System and method for maximizing operator profits by shaping cellular demand using network operational costs and price-demand function
Publication Date: 2012-Dec-11
The IP.com Prior Art Database
This article describes a method for pricing cellular network usage in a way that maximizes operator profits. The method uses network operational costs and price-demand function to shape the cellular demand so as to operate at this aforesaid optimal profit point,
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System and method for maximizing operator profits by shaping cellular demand using network operational costs and price -demand function
The telecom revolution of the 2000s has led call pricing to become very competitive with operators slashing tariffs and engaging in price wars. Customers have a greater choice of operators and often, the tariff affects the decision of choosing/switching operators, esp amongst price-sensitive customers in developing countries like India. On the other hand, operators incur huge capital and operating costs in building and running the telecom network.
Energy costs are among the largest operating expenses for telecom network operators, esp in developing countries which lack reliable grid electricity supply. Because of the absence of a reliable grid, tower operators have to build a parallel power backup infrastructure comprising of diesel generators, big batteries, and some times renewable sources like solar and wind. This power infrastructure contributes to a big chunk of the telco's capex and opex . For example, India has more than 400,000 telecom towers and presently 40% power requirements are met by grid electricity and 60% by diesel generators. The total diesel consumption is ~ 2 billion liters/annum and tower companies pass on these high energy costs to operators.
This dual-trend of large costs and falling Average Revenue Per User (ARPU) puts operators under tremendous profitability pressures. We propose a variable pricing method which exploits the price elasticity of demand and will let operators choose the optimal operating point for maximizing their "net profit" (revenues - operating cost) while offering customers attractive discounts. This method, shown in Fig 1 below, uses historical cellular usage data to learn the price-demand curve for each class of offered service, this indicates how price affects demand. Further, It uses the operational & energy costs to determine the Demand-Opex curve which indicates how the demand affects the operational expenses. Using the above inputs, the invention determin...