Predicting customer's gender and age depending on mobile phone data

In 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

Are cost models useful for telecoms regulators in developing countries?

Worldwide privatization of the telecommunications industry, and the introduction of competition in the sector, together with the ever-increasing rate of technological advance in telecommunications, raise new and critical challenges for regulation. Fo matters of pricing, universal service obligations, and the like, one question to be answered is this: What is the efficient cost of providing the service to a certain area or type of customer? As developing countries build up their capacity to regulate their privatized infrastructure monopolies, cost models are likely to prove increasingly important in answering this question. Cost models deliver a number of benefits to a regulator willing to apply them, but they also ask for something in advance: information. Without information, the question cannot be answered. The authors introduce cost models and establish their applicability when different degrees of information are available to the regulator. They do no by running a cost model with different sets of actual data form Argentina's second largest city, and comparing results. Reliable, detailed information is generally scarce in developing countries. The authors establish the minimum information requirements for a regulator implementing a cost proxy model approach, showing that this data constraint need not be that binding.ICT Policy and Strategies,Decentralization,Environmental Economics&Policies,Economic Theory&Research,Business Environment,ICT Policy and Strategies,Environmental Economics&Policies,Geographical Information Systems,Economic Theory&Research,Educational Technology and Distance Education

Mobile operators as banks or vice-versa? and: the challenges of Mobile channels for banks

This short paper addresses the strategic challenges of deposit banks, and payment clearinghouses, posed by the growing role of mobile operators as collectors and payment agents of flow of cash for themselves and third parties. Through analysis and data analysis from selected operators , it is shown that mobile operators achieve as money flow handlers levels of efficiency , profitability ,and risk control comparable with deposit banks – Furthermore , the payment infrastructures deployed by both are found to be quite similar , and are analyzed in relation to strategic challenges and opportunities This paves the way to either mobile operators taking a bigger role ,or for banks to tie up such operators to them even more tightly ,or for alliances/mergers to take place ,all these options being subject to regulatory evolution as analyzed as well . The reader should acknowledge that there is no emphasis on specific Mobile banking (M-Banking) technologies (security, terminals, application software) , nor on related market forces from the user demand point of view.banking;industry structure;mobile networks;operational cash flow;regulations;transaction systems

Techno-economic viability of integrating satellite communication in 4G networks to bridge the broadband digital divide

Bridging the broadband digital divide between urban and rural areas in Europe is one of the main targets of the Digital Agenda for Europe. Though many technological options are proposed in literature, satellite communication has been identified as the only possible solution for the most rural areas, due to its global coverage. However, deploying an end-to-end satellite solution might, in some cases, not be cost-effective. The aim of this study is to give insights into the economic effectiveness of integrating satellite communications into 4G networks in order to connect the most rural areas (also referred to as white areas) in Europe. To this end, this paper proposes a converged solution that combines satellite communication as a backhaul network with 4G as a fronthaul network to bring enhanced broadband connectivity to European rural areas, along with a techno-economic model to analyse the economic viability of this integration. The model is based on a Total Cost of Ownership (TCO) model for 5 years, taking into account both capital and operational expenditures, and aims to calculate the TCO as well as the Average Cost Per User (ACPU) for the studied scenarios. We evaluate the suggested model by simulating a hypothetical use case for two scenarios. The first scenario is based on a radio access network connecting to the 4G core network via a satellite link. Results for this scenario show high operational costs. In order to reduce these costs, we propose a second scenario, consisting of caching the popular content on the edge to reduce the traffic carried over the satellite link. This scenario demonstrates a significant operational cost decrease (more than 60%), which also means a significant ACPU decrease. We evaluate the robustness of the results by simulating for a range of population densities, hereby also providing an indication of the economic viability of our proposed solution across a wider range of areas

Dynamic assessment of exposure to air pollution using mobile phone data

Background: Exposure to air pollution can have major health impacts, such as respiratory and cardiovascular diseases. Traditionally, only the air pollution concentration at the home location is taken into account in health impact assessments and epidemiological studies. Neglecting individual travel patterns can lead to a bias in air pollution exposure assessments. Methods: In this work, we present a novel approach to calculate the daily exposure to air pollution using mobile phone data of approximately 5 million mobile phone users living in Belgium. At present, this data is collected and stored by telecom operators mainly for management of the mobile network. Yet it represents a major source of information in the study of human mobility. We calculate the exposure to NO2 using two approaches: assuming people stay at home the entire day (traditional static approach), and incorporating individual travel patterns using their location inferred from their use of the mobile phone network (dynamic approach). Results: The mean exposure to NO2 increases with 1.27 mu g/m(3) (4.3 %) during the week and with 0.12 mu g/m(3) (0.4 %) during the weekend when incorporating individual travel patterns. During the week, mostly people living in municipalities surrounding larger cities experience the highest increase in NO2 exposure when incorporating their travel patterns, probably because most of them work in these larger cities with higher NO2 concentrations. Conclusions: It is relevant for health impact assessments and epidemiological studies to incorporate individual travel patterns in estimating air pollution exposure. Mobile phone data is a promising data source to determine individual travel patterns, because of the advantages (e.g. low costs, large sample size, passive data collection) compared to travel surveys, GPS, and smartphone data (i.e. data captured by applications on smartphones)

Can a synergetic cooperation between telecom and utility network providers lead to a faster rollout of fiber to the home networks?

C

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin