications industry can be considered in the top of the list with approximate annual churn rate of 30%. Dealing with this problem, there exist different approaches via developing predictive models for customer churn but due to the nature of pre-paid mobile telephony market which is not contract-based, customer churn is not easily traceable and definable, thus constructing a predictive model would be of high complexity. Handling this issue, in this study, we developed a dual-step model building approach, which consists of clustering phase and classification phase. With this regard firstly, the customer base was divided into four clusters, based on their RFM related features, with the aim of extracting a logical definition of churn, and secondly, based on the churn definitions that were extracted in the first step, different algorithms were utilized with the intention of constructing predictive models for churn in our developed clusters. Evaluating and comparing the performance of the employed algorithms based on "gain measure", we concluded that employing a multi-algorithm approach in the model constructing step, instead of single-algorithm one, can bring the maximum gain among the tested algorithms.

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Customer Churn Prediction Using Improved One-Class Support Vector Machine

Cited by 74 publications

References 3 publications

Initializing Genetic Programming using Fuzzy Clustering and its Application in Churn Prediction in the Telecom Industry

Initializing Genetic Programming using Fuzzy Clustering and its Application in Churn Prediction in the Telecom Industry

Modeling Attrition in Organizations from Email Communication

A Dual-Step Multi-Algorithm Approach for Churn Prediction in Pre-Paid Telecommunications Service Providers

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