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

Comparative analysis of selected probabilistic customer lifetime value models in online shopping

The selection of a suitable customer lifetime value (CLV) model is a key issue for companies that are introducing a CLV managerial approach in their online B2C relationship stores. The online retail environment places CLV models on several specific assumptions, e.g. non-contractual relationship, continuous purchase anytime, variable-spending environment. The article focuses on empirical statistical analysis and predictive abilities of selected probabilistic CLV models that show very good results in an online retail environment compared to different model families. For comparison, eleven CLV models were selected. The comparison has been made to the online stores’ datasets from Central and Eastern Europe with annual revenues of hundreds of millions of euros and with almost 2.3 million customers. Probabilistic models have achieved overall good and consistent results on the majority of the studied transactional datasets, with BG/NBD and Pareto/NBD models that can be considered stable with significant lifts from the baseline Status quo model. Abe's variant of Pareto/NBD have underperformed multiple criterions and would not be fully useful for the studied datasets without further improvements. In the end, the authors discuss the deployment implications of selected CLV models and propose further issues for future research to address

A Middleware framework for self-adaptive large scale distributed services

Modern service-oriented applications demand the ability to adapt to changing conditions and unexpected situations while maintaining a required QoS. Existing self-adaptation approaches seem inadequate to address this challenge because many of their assumptions are not met on the large-scale, highly dynamic infrastructures where these applications are generally deployed on. The main motivation of our research is to devise principles that guide the construction of large scale self-adaptive distributed services. We aim to provide sound modeling abstractions based on a clear conceptual background, and their realization as a middleware framework that supports the development of such services. Taking the inspiration from the concepts of decentralized markets in economics, we propose a solution based on three principles: emergent self-organization, utility driven behavior and model-less adaptation. Based on these principles, we designed Collectives, a middleware framework which provides a comprehensive solution for the diverse adaptation concerns that rise in the development of distributed systems. We tested the soundness and comprehensiveness of the Collectives framework by implementing eUDON, a middleware for self-adaptive web services, which we then evaluated extensively by means of a simulation model to analyze its adaptation capabilities in diverse settings. We found that eUDON exhibits the intended properties: it adapts to diverse conditions like peaks in the workload and massive failures, maintaining its QoS and using efficiently the available resources; it is highly scalable and robust; can be implemented on existing services in a non-intrusive way; and do not require any performance model of the services, their workload or the resources they use. We can conclude that our work proposes a solution for the requirements of self-adaptation in demanding usage scenarios without introducing additional complexity. In that sense, we believe we make a significant contribution towards the development of future generation service-oriented applications.Las Aplicaciones Orientadas a Servicios modernas demandan la capacidad de adaptarse a condiciones variables y situaciones inesperadas mientras mantienen un cierto nivel de servio esperado (QoS). Los enfoques de auto-adaptación existentes parecen no ser adacuados debido a sus supuestos no se cumplen en infrastructuras compartidas de gran escala. La principal motivación de nuestra investigación es inerir un conjunto de principios para guiar el desarrollo de servicios auto-adaptativos de gran escala. Nuesto objetivo es proveer abstraciones de modelaje apropiadas, basadas en un marco conceptual claro, y su implemetnacion en un middleware que soporte el desarrollo de estos servicios. Tomando como inspiración conceptos económicos de mercados decentralizados, hemos propuesto una solución basada en tres principios: auto-organización emergente, comportamiento guiado por la utilidad y adaptación sin modelos. Basados en estos principios diseñamos Collectives, un middleware que proveer una solución exhaustiva para los diversos aspectos de adaptación que surgen en el desarrollo de sistemas distribuidos. La adecuación y completitud de Collectives ha sido provada por medio de la implementación de eUDON, un middleware para servicios auto-adaptativos, el ha sido evaluado de manera exhaustiva por medio de un modelo de simulación, analizando sus propiedades de adaptación en diversos escenarios de uso. Hemos encontrado que eUDON exhibe las propiedades esperadas: se adapta a diversas condiciones como picos en la carga de trabajo o fallos masivos, mateniendo su calidad de servicio y haciendo un uso eficiente de los recusos disponibles. Es altamente escalable y robusto; puedeoo ser implementado en servicios existentes de manera no intrusiva; y no requiere la obtención de un modelo de desempeño para los servicios. Podemos concluir que nuestro trabajo nos ha permitido desarrollar una solucion que aborda los requerimientos de auto-adaptacion en escenarios de uso exigentes sin introducir complejidad adicional. En este sentido, consideramos que nuestra propuesta hace una contribución significativa hacia el desarrollo de la futura generación de aplicaciones orientadas a servicios.Postprint (published version

The antecedents of post-initial adoption behavior in a S-D logic context: leveraging the power of the viral metaphor to advance service innovation adoption

