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

Who Renews? Who Leaves? Identifying Customer Churn in a Telecom Company Using Big Data Techniques

Within the context of the telecom industry, this teaching case is an active learning analytics exercise to help students build hands-on expertise on how to utilize Big Data to solve a business problem. Particularly, the case utilizes an analytics method to help develop a customer retention strategy to mitigate against an increasing customer churn problem in a telecom company. Traditionally, the forecast of customer churn uses various demographic and cell phone usage data. Big Data techniques permit a much finer granularity in the prediction of churn by analyzing specific activities a customer undertakes before churning. The authors help students to understand how data from customer interactions with the company through multiple channels can be combined to create a “session.” Subsequently, the authors demonstrate the use of effective visualization to identify the most relevant paths to customer churn. The Teradata Aster Big Data platform is used in developing this case study

Measuring churner influence on pre-paid subscribers using fuzzy logic

In the last decades, mobile phones have become the major medium for communication between humans. The site effect is the loss of subscribers. Consequently, Telecoms operators invest in developing algorithms for quantifying the risk to churn and to influence other subscribers to churn. The objective is to prioritize the retention of subscribers in their network due to the cost of obtaining a new subscriber is four times more expensive than retaining subscribers. Hence, we use Extremely Random Forest to classify churners and non-churners obtaining a Lift value at 10% of 5.5. Then, we rely on graph-based measures such as Degree of Centrality and Page rank to measure emitted and received influence in the social network of the carrier. Our methodology allows summarising churn risk score, relying on a Fuzzy Logic system, combining the churn probability and the risk of the churner to leave the network with other subscriber

Utjecaj društvene mreže na odljev korisnika u mobilnim mrežama

As the telecommunications sector has reached its mature stage, maintaining existing users has become crucial for service providers. Analyzing the call data records, it is possible to observe their users in the context of social network and obtain additional insights about the spread of influence among interconnected users, which is relevant to churn. In this paper, we examine the communication patterns of mobile phone users and subscription plan logs. Our goal is to use a simple model to predict which users are most likely to churn, solely by observing each user\u27s social network, which is formed by outgoing calls, and churn among their neighbors. To measure the importance of social network parameters with regard to churn prediction, we compare three models: spatial classification, regression model, and artificial neural networks. For each subscriber, we observe three social network parameters, the number of neighbors that have churned, the number of calls to these neighbors, and the duration of these calls for different time periods. The results indicate that using only one or two of these parameters yields results that are comparable or better than the complex models with large amounts of individual and/or social network input parameters that other researchers have proposed.Kako je telekomunikacijski sektor dosegao zreli stadij, zadržavanje postojećih korisnika od ključne je važnosti za pružatelje telekomunikacijskih usluga. Analizom liste poziva moguće je nadzirati korisnike u kontekstu društvene mreže i dobiti dodatni uvid u širenje utjecaja među povezanim korisnicima, što je relevantno za odljev korisnika. U ovom radu razmatramo obrasce komunikacije korisnika mobilnih mreža i podatke o planu pretplate. Naš cilj je korištenjem jednostavnog modela predvidjeti koji korisnici su najskloniji prijelazu na drugu mrežu, pritom koristeći samo korisnikovu društvenu mrežu koja se formira odlaznim pozivima i prijelazima između mreža njihovih susjeda. S ciljem mjerenja važnosti pojedinog parametra društvene mreže za predikciju prelaska na drugu mrežu uspoređena su tri modela: prostorna klasifikacija, regresijski model i model neuronske mreže. Za svakog pretplatnika razmatramo tri parametra društvene mreže: broj susjeda koji su promijenili mrežu, broj poziva prema njima kao i trajanje spomenutih poziva u različitim vremenskim razdobljima. Rezultati pokazuju kako se korištenjem samo jednog ili dva od navedenih parametara društvene mreže postižu rezultati koji su usporedivi ili bolji od rezultata složenijih modela drugih autora koji koriste veliki broj osobnih parametara i/ili parametara društvene mreže