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

© 2018 Elsevier Ltd 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)

Predviđanje odljeva utjecajnih mobilnih pretplatnika korištenjem značajki niske razine

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.Posljednjih godina, predviđanje odljeva korisnika jedna je on važnijih tema među pružateljima telekomunikacijskih usluga. Među odlazećim korisnicima, oni najutjecajniji zaslužuju posebnu pažnju, jer njihov odljev može okinuti i odljev sljedbenika. Cilj ovog članka je pronalazak dobrih prediktora utjecaja odljeva na mobilne uslužne mreže. U tu svrhu, razvijena je metoda za njihovu identifikaciju među 74 potencijalna kandidata. Identificirani prediktori su potom korišteni za konačnu izgradnju modela predviđanja 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 predviđanjem izdvojeni utjecajni korisnici imaju veći broj sljedbenika. Uspješno zadržavanje predviđenog odljeva moglo bi uvelike utjecati na njegovo smanjenje, pošto bi samim time spriječilo i odljev sljedbenika

Increasing the robustness of uplift modeling using additional splits and diversified leaf select

While the COVID-19 pandemic negatively affects the world economy in general, the crisis accelerates concurrently the rapidly growing subscription business and online purchases. This provokes a steadily increasing demand of reliable measures to prevent customer churn which unchanged is not covered. The research analyses how preventive uplift modeling approaches based on decision trees can be modified. Thereby, it aims to reduce the risk of churn increases in scenarios with systematically occurring local estimation errors. Additionally, it compares several novel spatial distance and churn likelihood respecting selection methods applied on a real-world dataset. In conclusion, it is a procedure with incorporated additional and engineered decision tree splits that dominates the results of an appropriate Monte Carlo simulation. This newly introduced method lowers probability and negative impacts of counterproductive churn prevention campaigns without substantial loss of expected churn likelihood reduction effected by those same campaigns

Network Churn: The Effects of Self-Monitoring Personality on Brokerage Dynamics

The apparent stability of social network structures may mask considerable change and adjustment in the ties that make up the structures. In this study, we theorize and test-using longitudinal data on friendship relations from a radiology department located in the Netherlands-the idea that the characteristics of this "network churn" and the resultant brokerage dynamics are traceable to individual differences in self-monitoring personality. High self-monitors were more likely than low self-monitors to attract new friends and to occupy new bridging positions over time. In comparison to low self-monitors, the new friends that high self-monitors attracted tended to be relative strangers, in the sense that they were unconnected with previous friends, came from different functions, and more efficiently increased the number of structural holes in the resultant network. Our study suggests that dispositional forces help shape the dynamic structuring of networks: individuals help (re)create the social network structures they inhabit. © 2010 by Johnson Graduate School

How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage

As truly ubiquitous wearable computers, mobile phones are quickly becoming the primary source for social, behavioral and environmental sensing and data collection. Today’s smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In many cases, this analysis work is the result of exploratory forays and trial-and-error. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones for different sizes of analyzed networks. In particular, we examine how the ability to predict individual features and social links is incrementally enhanced with the accumulation of additional data. To accomplish this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 130 adult members of a young-family residential community over the course of a year and consequently has become one of the most comprehensive mobile phone datasets gathered in academia to date. Our results show that features such as ethnicity, age and marital status can be detected by analyzing social and behavioral signals. We then investigate how the prediction accuracy is increased when the users sample set grows. Finally, we propose a method for advanced prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. These predictions have practical implications, such as influencing the design of mobile data collection campaigns or evaluating analysis strategies

Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes

Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily, and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities