The Role of Peer Influence in Churn in Wireless Networks

Subscriber churn remains a top challenge for wireless carriers. These carriers need to understand the determinants of churn to confidently apply effective retention strategies to ensure their profitability and growth. In this paper, we look at the effect of peer influence on churn and we try to disentangle it from other effects that drive simultaneous churn across friends but that do not relate to peer influence. We analyze a random sample of roughly 10 thousand subscribers from large dataset from a major wireless carrier over a period of 10 months. We apply survival models and generalized propensity score to identify the role of peer influence. We show that the propensity to churn increases when friends do and that it increases more when many strong friends churn. Therefore, our results suggest that churn managers should consider strategies aimed at preventing group churn. We also show that survival models fail to disentangle homophily from peer influence over-estimating the effect of peer influence.Comment: Accepted in Seventh ASE International Conference on Social Computing (Socialcom 2014), Best Paper Award Winne

Process Framework for Subscriber Management and Retention in Nigerian Telecommunication Industry

in the global telecommunication industry. Hence, a dominant approach for subscriber management and retention is churn control, since it is cheaper to retain an existing subscriber than acquiring a new one. Predictive modeling employs the use of data mining techniques to identify patterns and provide a result that a group of subscribers are likely to churn in the near future. However, the effectiveness of subscriber retention strategy in an organization can be further boosted if the reason for churn and the timing of churn can also be predicted. In this paper, we propose a data mining process framework that can be used to predict churn, determine when a subscriber is likely to churn, provides the reason why a subscriber may churn, and recommend appropriate intervention strategy for customer retention using a combination of statistical and machine learning techniques. This experiment is carried out using data from a major telecom operator in Nigeria

Implications of Mandatory Registration of Mobile Phone Users in Africa

Sub-Saharan Africa ranks among the top regions in terms of growth in the number of mobile phone users. The success of mobile telephony is attributed to the opening of markets for private players and lenient regulatory policy. However, markets may be increasingly saturated and new regulations introduced across Africa could also have a negative impact on future growth. Since 2006, the majority of countries in the region have introduced mandatory registration of users of prepaid SIM cards with their personal identity details. This potentially increases the costs of using mobile telephony. I present a fixed effects model for the estimation of the impact of mandatory registration on mobile penetration growth, which is based upon a panel dataset of 32 countries in Sub-Saharan Africa for the years 2000 to 2010. The results show that the introduction of mandatory registration depresses growth in mobile penetration.Telecommunication, government policy, consumer protection, privacy

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

Telecomm Subscriber Management System(TSMS)

In the current fast growing rate of mobile phone users, Telecomm Subscriber Management System (TSMS) leads to a good management of subscribers’ number by indicating the most current status of a subscriber number in telecommunication world. It helps to improve the management of subscriber numbers efficiently and also it helps to increase the revenue generated event (RGE) by the subscriber where lies the goals and objectives of any mobile telecommunication operators. This work is an automation of manual processes whereby we validate data first to ensure that there is no redundant data then we proceed to the subscriber classification. In Parallel there are several concerns that this project will provide an efficient improvement of subscriber number management that helps the telecommunication company to make significant profit in order to ensure its success in the market. There are problems are : inefficient subscriber’s number management which is leading to difficulties in identifying subscriber number’s status as well difficulties to clarify the usage of the block that have owned the company. There are several research papers that have been discussed about this. This work is a significant enhancement of resealed methods procedures of the researches and studies done previously. The implementation of this system will help the company in keeping the database updated, will reduce missing data and data redundancy as well as data inconsistency and unreliable data in the database as well as it has great advantages in their profit margin improvement

Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models

Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models

Not just the best years of my life: personal growth in higher education

Our conception of product affirmation depicts a product as “sculptor” of the consumer’s ideal self, similar to how a relationship partner can help us achieve our aspirations and goals. We performed two studies to look at the role of higher education as a product in affirming a consumer’s ideal self. We found that product affirmation for undergraduate students and alumni (with the university as the product that affirms the ideal self of the student/alumnus) leads to increases in the experience of various positive emotions, the acquisition of various positive traits, and positive evaluations of the university. Additionally, we found that product affirmation effects were more pronounced and robust in one’s personal ideal-self domain than in one’s professional ideal-self domain. Practical implications, study limitations, and future directions are discussed, as well as preliminary findings from a follow-up experiment using a sample of graduate students