IntroductionThe telecommunications sector has become one of the main industries in developed countries. The technical progress and the increasing number of operators raised the level of competition [1]. Companies are working hard to survive in this competitive market depending on multiple strategies. Three main strategies have been proposed to generate more revenues [2]: (1) acquire new customers, (2) upsell the existing customers, and (3) increase the retention period of customers. However, comparing these strategies taking the value of return on investment (RoI) of each into account has shown that the third strategy is the most profitable strategy [2], proves that retaining an existing customer costs much lower than acquiring a new one [3], in addition to being considered much easier than the upselling strategy [4]. To apply the Abstract Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3%. Another main contribution is to use customer social network in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against AUC standard. The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company. The dataset contained all customers' information over 9 months, and was used to train, test, and evaluate the system at SyriaTel. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM" and Extreme Gradient Boosting "XGBOOST". However, the best results were obtained by applying XGBOOST algorithm. This algorithm was used for classification in this churn predictive model.
Due to the increased competition between telecommunication operators and growing customers' churn rate, telecommunication companies were seeking to improve customer loyalty. In order to increase customer satisfaction, most telecom companies resort to customer segmentation which entails separating the targeted customers into different groups based on demographics or usage perspective including gender, age-group, buying behavior, usage pattern, special interests and other features that represent the customer. Customer segmentation adopted by most telecom operators to provide the customer with the right offer.
IntroductionNowadays, the mobile phone is one of the fastest growing technologies in the developing world with global penetration rates reaching 90% [1]. This makes it a huge warehouse for customer's data. That is, every action taken by the customer (short message service (SMS), Call or Internet session) gets recorded within the telecom operator, in the so called (CDRs). There are many types of CDRs used mainly by telecom billing systems. CDR contains a lot of information, (type of event, who is involved in this event, datetime, cell identifier where this event has taken place). This raw data represents a valuable source for analyzing human and social behavior [2]. In the agricultural domain [3] mobile phone data is used to analyze mobility and seasonal activity patterns related to livelihood zones in Senegal, by creating mobility profiles for population and segmentation. While in energy domain [4] this data is used to analyze human activity, facilitate population growth estimation in rural areas and extrapolate electricity needs. In health sector [5,6] mobile phone data is used to study the relation between human mobility AbstractIn the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
International audienceWith the increasing growth in popularity of Web services, discovering relevant Web services becomes a significant challenge. The introduction of intentional services has been proposed to bridge the gap between low level, technical softwareservice descriptions and high level, strategic expressions of business needs for services. Current Web Services technology based on UDDI and WSDL does not make use of this "intention" and therefore fails to address the problem of matching between capabilities of services and business user needs. In this work, we are interested in extending existing approaches for the description of intentional Services. Our proposed approach is an extension of the W3C recommendation on semantics for Web services (SAWSDL) and uses two types of ontologies: verb ontology representing syntactic and semantic concepts related to verbs, and product ontology, which is a Domain Ontology containing the concepts defining a common vocabulary for all objects manipulated in the business domain. We present how our approach can help publishing intentional services
Goal modeling is a prominent design paradigm in various domains of information systems engineering. In the field of service oriented computing (SOC) and in the field of semantic web services, emergent research works are basing their engineering approach on the goal concept. Because of the complexity of processes underlying SOC, the usage of the goal concept can vary to a large extent. In this paper, we study approaches to service oriented engineering and propose a framework to analyze and better understand how the goal concept is used in web service discovery. The framework is inspired by the four world vision of information systems engineering (i.e. subject, system, development and usage). Using this framework, we review eight prominent research works in SOE. Through this analysis, we seek to better understand the link between semantic web technologies and the goal concept, and what are the challenging issues in terms of goal usage in service discovery.
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