Customer churn prediction in telecom using machine learning and social network analysis in big data platform

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.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK

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

Limit order book as a market for liquidity

We develop a dynamic model of an order-driven market populated by discretionary liquidity traders. These traders must trade, yet can choose the type of order and are fully strategic in their decision. Traders differ by their impatience: less patient traders demand liquidity, more patient traders provide it. Three equilibrium types are obtained - the type is determined by three parameters: the degree of impatience of the patient traders, which we interpret as the cost of execution delay in providing liquidity; their proportion in the population, which is the cost of the minimal price improvement. Despite its simplicity, the model generates a rich set empirical predictions on the relation between market parameters, time to execution, and spreads. We argue that the economic intuition of this model is robust, thus its main results will remain in more general models.limit and market orders; time-to-execution; market quality

Liquidity cycles and make/take fees in electronic markets

In this paper, the authors develop a dynamic model of trading with two specialized sides: traders posting quotes (“market makers”) and traders hitting quotes (“market takers”). Traders monitor the market to seize profit opportunities, generating high frequency make/take liquidity cycles. Monitoring decisions by market-makers and market-takers are self-reinforcing, generating multiple equilibria with differing liquidity levels and duration clustering. The trading rate is typically maximized when makers and takers are charged different fees or even paid rebates, as observed in reality. The model yields several empirical implications regarding the determinants of make/take fees, the trading rate, the bid-ask spread, and the effect of algorithmic trading on these variables. Finally, algorithmic trading can improve welfare because it increases the rate at which gains from trade are realized.liquidity; monitoring; make/take fees; duration clustering; algorithmic trading; two-sided markets

The diminishing liquidity premium

Previous evidence suggests that less liquid stocks entail higher average returns. Using NYSE data, we present evidence that both the sensitivity of returns to liquidity and liquidity premia have significantly declined over the past four decades to levels that we cannot statistically distinguish from zero. Furthermore, the profitability of trading strategies based on buying illiquid stocks and selling illiquid stocks has declined over the past four decades, rendering such strategies virtually unprofitable. Our results are robust to several conventional liquidity measures related to volume. When using liquidity measure that is not related to volume, we find just weak evidence of a liquidity premium even in the early periods of our sample. The gradual introduction and proliferation of index funds and exchange traded funds is a possible explanation for these results

The Formulation of the Blue Homeland Doctrine

The Blue Homeland Doctrine expresses Turkey's legitimate maritime rights within the framework of international law. Efforts to protect these rights make an important contribution to global and regional peace in terms of both implementing international law and the possibility of regional countries benefiting from all resources equitably. Contrary to allegations made by the detractors of the Blue Homeland Doctrine, it seems that not only Turkey but also all countries in the region can reap immense gains from the full implementation of this doctrine. Moreover, this doctrine is far from promoting an "expansionist" policy, especially considering how the Blue Homeland Doctrine anticipates the creation of cooperation mechanisms with riparian states in the Eastern Mediterranean. The implementation of this doctrine will greatly contribute to the development of international trade and the more efficient use of energy resources. Regarding those countries whose attitude is still inspired by "maritime piracy," one should take into account the fact that Turkey possesses a superior naval fleet to protect its rights and up-to-date military-industrial infrastructure

Equilibrium in the Two Player, k-Double Auction with Affiliate Private Values

We prove the existence of an increasing equilibrium, and study the comparative statics of correlation in the k-double auction with affiliated private values. This is supposedly the simplest bilateral trading mechanism that allows for dependence in valuations between buyers and sellers. In the case k ?{0 ,1} there exists a unique equilibrium in non-dominated strategies. Using this equilibrium we show that correlation has a dual effect on strategic bidding. It might impose bidders to become more or less aggressive depending on their private valuation, and on the level of correlation. In the case k ? (0 ,1), we prove the existence of a family of strictly increasing equilibria, and demonstrate them using examples. Moreover, we show that equilibria in the case of independent private values are pointwise limits of equilibria with strictly affiliated private values

Security Management of Intelligent Technologies in Business Intelligence Systems

The article discusses the security methods of intelligent technologies in Business Intelligence (BI) systems. Security technologies are considered taking into account BI four-layer architecture which includes: а) transactional systems layer; b) ETL-procedures – extraction, conversions and data loading layer; c) data warehouses and data marts layer; d) OLAP-tools and user interface layer. The characteristic of the general BI systems security technologies, data storage security strategies and intellectual data mining subsystems and OLAP-tools is resulted. For data mining models and to provide them with the analyst, considered the requirements of access rights to the analyzed information, database backups creation necessity, the requirements to hide sensitive data

Performance Evaluation with High Moments and Disaster Risk

Traditional performance evaluation measures do not account for tail events and rare disasters. To address this issue, we reinterpret the riskiness measures of Aumann and Serrano (2008) and Foster and Hart (2009) as performance indices. We derive the moment properties of these indices and their sensitivity to rare disasters and show that they are consistent with the asset pricing literature. As applications, we show that “anomalous” investment strategies such as “momentum” or investment in private equity lose much of their glamour when accounting for high moments and rare events. Furthermore, using the indices to select mutual funds results in desirable high-moment properties out of sample