An Analysis of Pricing Telecommunications Network Services with Data Mining Methods

Jongsawas Chongwatpol
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Jongsawas Chongwatpol: NIDA Business School, National Institute of Development Administration, Thailand

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Abstract: Research on developing pricing mechanisms for telecommunications service providers has been going on for decades. Many agencies have adopted various pricing schemes to charge their subscribers. However, due to the changes in the economic environment and technological infrastructure, the loss of subscribers is one of the important issues nowadays and these agencies need to adjust their pricing mechanisms to improve retention, to recover the cost of operations, and to maximize profitability. Practically, current pricing mechanisms do not reflect the changes in subscriber behaviors. This study seeks to fill this gap and examines how data mining techniques can help in making telecommunications pricing decisions. Consequently, any telecommunications service providers can evaluate their pricing strategy with respect to the organizational objectives and subscriber satisfaction perspectives. An in-depth study of a state telecommunications service agency, OneNet - a division of the Oklahoma State Regents for Higher Education, is conducted. OneNet operates as an enterprise-type fund that provide cost-effective, equalized access to advanced network and telecommunications services to educational, governmental, and health care entities. OneNet must recover their costs through billing their subscribers and by justifying appropriations directly from the state legislatures. Our experiments are based on a data base of 5,000 U.S. domestic subscribers. Many data mining techniques such as stepwise regression model, decision tree, and artificial neural network (ANN) are used to analyze data sets with multiple predictor variables, which include both network and non-network related factors. Our preliminary results show that types of circuits, membership fees, maintenance and repair costs of network-related equipment, and hub locations are the key factors that categorize OneNet’s subscribers into four groups. Pricing mechanisms for each group are developed separately based on the identified key factor characteristics. Although we present this research in the context of OneNet, it is equally applicable to other providers of telecommunications services.

Keywords: Data Mining; Pricing; Telecommunications; Neural Network; Decision tree (search for similar items in EconPapers)
Date: 2013
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