Mobile Customer Clustering Analysis Based on Call Detail Records

Competition in the mobile telecommunications industry is becoming more and more fierce. In order to improve mobile operator’s competitiveness and customer value, several data mining technologies can be used. One of the most important data mining technologies is customer clustering analysis. This targeting practice has been proven manageable and effective for mobile telecommunications industry. Most telecommunications carriers cluster their mobile customers by billing system data. This paper discusses how to cluster mobile customers based on their call detail records and analyze their consumer behaviors. Finally, an application of a mobile customer clustering analysis is given in this paper

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

Telecom customer segmentation and precise package design by using data mining

Changes in the form of communication have prompted the telecommunications industry to flourish. In the "big data era" of information explosion, as one of the leading industries in the information age, the development of the telecommunications industry depends not only on communication technology, but also on the ability of enterprises to optimize resource allocation. At present, the information resources owned by telecom companies mainly come from customers. During the development process, they have accumulated a large amount of customer data, which truly and objectively reflects the behavior of consumers. This paper is dedicated to combining data mining technology with the rich data resources of the telecom industry and the latest marketing theories, not only effectively helping subdivide the telecommunications customer market, but also supporting telecommunications companies in developing more accurate and efficient marketing strategies. In addition, data analysis method such as factor analysis, regression analysis and discriminant analysis are used to analyze the demographic, business, SMS messages and expense characteristics of telecom customers, providing a new vision and reference for the telecom industry to achieve accurate packaging design. Based on the above research results, a discriminant model for the loss of telecom customers is constructed, which will help telecommunications companies to obtain a control method for telecom customer management risk. At last, data mining technology is used to optimize the combination design of telecommunication services, which offer effective advice on precise telecom package design to telecommunications companies

Business Intelligence Applications and Data Mining Methods in Telecommunications: A Literature Review

Telecommunication companies are operating today in an extremely challenging business environment. The Telecommunication industry is in possession of large quantities of data, generated from numerous operational systems, and is confronted with many business problems that need urgent handling. Naturally, it has been among the first to adopt Business Intelligence (BI) and Data Mining technologies. The main purpose of this paper is to present a literature review related to BI and Data Mining in Telecommunications, from business perspective - defining the main areas of BI and Data Mining applications, and from research perspective - identifying the most common Data Mining techniques and methods used

An intelligent alarm management system for large-scale telecommunication companies

This paper introduces an intelligent system that performs alarm correlation and root cause analysis. The system is designed to operate in large- scale heterogeneous networks from telecommunications operators. The pro- posed architecture includes a rules management module that is based in data mining (to generate the rules) and reinforcement learning (to improve rule se- lection) algorithms. In this work, we focus on the design and development of the rule generation part and test it using a large real-world dataset containing alarms from a Portuguese telecommunications company. The correlation engine achieved promising results, measured by a compression rate of 70% and as- sessed in real-time by experienced network administrator staff

Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

Application of Data Mining in Telecommunication Industry

Data Mining is a logical procedure intended to investigate data (normally a lot of data - commonly business or market related - otherwise called "enormous data") looking for predictable examples as well as methodical connections amongst factors, and after that to approve the discoveries by applying the recognized examples to new subsets of data. The telecommunications industry inside the division of data and correspondence technology is comprised of all Telecommunications/telephone companies and web access suppliers and assumes the urgent part in the development of versatile interchanges and the data society. Customary telephone calls keep on being the industry's greatest income generator, yet because of advances in arrange technology, Telecom today is less about voice and progressively about content (informing, email) and pictures (e.g. video gushing). Fast web access for PC based data applications, for example, broadband data administrations and intelligent stimulation , is unavoidable. Digital Subscriber Line (DSL) is the primary broadband telecom technology. The quickest development originates from (esteem included) administrations conveyed over portable systems

Largest Mergers and Acquisitions by Corporations, 2006

[Excerpt] Mergers and acquisitions (M&A) activity has continued to increase, especially in each of the past three years. This report provides a listing of the largest M&A transactions worldwide (value equivalent to $2 billion or more) that were proposed during 2006. The report includes the dates on which transactions were completed and shows M&A deals that were still pending as of December 31, 2006, or that were not successful. These data have been drawn from publicly available sources and have not been otherwise verified by the Congressional Research Service. This report will not be updated

Mining Target-Oriented Fuzzy Correlation Rules to Optimize Telecom Service Management

To optimize telecom service management, it is necessary that information about telecom services is highly related to the most popular telecom service. To this end, we propose an algorithm for mining target-oriented fuzzy correlation rules. In this paper, we show that by using the fuzzy statistics analysis and the data mining technology, the target-oriented fuzzy correlation rules can be obtained from a given database. We conduct an experiment by using a sample database from a telecom service provider in Taiwan. Our work can be used to assist the telecom service provider in providing the appropriate services to the customers for better customer relationship management.Comment: 10 pages, 7 table