Model of Customer Churn Prediction on Support Vector Machine

Xia, Guoen; Jin, Weidong

doi:10.1016/s1874-8651(09)60003-x

Cited by 121 publications

(50 citation statements)

References 10 publications

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“…Ying et al [5] proposed the SVM method with different types of weighting parameters based on the characteristic of unbalanced size of positive and negative samples in actual customers churn data. Xia et al [6] compared methods such as artificial neural network, decision tree and Bayes classifier from aspects such as accuracy, hit rate, coverage rate and lifting coefficient. Gopal R K et al [7] firstly adopted ordinal regression method to model the users churn situation.…”

Section: Backgroundsmentioning

confidence: 99%

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Using Combined Model Approach for Churn Prediction in Telecommunication

Liu¹,

Zhang²,

Yang³

2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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Abstract:To solve the prediction problem of hybrid data in users' consumption information of telecommunication, the paper use the fuzzy K-Prototypes (FKP) and support vector machine (SVM) combined model to improve the accuracy of users churn prediction. In the combined model, FKP is adopted to cluster hybrid large data volume effectively, and then the samples nearby cluster center in each cluster as the input of SVM to promote the prediction efficiency. As shown in the experience validation results, the proposed FKP-SVM combined model has excellent performance in predicting churn, due to reduce the training time of hybrid large-scale data set and save system resources.

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Section: Backgroundsmentioning

confidence: 99%

“…The following evaluation model is adopted to assess the experiment results [6], true (actual) churn in datasets is compared with predicted churn, as shown in Table 2. Table 2, calculating the accuracy, hit rate and coverage rate of the model, the specific formula is as follows:…”

Section: Model Evaluationmentioning

confidence: 99%

Using Combined Model Approach for Churn Prediction in Telecommunication

Liu¹,

Zhang²,

Yang³

2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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“…Xia and Jin [19] first find out the criteria that affect churn using Factor Analysis and using these criteria, they build an SVM model in order to predict churn. Idris et al [9] find the optimum decision tree using Particle Swarm Optimization and conclude that metaheuristic-based decision trees outperform in terms of classification accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Churn Detection and Prediction in Automotive Supply Industry

Karapinar

Altay

Kayakutlu

2016

Annals of Computer Science and Information Systems

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Abstract-Companies have both large certified enterprises and small unauthorized service providers as their competitors in the automotive supply industry. As technology related industries undergo more intensive competition, churn detection and prediction become essential to be precautious about leaving customers. The literature for churn detection offers numerous statistical and intelligent methods. In this study, Artificial Neural Networks and Decision Trees are applied to detect the churn in and analyze the validity of these methods for the automotive supply industry. The problem involves both categorical and continuous numerical decision inputs which cannot simultaneously fed into Decision Trees. In this case, continuous inputs should be divided into binary categorical ones by splitting into various intervals which are called buckets. Particle Swarm Optimization algorithm is implemented for finding optimal buckets for the churn problem data. Results indicate that while both algorithms are promising, the bucket tuning for Decision Trees complicate the churn detection process.

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“…Other studies [5], [6], [7], [8] , [9], [10], [11], [12], [13] proposed different approaches for churn prediction in which users were considered individually.…”

Section: Previous Researchmentioning

confidence: 99%

“…In [13], the authors proposed a support vector machine model to predict churn, and compared it with artificial neural networks, decision trees, logistic regression, and naive Bayesian classifiers.…”

Section: Previous Researchmentioning

confidence: 99%

Impact of Social Network to Churn in Mobile Network

2015

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Original scientific paperAs the telecommunications sector has reached its mature stage, maintaining existing users has become crucial for service providers. Analyzing the call data records, it is possible to observe their users in the context of social network and obtain additional insights about the spread of influence among interconnected users, which is relevant to churn. In this paper, we examine the communication patterns of mobile phone users and subscription plan logs. Our goal is to use a simple model to predict which users are most likely to churn, solely by observing each user's social network, which is formed by outgoing calls, and churn among their neighbours. To measure the importance of social network parameters with regard to churn prediction, we compare three models: spatial classification, regression model, and artificial neural networks. For each subscriber, we observe three social network parameters, the number of neighbors that have churned, the number of calls to these neighbors, and the duration of these calls for different time periods. The results indicate that using only one or two of these parameters yields results that are comparable or better than the complex models with large amounts of individual and/or social network input parameters that other researchers have proposed.Key words: Churn, Social Network Analysis, Machine Learning, Mobile Network Utjecaj društvene mreže na odljev korisnika u mobilnim mrežama. Kako je telekomunikacijski sektor dosegao zreli stadij, zadržavanje postojećih korisnika od ključne je važnosti za pružatelje telekomunikacijskih usluga. Analizom liste poziva moguće je nadzirati korisnike u kontekstu društvene mreže i dobiti dodatni uvid u širenje utjecaja među povezanim korisnicima, što je relevantno za odljev korisnika. U ovom radu razmatramo obrasce komunikacije korisnika mobilnih mreža i podatke o planu pretplate. Naš cilj je korištenjem jednostavnog modela predvidjeti koji korisnici su najskloniji prijelazu na drugu mrežu, pritom koristeći samo korisnikovu društvenu mrežu koja se formira odlaznim pozivima i prijelazima između mreža njihovih susjeda. S ciljem mjerenja važnosti pojedinog parametra društvene mreže za predikciju prelaska na drugu mrežu uspoređena su tri modela: prostorna klasifikacija, regresijski model i model neuronske mreže. Za svakog pretplatnika razmatramo tri parametra društvene mreže: broj susjeda koji su promijenili mrežu, broj poziva prema njima kao i trajanje spomenutih poziva u različitim vremenskim razdobljima. Rezultati pokazuju kako se korištenjem samo jednog ili dva od navedenih parametara društvene mreže postižu rezultati koji su usporedivi ili bolji od rezultata složenijih modela drugih autora koji koriste veliki broj osobnih parametara i/ili parametara društvene mreže.

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Model of Customer Churn Prediction on Support Vector Machine

Cited by 121 publications

References 10 publications

Using Combined Model Approach for Churn Prediction in Telecommunication

Using Combined Model Approach for Churn Prediction in Telecommunication

Churn Detection and Prediction in Automotive Supply Industry

Impact of Social Network to Churn in Mobile Network

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