Customer Churn Prediction for Broadband Internet Services

Huang, B. Q.; Kechadi, Tahar; Buckley, Brian

doi:10.1007/978-3-642-03730-6_19

Cited by 29 publications

(18 citation statements)

References 10 publications

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“…call-details that consists of five-month call-details M 4, ..., M 1, M. The time series training data lags one month behind testing data. The detail of these features can be found in [3,16].…”

Section: Datasetmentioning

confidence: 99%

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A Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction

Huang

Chen

et al. 2016

Advances in Data Mining. Applications and Theoretical Aspects

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Abstract. Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However most of rulebased learning algorithms designed with the assumption of well-balanced datasets, may provide unacceptable prediction results. This paper introduces a Fuzzy Association Rule-based Classification Learning Algorithm for customer churn prediction. The proposed algorithm adapts CAIM discretization algorithm to obtain fuzzy partitions, then searches a set of rules using an assessment method. The experiments were carried out to validate the proposed approach using the customer services dataset of Telecom. The experimental results show that the proposed approach can achieve acceptable prediction accuracy and efficient for churn prediction.

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Section: Datasetmentioning

confidence: 99%

“…For most services, once the number of service subscribes reaches its peak, the company places a special emphasis on the retention of valuable customers because acquiring new customer is difficult and costly. Recently, data mining techniques have been emerged to tackle the challenging problem of customer churn [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction

Huang

Chen

et al. 2016

Advances in Data Mining. Applications and Theoretical Aspects

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“…This is due to the low barriers involved in switching service providers. The problem has been studied in other service-based industries as well, such as banking [13], ISPs [14], credit cards [15], insurance [16] and P2P networks [17].…”

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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“…Churn has been analysed in a wide range of industries: most widely in the telcom sector [1], [2], [3], but also in the fields of banking [4], Internet service providers [5], P2P networks [6], online games [7], and much more. In its most general sense churn refers to the full or partial defection of a customer.…”

Section: Churn In the Literaturementioning

confidence: 99%

Churn in Social Networks: A Discussion Boards Case Study

Karnstedt

Hennessy

Chan

et al. 2010

2010 IEEE Second International Conference on Social Computing

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Churn has been identified as an important issue in a wide range of industries. In social networks, churn represents a significant risk for the health and functioning of communities. However, the importance and actual meaning of churn in social networks is almost unexplored. This work provides a general view on these issues and discusses aspects that are especially relevant to discussion boards. We provide a broad literature review on "traditional" churn analysis and prediction and highlight the specialities of churn in social networks. We further present an empirical analysis of a churn definition particularly appropriate for discussion boards and propose future research directions for predicting churn in social networks, focusing on the importance of social roles, influence and influence diffusion.

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Customer Churn Prediction for Broadband Internet Services

Cited by 29 publications

References 10 publications

A Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction

A Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction

Modeling Attrition in Organizations from Email Communication

Churn in Social Networks: A Discussion Boards Case Study

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