The Objective of this thesis is to address some interesting problems in the Indian cellular services market. The first problem we address relates to identifying important change points that marked the evolution of the telecom market since Indian Independence. We use the data on per-capita availability of telephones in India to this effect. We identify important change points that mapped to the computerization move in 1989, the liberalization and globalization policies starting from 1991 and subsequently the introduction of NTP 1997 and NTP 1999. We also identify the important change points that mark the growth of cellular services subscriber base in India. We map change points detected to some of the important macro level policy initiatives that were taken by TRAI. The second problem we address is the assessment of policy interventions on the growth of cellular subscriber base in India. We model the impact of two important policy interventions namely, the NTP 1999 and its spill-over policy the entry of the fourth player into the market to offer services. We model the abrupt temporary, abrupt permanent and gradual permanent impacts of these interventions individually and in a coupled manner. We are arguably the first to use the intervention analysis and change point analysis to study the Indian telecom market. The third problem relates to the most challenging task of forecasting the growth of cellular services subscribers in India. We use novel machine learning techniques like ε-SVR and ν-SVR and compare its performance with ANN and ARIMA using standard performance metrics. Initially, we venture to predict the aggregate subscriber growth of cellular mobile subscribers in India using the SVR techniques. This would be of interest to the policy makers from a strategic standpoint. Subsequently, we predict the marginal(monthly) subscriber growth using SVR and tabulate the results for varying depths of forecasting which would be of interest to service providers form an operation standpoint. We find that the SVR techniques performed better than ANN and ARIMA particularly with respect to forward or out-sample forecasting when the time periods increase. The final problem involves a differential game model in an oligopoly set up for the telecom service providers who tried to optimize their advertisement innovation mix in order to maximize their discounted flow of profits. We consider the situation where the service providers make Cournot conjectures about the action of their rivals. The firms would not enter into agreements or form cartels. The firms choose the quantity they want to sell simultaneously. The essence of the Cournot conjecture was that though it was a quantity based competition, no single firm could unilaterally try to improve the total quantity sold in the market. Every firm made only one decision and did so when other firms were simultaneously making decisions. We have come across papers that considered either advertisement or product/process innovation separately but not together. We incorporate both these control variables with the inverse demand function as the state variable. We propose an open-loop solution that is dependent on time. We conduct experiments with various combinations of churn and spill-over rates of advertisement and innovation and thereby get some managerial insights.