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

Examining individual differences through ‘everyday’ smartphone behaviours: Exploring theories and methods.

The mass adoption of digital technologies has instigated a transition whereby people are no longer ‘independent organic actors’ in society but have amalgamated with the technology they use on a daily basis. Consequently, people leave behind a ‘digital fingerprint’ whenever they use technologies such as smartphones, and the qualities of this trace can predict a variety of characteristics about the user. In this thesis, I explore how individual differences such as personality, demographics, and health relate to directly observable smartphone behaviours, that are logged ‘in situ’ via software installed on the device itself. By adopting an interdisciplinary approach between psychology and computer science, this thesis primarily considers the theoretical (chapter two), ethical (chapter three) and methodological (chapter four) underpinnings required to explore these human-smartphone relationships. Notably, traces of use do not have to be complex, as meta-data such as the smartphone operating system a person uses can reveal information regarding a user’s personality, as long as there is trace-to-trait relevance. Findings from chapters five and six also reveal that some individual differences can be better predicted from objective smartphone use than others. For example, age and gender can be discerned from smartphone usage logs whereas, mental health variables only had small positive correlations with smartphone screen time. However, an important contribution of this thesis resides in its methodological considerations, as self-reports of technology use can impact the relationships with individual differences and cannot be used as a substitute for objective logs. All the above has applied implications for security and health, which can benefit from the ability to infer characteristics about people, when self-reports are arduous, unfeasible or lack scientific rigour