Measuring churner influence on pre-paid subscribers using fuzzy logic

In the last decades, mobile phones have become the major medium for communication between humans. The site effect is the loss of subscribers. Consequently, Telecoms operators invest in developing algorithms for quantifying the risk to churn and to influence other subscribers to churn. The objective is to prioritize the retention of subscribers in their network due to the cost of obtaining a new subscriber is four times more expensive than retaining subscribers. Hence, we use Extremely Random Forest to classify churners and non-churners obtaining a Lift value at 10% of 5.5. Then, we rely on graph-based measures such as Degree of Centrality and Page rank to measure emitted and received influence in the social network of the carrier. Our methodology allows summarising churn risk score, relying on a Fuzzy Logic system, combining the churn probability and the risk of the churner to leave the network with other subscriber

Dropout Prediction: A Systematic Literature Review

Dropout predicting is challenging analysis process which requires appropriate approaches to address the dropout. Existing approaches are applied in different areas such as education, telecommunications, retail, social networks, and banking services. The goal is to identify customers in the risk of dropout to support retention strategies. This research developed a systematic literature review to evaluate the development of existing studies to predict dropout using machine learning, following the guidelines recommended by Kitchenham and Peterson. The systematic review followed three phases planning, conducting, and reporting. The selection of the most relevant articles was based on the use of Active Systematic Review tool using artificial intelligence algorithms. The criteria identified 28 articles and several research lines where identified. Dropout is a transversal problem for several sectors of economic activity, where it can be taken countermeasures before it happens if detected early

Applying data mining in telecommunications

This thesis applies data mining in commercial settings in the telecommunications industry. The research for this thesis has been performed at T-Mobile Netherlands B.V. and the methods described in some of the chapters have been also applied in Deutsche Telekom subsidiaries in other countries. We had a rare opportunity to work on real commercial data sets and have the results of our research deployed in practice. Throughout this thesis we describe some of the challenges that data miners (or data scientists) meet when working on business problems and our solutions to these problems. The complex data sets we were analyzing contained in certain cases millions of records. In this research we were using simple methods combined in innovative ways to achieve results that were either an improvement on how the business was previously solving these problems or solving important business problems that were not addressed before in such detail. We address the stages of CRISP-DM (CRoss Industry Standard Process for Data Mining), and our main focus is on the stages least covered in literature.T-Mobile Netherlands B.V.Algorithms and the Foundations of Software technolog

The Digital Transformation of the News Media Business – Paid Content and Entrepreneurship in Digital Journalism

The digital transformation of the news business continues to agitate publishers. Concerned about declining sales in the print segment, legacy outlets, local news companies and freelance journalists alike search for ways to monetize digital journalism properly. At first glance, digital journalism and its monetisation as paid content seem a promising effort. The digitisation of the news business enabled distribution at a marginal cost of almost zero while giving journalists access to new research technologies and lowering the cost of entry for smaller companies. However, while digital journalism enjoys broad popularity and use, online news are gaining few paying customers. Furthermore, online news compete within a larger digital media complex, comprising movies, games, and social media. After 25 years of experimentation, the digital future of journalism is still heavily debated in media management. Concerning the reconstitution as a digital medium, this research examines conditions of success and obstacles for the digital news media business to be successful as a business venture. Therefore, the research question reads What factors enable the viability and entrepreneurial success of the news media business in light of the consequences of digital transformation? The overarching research question is considered from two angles: The first angle concerns the demand side by looking at the antecedents of the audience's willingness to pay for paid content. The second angle focuses on the supply side and therefore examines antecedents of success in the context of digital journalistic start-ups and founders. In four studies, this thesis develops an analysis of the online news business with a local focus on the German news market. For this purpose, a variety of methods ranging from qualitative work and literature review to empirical research employing path analysis and predictive analytics are applied. Theoretically, digital transformation, free mentality and other peculiarities of information goods inform the frame of this work. Thus, this research aims at contributing to a financially sustainable news media business

JTIT

kwartalni

Exploration of customer churn routes using machine learning probabilistic models

The ongoing processes of globalization and deregulation are changing the competitive framework in the majority of economic sectors. The appearance of new competitors and technologies entails a sharp increase in competition and a growing preoccupation among service providing companies with creating stronger bonds with customers. Many of these companies are shifting resources away from the goal of capturing new customers and are instead focusing on retaining existing ones. In this context, anticipating the customer¿s intention to abandon, a phenomenon also known as churn, and facilitating the launch of retention-focused actions represent clear elements of competitive advantage. Data mining, as applied to market surveyed information, can provide assistance to churn management processes. In this thesis, we mine real market data for churn analysis, placing a strong emphasis on the applicability and interpretability of the results. Statistical Machine Learning models for simultaneous data clustering and visualization lay the foundations for the analyses, which yield an interpretable segmentation of the surveyed markets. To achieve interpretability, much attention is paid to the intuitive visualization of the experimental results. Given that the modelling techniques under consideration are nonlinear in nature, this represents a non-trivial challenge. Newly developed techniques for data visualization in nonlinear latent models are presented. They are inspired in geographical representation methods and suited to both static and dynamic data representation