Repercussions of innovation adoption and diffusion studies have long been imperative to the success of novel introductions. However, perceptions and deductions of current innovation understandings have been changing over time. The paradigm shift from the goods-dominant (G-D) logic to the service-dominant (S-D) logic potentially makes the distinction between product (goods) innovation and service innovation redundant as the S-D logic lens views all innovations as service innovations (Vargo and Lusch, 2004; 2008; Lusch and Nambisan, 2015). From this perspective, product innovations are in essence service innovations, as goods serve as mere distribution mechanisms to deliver service. Nonetheless, the transition to such a broadened and transcending view of service innovation necessitates concurrently a change in the underlying models used to investigate innovation and its subsequent adoption. The present research addresses this gap by engendering a novel model for the most crucial period of service diffusion within the S-D logic context – the post-initial adoption phase, which demarcates an individual’s behavior after the initial adoption decision of a service. As a wellfounded understanding of service diffusion and the complementary innovation adoption still lingers in its infancy, the current study develops a model based on interdisciplinary domains mapping. Here fore, knowledge of the relatively established viral source domain is mapped to the comparatively undetermined target domain of service innovation adoption. To assess the model and test the importance of the explanatory variables, survey data from 750 respondents of a bank in Northern Germany is scrutinized by means of Structural Equation Modeling (SEM). The findings reveal that the continuance intention of a customer, actual usage of the service and the customer influencer value all constitute important postinitial adoption behavior that have meaningful implications for a successful service adoption. Second, the four constructs customer influencer value, organizational commitment, perceived usefulness and service customization are evidenced to have a differential impact on a iv customer’s post-initial adoption behavior. Third, this study indicates that post-initial adoption behavior further underlies the influence of a user’s age and besides that is also provoked by the internal and external environments of service adoption. Finally, this research amalgamates the broad view of service innovation by Nambisan and Lusch (2015) with the findings ensuing this enquiry’s model to arrive at a framework that it both, generalizable and practically applicable. Implications for academia and practitioners are captured along with avenues for future research

Service discovery and prediction on Pervasive Information System

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

Customer Relationship Management : Concept, Strategy, and Tools -3/E

Customer relationship management (CRM) as a strategy and as a technology has gone through an amazing evolutionary journey. After the initial technological approaches, this process has matured considerably – both from a conceptual and from an applications point of view. Of course this evolution continues, especially in the light of the digital transformation. Today, CRM refers to a strategy, a set of tactics, and a technology that has become indispensable in the modern economy. Based on both authors’ rich academic and managerial experience, this book gives a unified treatment of the strategic and tactical aspects of customer relationship management as we know it today. It stresses developing an understanding of economic customer value as the guiding concept for marketing decisions. The goal of this book is to be a comprehensive and up-to-date learning companion for advanced undergraduate students, master students, and executives who want a detailed and conceptually sound insight into the field of CRM

A data analysis approach to study events’ influence in social networks

Dissertação de mestrado em Computer ScienceNowadays, the assimilation of web content, by each individual, has a considerable impact on our’ everyday life. With the undeniable success of online social networks and microblogs, such as Facebook, Instagram and Twitter, the phenomenon of influence exerted by users of such platforms on other users, and how it propagates in the network, has been attracting, for some years computer scientists, information technicians, and marketing specialists. Increased connectivity, multi-model access and the rise of social media shortened the distance between almost every person in the world, more and more content is generated. Extracting and analyzing a significant amount of data is not a trivial task, Big Data techniques are essential. Through the analysis of this interaction, an exchange of information and feelings, it is entirely imaginable its usefulness in understanding complex human behaviours and so, help diverse organization’s decision-making. Influence maximization and viral marketing are among the possibilities. This work is intended to study what is the impact and role that an event’s social influence has and how does it propagate, particularly on its surrounding territory. This influence is inferred by analysis of the online platform’s data, by applying intelligent techniques, right after its extraction. The final step is to validate the results with data from different sources. Helping businesses through actionable and valuable knowledge is the ultimate goal. This document contemplates an introductory section where the study subject and its State of the Art are addressed. Next, the problem and what direction to take to solve it are discussed.Atualmente, a assimilação de conteúdo Web, por cada individuo, tem um impacto considerável no nosso quotidiano. Com o inegável sucesso de redes sociais e microblogs, como por exemplo Facebook, Instagram e Twitter, o fenómeno de influência exercida, por utilizadores de tais plataformas, em outros utilizadores e como se propaga na rede tem atraído, por alguns anos, informáticos, técnicos de informação e especialistas em marketing. O aumento da conectividade, o acesso multi-modal e a proliferação dos meios de comunicação social reduziram a distância entre quase todas as pessoas do mundo, mais e mais conteúdo é gerado. Extrair e analisar uma grande quantidade de dados não é uma tarefa trivial, são essenciais técnicas de Big Data. Através da análise desta interação, troca de informações e emoções, é perfeitamente imaginável a sua utilidade na compreensão de complexos comportamentos humanos e, portanto, ajudar na tomada de decisão de diversas organizações. A maximização da influência e o marketing viral estão entre as possibilidades. Este trabalho destina-se a estudar qual é o impacto e o papel que a influência social de um evento tem e como se propaga, particularmente no território envolvente. Esta influência é inferida pela análise dos dados de plataformas online, aplicando técnicas inteligentes, logo após a sua extração . O passo final é validar os resultados com dados de diferentes fontes. Ajudar empresas através do conhecimento valioso e atuável é o objetivo final. Este documento contempla uma seção introdutória, onde o assunto de estudo e o seu estado da arte são abordados. De seguida, é discutido o problema e a direção a seguir para o solucionar

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band II

Übersicht der Teilkonferenzen Band II • eHealth as a Service – Innovationen für Prävention, Versorgung und Forschung • Einsatz von Unternehmenssoftware in der Lehre • Energieinformatik, Erneuerbare Energien und Neue Mobilität • Hedonische Informationssysteme • IKT-gestütztes betriebliches Umwelt- und Nachhaltigkeitsmanagement • Informationssysteme in der Finanzwirtschaft • IT- und Software-Produktmanagement in Internet-of-Things-basierten Infrastrukturen • IT-Beratung im Kontext digitaler Transformation • IT-Sicherheit für Kritische Infrastrukturen • Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy) • Prescriptive Analytics in I