Estimating the effect of word of mouth on churn and cross-buying in the mobile phone market with Markov logic networks

Dierkes, Torsten; Bichler, Martin; Krishnan, Ramayya

doi:10.1016/j.dss.2011.01.002

Cited by 59 publications

(49 citation statements)

References 45 publications

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“…In [14], the authors compared the performance of MLN and logistic regression models that considered both network and user description attributes. The benchmark against the logistic regression model which considered only individual attributes proved that the MLN model did not perform better than the benchmark.…”

Section: Analysis Of Social Impact On Churn Probabilitymentioning

confidence: 99%

“…Using Markov Logic Networks (MLNs), Dierkes et al [14] examined whether the user churn decisions of individuals in previous time periods impacted on other users with whom the target customer interacted via voice call, short message service (SMS), or multimedia message service (MMS). To design the prediction model, the authors selected a data sample consisting of 2,654 users, of which 645 had churned and 2,000 had not.…”

Section: Previous Researchmentioning

confidence: 99%

“…In [14], the authors used a dataset sample consisting of 2,645 customers, containing 645 churners and 2,000 non-churners. In the paper, they stated it was not possible to run the model with a larger dataset.…”

Section: Analysis Of Social Impact On Churn Probabilitymentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Analysis Of Social Impact On Churn Probabilitymentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Social Network to Churn in Mobile Network

2015

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“…Most works make use of converting relational representation data to feature-based data and enriching "classical" features. Some works include different centralities (e.g., betweenness and closeness) as additional features, some add features such as the number of neighbouring churners and the number of calls to neighbouring churners (Dierkes et al 2011), and others use calculated network attributes, such as the neighbour composition, tie strength, similarity, and homophily (Zhang et al 2010(Zhang et al , 2012. Richter et al (2010) proposed a systematic study that evaluates the relevance of group-related features to churn.…”

Section: Churn Prediction In the Literaturementioning

confidence: 99%

A diffusion model for churn prediction based on sociometric theory

Droftina

Štular

Košir

2014

Adv Data Anal Classif

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Churn prediction has received much attention in the last decade. With the evolution of social networks and social network analysis tools in recent years, the consideration of social ties in churn prediction has proven promising. One possibility is to use energy diffusion models to model the spread of influence through a social network. This paper proposes a novel churn prediction diffusion model based on sociometric clique and social status theory. It describes the concept of energy in the diffusion model as an opinion of users, which is transformed to user influence using the derived social status function. Furthermore, a novel diffusion model prediction scheme applicable to a single user or a small subset of users is described: the Targeted User Subset Churn Prediction Scheme. The scheme allows fast churn prediction using limited computing resources. The diffusion model is evaluated on a real dataset of users obtained from the largest Slovenian mobile service provider, using the F-measure and lift curve. The empirical results show a significant improvement in prediction accuracy of the proposed method compared with the basic spreading activation technique (SPA) diffusion model. More specifically, our approach outperforms a basic SPA diffusion model by 116 % in terms of lift in the fifth percentile.U. Droftina (B) · M. Štular

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“…Perhaps the closest is the Markov logic network described in [11], which employs firstorder logic and graphical model to simulate churn behaviors. However, it requires extensive human intervention (e.g., handcoded graph structure) and its scalability is rather limited so far.…”

Section: Introductionmentioning

confidence: 99%

Collective Churn Prediction in Social Network

Oentaryo

Lim

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach based on collective classification (CC), which accounts for both the intrinsic and extrinsic factors by utilizing the local features of, and dependencies among, individuals during prediction steps. We evaluate our CC approach using real data provided by an established mobile social networking site, with a primary focus on prediction of churn in chat activities. Our results demonstrate that using CC and social features derived from interaction records and network structure yields substantially improved prediction in comparison to using conventional classification and user profile features only.

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Estimating the effect of word of mouth on churn and cross-buying in the mobile phone market with Markov logic networks

Cited by 59 publications

References 45 publications

Impact of Social Network to Churn in Mobile Network

Impact of Social Network to Churn in Mobile Network

A diffusion model for churn prediction based on sociometric theory

Collective Churn Prediction in Social Network

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