Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

Customer-oriented risk assessment in Network Utilities

For companies that distribute services such as telecommunications, water, energy, gas, etc., quality perceived by the customers has a strong impact on the fulfillment of financial goals, positively increasing the demand and negatively increasing the risk of customer churn (loss of customers). Failures by these companies may cause customer affection in a massive way, augmenting the intention to leave the company. Therefore, maintenance performance and specifically service reliability has a strong influence on financial goals. This paper proposes a methodology to evaluate the contribution of the maintenance department in economic terms, based on service unreliability by network failures. The developed methodology aims to provide an analysis of failures to facilitate decision making about maintenance (preventive/predictive and corrective) costs versus negative impacts in end-customer invoicing based on the probability of losing customers. Survival analysis of recurrent failures with the General Renewal Process distribution is used for this novel purpose with the intention to be applied as a standard procedure to calculate the expected maintenance financial impact, for a given period of time. Also, geographical areas of coverage are distinguished, enabling the comparison of different technical or management alternatives. Two case studies in a telecommunications services company are presented in order to illustrate the applicability of the methodology

Analysis of the Effectiveness of Tariffs for Telecommunications Services with Broadband Access

The purpose of the study was to analyze the efficiency of tariffs of companies providing telecommunications services with broadband access. The analysis was carried out with respect to both the efficiency of the tariff system in general and its individual elements. The structure of costs for services providing access to the Internet and networks of digital television has been studied. The scheme of the analysis of the product and cost parts of the tariff system of telecommunication companies is proposed. The advantages and disadvantages of pricing strategies for telecommunication services are discussed

Telecommunications 2000: Strategy, HR Practices and Performance

This report constitutes the first benchmarking survey of business and human resource practices among a nationally representative sample of workplaces in the broadly defined telecommunications industry that includes wireline, wireless, cable, and internet providers. It grows out of a multi-year study of organizational change in the industry, and is based on extensive field study, site visits, interviews, and surveys conducted by research teams at Cornell and Rutgers Universities. Managers at 577 establishments across the country gave generously of their time during a lengthy telephone survey. The study was made possible through a generous grant by the Alfred P. Sloan Foundation

Telecommunications 2000 Strategy, HR Practices & Performance

This report constitutes the first benchmarking survey of business and human resource practices among a nationally representative sample of workplaces in the broadly defined telecommunications industry that includes wireline, wireless, cable, and internet providers. It grows out of a multi-year study of organizational change in the industry, and is based on extensive field study, site visits, interviews, and surveys conducted by research teams at Cornell and Rutgers Universities. Managers at 577 establishments across the country gave generously of their time during a lengthy telephone survey. The study was made possible through a generous grant by the Alfred P. Sloan Foundation. While this report is based on data collected among workplaces in the U.S., it has implications for the restructuring of the global telecommunications industry. In other research, we have found that the United States has been at the forefront of market deregulation and technology change, but many other countries have followed a similar path and look to the United States as a model for organizational restructuring (Katz 1997). Thus, at least some of the patterns we find here are likely to occur in other countries undergoing similar patterns of deregulation

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