Time series for early churn detection: Using similarity based classification for dynamic networks

Óskarsdóttir, María; Calster, Tine Van; Baesens, Bart; Lemahieu, Wilfried; Vanthienen, Jan

doi:10.1016/j.eswa.2018.04.003

Cited by 43 publications

(12 citation statements)

References 33 publications

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“…The literature highlights a great diversity of ML and data mining techniques for CCP. Among these are Decision tree, Logistic regression and Random forests (De Caigny et al, 2018;Höppner, Stripling, Baesens, & Verdonck, 2017;Nisha, 2016), Support vector machine (Dong, Suen, & Krzyzak, 2005;Farquad, Ravi, & Raju, 2014;He, Shi, Wan, & Zhao, 2014;Renjith, 2017;Wang, Zhang, & Yu, n.d.), Active learning (Jamil & Khan, 2016;Verbeke, Martens, Mues, & Baesens, 2011), Rough set theory (Amin et al, 2016;Amin, Anwar, Adnan, Khan, & Iqbal, 2015), Negative correlation learning (Rodan, Fayyoumi, Faris, Alsakran, & Al-Kadi, 2015), Dynamic networks (Óskarsdóttir, Van Calster, Baesens, Lemahieu, & Vanthienen, 2018), AdaBoost ensemble techniques (Idris et al, 2017), Sequential pattern mining (Culbert et al, 2018), Recursive PARTioning (RPART) (Vafeiadis et al, 2015) Artificial neural network (Kasiran, Ibrahim, Syahir, & Ribuan, 2014;Tsai & Lu, 2009;Zakaryazad & Duman, 2016). The aforementioned discussion shown the importance of CCP as it is very beneficial assets of competitive businesses and equally important for various domain, such as; Banking sector (Chitra & Subashini, 2011;He et al, 2014;Oyeniyi & Adeyemo, 2015), Financial services (Charles et al, 2017;He et al, 2014), Social networks (Maria, Verbeke, Sarraute, Baesens, & Vanthienen, 2016;Óskarsdóttir et al, 2017;Verbeke, Martens, & Baesens, 2014), Online gaming industry (Kawale, Pal, & Srivastava, 2009;Suznjevic, Stupar, & Matijasevic, 2011), Human resource man-agement (Sarad...…”

Section: Customer Churn and Ccp Modelingmentioning

confidence: 99%

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Amin

Shah

Khattak

et al. 2019

International Journal of Information Management

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Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).

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Section: Customer Churn and Ccp Modelingmentioning

confidence: 99%

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Amin

Shah

Khattak

et al. 2019

International Journal of Information Management

View full text Add to dashboard Cite

show abstract

“…Computational results show that it is somehow related to the relationship of each customer with other banks. Inspired by Óskarsdóttir et al [ 10 ], it would be interesting to better understand the motivations to churn, including “social contagion”.…”

Section: Discussionmentioning

confidence: 99%

“…Most of the referenced work consider applications in the telecommunication sector (see, for instance, [ 10 – 15 ]). Applications of churn prediction methods in other sectors can also be found.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Customer Churn Prediction in Retail Banking

Dias

Godinho

Torres

2020

Lecture Notes in Computer Science

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Predicting in advance whether a given customer will end his relationship with a company has an undeniable added value for all organizations, since targeted campaigns can be prepared to promote customer retention. In this work, six different methods using machine learning have been investigated on the retail banking customer churn prediction problem, considering predictions up to 6 months in advance. Different approaches are tested and compared using real data. Out of sample results are very good, even with very challenging out-of-sample sets composed only of churners, that truly test the ability to predict when a customer will churn. The best results are obtained by stochastic boosting, and the most important variables for predicting churn in a 1–2 months horizon are the total value of bank products held in recent months and the existence of debit or credit cards in another bank. For a 3–4 months horizon, the number of transactions in recent months and the existence of a mortgage loan outside the bank are the most important variables.

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“…Although it has been shown that there is an influence among customers' relationships and their churn decision (Óskarsdóttir, Van Calster, Baesens, Lemahieu, & Vanthienen, 2018), as far as we know, centrality metrics over temporal graphs have not been applied over telco customers in the context of churn prediction.…”

Section: Social Network Analysis For Churn Predictionmentioning

confidence: 99%

Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts

Calzada-Infante

Óskarsdóttir

Baesens

2020

Expert Systems with Applications

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The telecommunication industry is a saturated market where a proper implementation of a retention campaign is critical to be competitive, since retaining a customer is cheaper than attracting a new one. Hence, it is crucial to detect customer behavioral patterns and define accurate approaches to predict potential churners. Multiple researchers have used binary classification methods to predict churn of customers. Some of them verify that customers' social relationships influence the decision of changing the operator.We propose a novel method to extract the dynamic relevance of each customer using social network analysis techniques with a binary classification method called similarity forests. The dynamic importance of each customer is determined by applying various centrality metrics over temporal graphs, to represent the relationships between customers and to extract behavioral patterns of churners and non-churners. These relationships are established in a temporal graph using the call detail records (CDR) of telco's customers. In this paper, we compare the performance of different centrality metrics applied over two types of temporal graphs: Time-Order Graph and Aggregated Static Graph.

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Time series for early churn detection: Using similarity based classification for dynamic networks

Cited by 43 publications

References 33 publications

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Machine Learning for Customer Churn Prediction in Retail Banking

Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts

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