Development and performance assessment of adaptive nonlinear models for revenue prediction of a mobile network operator.

The commoditization of voice, saturation of the urban market, fierce competition, and the increased cost of the spectrum have forced the mobile telecom network operators to promote and garner revenue from non-voice services (NVS) namely value-added services (VAS) and data services (DS). It is a fact that monthly revenue from different segments of a mobile telecom service provider is non-linearly related to its previous revenue. Hence, existing linear prediction models such as regression and linear combiner do not exhibit accurate prediction performance. This article proposes one linear adaptive linear combiner (ALC) and three nonlinear (trigonometric expansion based neural network (TENN), multi-layer perceptron (MLP) and radial basis function (RBF)) models for prediction of revenue from voice services (VS), DS and VAS segments. The real-life revenue data of a mobile telecom service provider in a licensing area of India is utilized for this study. The predictor performance has been obtained from the simulation study of the models and analyzed. It is observed that the prediction performance of linear ALC model is the worst. The TENN model outperforms the MLP and RBF models from amongst the proposed nonlinear models based on the available historical data. In essence, the study demonstrates that the overall ranking of the four models based on prediction performance are TENN, RBF, MLP, and ALC. [ABSTRACT FROM AUTHOR]