Computer assisted customer churn management: State-of-the-art and future trends

Hadden, John; Tiwari, Ashutosh; Roy, Rajkumar; Ruta, Dymitr

doi:10.1016/j.cor.2005.11.007

Cited by 238 publications

(116 citation statements)

References 61 publications

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“…Buckinx and Van den Poel (2005), Hadden et al (2005), Reinartz and Kumar (2003), Song et al (2004), and Van den Poel and Larivière (2004) present literature reviews of customer churn studies. The Appendix 1 presents a review of the literature about customer churn prediction in the TI in contractual settings and continuous time, which is the scope of this study.…”

Section: Because (I) the Retention Of Unprofitable Customermentioning

confidence: 99%

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Detecting Customer Defections: An Application of Continuous Duration Models

Portela¹,

Menezes²

2011

JGSM

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Section: Because (I) the Retention Of Unprofitable Customermentioning

confidence: 99%

“…Nevertheless, the fixed telecommunications market is becoming saturated in Portugal and, as a consequence, the pool of "available customers" is limited and firms need to change their strategy from customer acquisition to customer retention (Hadden et al, 2005;Hung et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Detecting Customer Defections: An Application of Continuous Duration Models

Portela¹,

Menezes²

2011

JGSM

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“…: regression analysis, Naïve Bayes (NB), Support Vector Machine (SVM), Neural Network (NN). Regression analysis is the most popular technique to predict customer satisfaction [11], and the best technique for data sample less than 1000 compare to any data mining techniques [12]. For example, Mihelis et al (2001) use regression model to measure customer satisfaction.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining for Business Intelligence in Social Network: A survey

Bhanap¹,

Kawthekar²

2015

International Advanced Research Journal in Science, Engineering

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Abstract:In the era of digital world, due to the revolution in the field of Information and Communication Technology, Social Networks are providing an accessible platform for users to share and use information in almost all sectors of the society. As a result of this , pervasive usage of social network generates huge amount of social data. Mining this social data has its potential to extract actionable patterns, behave ors which are beneficial business organizations and customers. Different mining tools and techniques can be used to achieve better decision making in the field of Electronic Business Intelligence. This paper reviews basics of Data Mining, Social Network Analysis and its applications in Business Intelligence with data mining techniques and suggest how this survey and study of the data mining approaches can benefit the importance of social network analysis and mining for business intelligence.

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“…According to past research, maintaining existing customers is even more important than attracting new ones because the cost of winning a new customer is far greater than the cost of preserving an existing one [1]. Many methods have already been proposed for churn prediction using past data.…”

Section: Introductionmentioning

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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Computer assisted customer churn management: State-of-the-art and future trends

Cited by 238 publications

References 61 publications

Detecting Customer Defections: An Application of Continuous Duration Models

Detecting Customer Defections: An Application of Continuous Duration Models

Data Mining for Business Intelligence in Social Network: A survey

Impact of Social Network to Churn in Mobile Network

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