The effect of social affinity and predictive horizon on churn prediction using diffusion modeling

Baras, Dorit; Ronen, Amir; Yom-Tov, Elad

doi:10.1007/s13278-014-0232-2

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…The real power of our approach is shown in the degree followers, where our approach is superior at the values of k between 3% and 10%. Consider, that the values of k usually used in practice are between 0.1% and 10% [7]. Therefore, at 70% of this interval, our method outperforms the other two.…”

Section: Evaluation Of the Churn-influence-prediction Modelmentioning

confidence: 94%

“…Although, only 20% of the whole possible range of values of k is used, this covers the whole applicable range typically used by service providers {k : 0.1% ≤ k ≤ 10%} [7]. For each configuration of k and n and each of the three ranking methods we counted the true positives (predicted churners that actually churned) in the test dataset.…”

Section: Evaluation Of the Churn-influence-prediction Modelmentioning

confidence: 99%

“…Using such models (henceforth, we refer to them as the classical models), the customers are assigned churn probability scores, which are necessary for labelling them as churners or non-churners. Then, the top k-percent (typically {k : 0.1% ≤ k ≤ 10%} [7]) of customers determined most likely to churn are offered incentives to prevent them from actually churning. However, not all customers are equally valuable to the company.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Influential Mobile-Subscriber Churners using Low-level User Features

2015

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In the last years, customer churn prediction has been very high on the agenda of telecommunications service providers. Among customers predicted as churners, highly influential customers deserve special attention, since their churns can also trigger churns of their peers. The aim of this study is to find good predictors of churn influence in a mobile service network. To this end, a procedure for determining the weak ground truth on churn influence is presented and used to determine the churn influence of prepaid customers. The determined scores are used to identify good churn-influence predictors among 74 candidate features. The identified predictors are finally used to build a churn-influence-prediction model. The results show that considerably better churn prediction results can be achieved using the proposed model together with the classical churn-prediction-model than by using the classical churn-prediction model alone. Moreover, the successfully predicted churners by the combined approach also have a greater number of churn followers. A successful retention of the predicted churners could greatly affect churn reduction since it could also prevent the churns of these followers. Key words: Churn prediction, User influence, Social network, Weak ground truth, Churn-influence modelPredvianje odljeva utjecajnih mobilnih pretplatnika korištenjem značajki niske razine. Posljednjih godina, predvianje odljeva korisnika jedna je on važnijih tema meu pružateljima telekomunikacijskih usluga. Meu odlazećim korisnicima, oni najutjecajniji zaslužuju posebnu pažnju, jer njihov odljev može okinuti i odljev sljedbenika. Cilj ovogčlanka je pronalazak dobrih prediktora utjecaja odljeva na mobilne uslužne mreže. U tu svrhu, razvijena je metoda za njihovu identifikaciju meu 74 potencijalna kandidata. Identificirani prediktori su potom korišteni za konačnu izgradnju modela predvianja odljeva korisnika. Znatno bolji rezultati ostvaruju se kada se koristi predloženi model u kombinaciji s klasičnim modelom, nego kada se klasični model koristi zasebno. Štoviše, kombiniranim predvianjem izdvojeni utjecajni korisnici imaju veći broj sljedbenika. Uspješno zadrža-vanje predvienog odljeva moglo bi uvelike utjecati na njegovo smanjenje, pošto bi samim time spriječilo i odljev sljedbenika.Ključne riječi: predvianje odljeva, utjecaj korisnika, društvena mreža, slabi referentni podaci, model utjecaja odljeva

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Section: Evaluation Of the Churn-influence-prediction Modelmentioning

confidence: 94%

Section: Evaluation Of the Churn-influence-prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Influential Mobile-Subscriber Churners using Low-level User Features

2015

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“…This SPA algorithm is an effective approach with which to determine potential churners affected by recent churners, who are strongly connected to them. However, several detailed studies of the SPA algorithm suggested possibilities of improving the model (Baras et al 2012;Kawale et al 2009). Kawale et al (2009) suggested that "churn energy" should spread through the user network in both positive and negative senses.…”

Section: Churn Prediction In the Literaturementioning

confidence: 99%

“…The model was evaluated on users of online multi-player role-playing games and found to perform better than the basic SPA algorithm. Baras et al (2012) compared several different methods of estimating the connection strength between users, which can be used as input to an SPA diffusion model. They showed that using a social measure (i.e., the number of shared vs. unshared outgoing neighbours) as a connection weight instead of using the number of calls between users significantly improves the predictive power of an SPA diffusion model.…”

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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Service discovery and prediction on Pervasive Information System

Najar

Pinheiro

Souveyet

2015

J Ambient Intell Human Comput

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International audienceRecent evolution of technology and its usages, such as BYOD (Bring Your Own Device) and IoT (Internet of Things), transformed the way we interact with Information Systems (IS), leading to a new generation of IS, called the Pervasive Information Systems (PIS). These systems have to face heterogeneous pervasive environments and hide the complexity of such environment end-user. In order to reach transparency and proactivity necessary for successful PIS, new discovery and prediction mechanisms are necessary. In this paper, we present a new user-centric approach for PIS and propose new service discovery and prediction based on both user's context and intentions. Intentions allow focusing on goals user wants to satisfy when requesting a service. Those intentions rise in a given context, which influence the service implementation. We propose a service discovery mechanism that observes user's context and intention in order to offer him/her the most appropriate service satisfying her/his intention on the current context. We also propose a prediction mechanism that tries to anticipate user's intentions considering the user's history and the observed context. We evaluate both mechanisms and discuss advanced features future PIS will have to deal with

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The effect of social affinity and predictive horizon on churn prediction using diffusion modeling

Cited by 8 publications

References 25 publications

Predicting Influential Mobile-Subscriber Churners using Low-level User Features

Predicting Influential Mobile-Subscriber Churners using Low-level User Features

A diffusion model for churn prediction based on sociometric theory

Service discovery and prediction on Pervasive Information System

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