Telecom Subscribers Research Articles

ICT Sector Performance Review for India

Telecommunication reforms in India saw very little privatization and much more of market liberalization accompanied with the introduction of new laws and regulations. Regulatory agencies and regulation have become integral components of the telecom reform process, in order to protect consumers, reassure investors and, in theory, help advance competition. In a country like India the role of the regulator is much beyond regulation of segments that are potentially monopolistic but its performance should also be measured in terms of its ability to foster competition. Studies have shown a close relationship between the nature of a regulatory regime and the investment behavior of the firms subject to that regime. Changes in regulation have often been followed by changes in investment behavior (Graeme Guthrie, 2006).The results of liberalization have been impressive. Teledensity has increased from merely 2 percent or so in 1999 to around 61 percent in 2010 and almost 8-10 million mobile subscribers are added every month. Wireless has been the principal engine for telecom growth in the country. The wireless subscriber base has grown from 0.88 million in1999 to 687.71 million in 2010-11. Given that the mobile sector grew at the Compound Annual Growth Rate (CAGR) of 84 .01 percent in the last decade, the telecom subscriber base has already outstripped what was envisaged for the 11th Plan ending in 2102.

Determinants of Customers Churn in Emerging Telecom Markets: A Study Of Indian Cellular Subscribers

Abstract Marketing is said to be a zero sum game i.e. each gain of customer for a firm is always at the expense of some other firm's customers. Therefore in a marketplace churn is a natural occurrence. Churn in Indian telecom market is among the highest in growing telecom markets. By using binary logistics regression analysis based models based covering a sample of 822 Indian telecom subscribers; this paper attempts to examine the determinants of churn. The future churn is found to be dependent on satisfaction level of the customer with the service provider, attitude and loyalty of the customer variables, intended churn (i.e. intention to churn) and current loyalty (defined as intention to recommend) and distraction (i.e. intention to experiment).

A novel evolutionary data mining algorithm with applications to churn prediction

Classification is an important topic in data mining research. Given a set of data records, each of which belongs to one of a number of predefined classes, the classification problem is concerned with the discovery of classification rules that can allow records with unknown class membership to be correctly classified. Many algorithms have been developed to mine large data sets for classification models and they have been shown to be very effective. However, when it comes to determining the likelihood of each classification made, many of them are not designed with such purpose in mind. For this, they are not readily applicable to such problems as churn prediction. For such an application, the goal is not only to predict whether or not a subscriber would switch from one carrier to another, it is also important that the likelihood of the subscriber's doing so be predicted. The reason for this is that a carrier can then choose to provide a special personalized offer and services to those subscribers who are predicted with higher likelihood to churn. Given its importance, we propose a new data mining algorithm, called data mining by evolutionary learning (DMEL), to handle classification problems of which the accuracy of each predictions made has to be estimated. In performing its tasks, DMEL searches through the possible rule space using an evolutionary approach that has the following characteristics: 1) the evolutionary process begins with the generation of an initial set of first-order rules (i.e., rules with one conjunct/condition) using a probabilistic induction technique and based on these rules, rules of higher order (two or more conjuncts) are obtained iteratively; 2) when identifying interesting rules, an objective interestingness measure is used; 3) the fitness of a chromosome is defined in terms of the probability that the attribute values of a record can be correctly determined using the rules it encodes; and 4) the likelihood of predictions (or classifications) made are estimated so that subscribers can be ranked according to their likelihood to churn. Experiments with different data sets showed that DMEL is able to effectively discover interesting classification rules. In particular, it is able to predict churn accurately under different churn rates when applied to real telecom subscriber data.