Comparison of Telecommunication Service Providers' Pricing Strategies for Packet Based Services

Transcription

1 2008 Internatioal Symposium on Telecommunications Comparison of Telecommunication Service Providers' Pricing Strategies for Packet Based Services Seyed Mohammadreza Mirsarraf Strategic Studies Department Iran Telecommunication Research Center Tehran, Iran Ali Reza Mansoori Communication Technology Department Iran Telecommunication Research Center Tehran, Iran Abstract To remain competitive in the turbulent and rapidly evolving telecommunication market, service providers (SPs) should use proper pricing strategy to retain subscribers and even attract more subscribers. The SP s pricing strategy should modify the prices continually regarding to the market to maximize its revenue. In this paper two novel pricing strategies are introduced, one based on game theory and the other based on Infinite Impulse Response (IIR) filtering. A simple strategy entitled fixed price strategy which calculates the price according to the cost is also used and compared with the other two pricing strategies. To show effectiveness of the strategies a telecommunication market is modeled in which some SPs operate and each SP uses one of the three pricing strategies. Simulation results show that the game theory-based strategy is the most effective strategy among the three strategies. An analytical model to compute average key parameters of SP in equilibrium market is also reported in the paper. This model is used to check analytical and simulative coincidence. Keywords- Pricing; Pricing Strategy; Game Theory; IIR Filtering I. INTRODUCTION One of the main concerns of any telecommunication service provider (SP) is pricing its services. Pricing is very important because low price encourages more people to subscribe in the competition market but less profit from every subscriber and, conversely, high price discourage people to subscribe but more profit from every subscriber. Thus the main question in pricing is How to find the trade-off? SP s pricing strategy specifies the trade-off according to different parameters including market based parameters (e.g. competitors price and customer demands) and internal parameters (e.g. operational expenditure). In the competitors market those SPs remain successfully that have relatively more powerful pricing strategy. Some telecommunication services are charged as flat rate, a fixed charge for a fixed time (e.g. one month) without any constraint for usage. Some other charging models are time based, for example internet connection which is charged for a fixed hours regardless how much data transferred during connection time. Such sorts of charging models are relatively simpler than another set of charging models which are usage based. Ordinary voice call service is an example of usage based service which usually depends on call distance, duration and time of calling (day/night and holiday/working day). wadays SPs provide a variety of advanced packet based service types such as Internet connection, voice over IP, IPTV, mobile TV, video on demand, chatting, interactive gaming, etc. The fair charging for this sort of advanced services which have a good market around the world is usage-based. Some charging models for third generation packet based mobile services such as metered charging, packet charging, fixed price charging, flow based charging, class based charging, expected capacity charging, edge pricing, Paris-metro charging, and packet based reservation charging are introduced in [1]. Packet based charging is a kind of usage based charging model for packet based services. In this model, charge of any service usage is based on the volume of transferred packets. SPs usually offer different service levels in every service type. Packet transferring rate is a major parameter of service level and is of great importance for SP to survive in the competitive market. As the majority of new advanced telecommunication services are packet based, this model is focused in this research. From another aspect different business models are introduced in [2] for charging, billing and payments of packet based service providers including network operator centric, content aggregator centric, and content provider centric. The model focused in this research is a content operator centric in which SP performs as a content distributor and has direct connection with customers. Also SP may have some other suppliers and partners but this is transparent from customers point of view. In the real telecommunication world, cost reduction is a key activity for every SP. Some researches focus on different cost reduction ways, for example [3] describes a framework for capacity planning and costing in IP network environment, and in [4], some market driven parameters are discussed. Moreover, for estimating cost of delivering every service as a base for service pricing, it is important to know how the overall cost is shared among different services [5]. In addition to the cost of preparing and delivering service to customers, some other parameters influence revenue such as competitors price and customer satisfaction of delivered service [6]. Some pricing techniques are discussed in [7] such as cost based, bargaining games, pricing in practice, and flat rate. The /08/$ IEEE 30

2 pricing technique focused in this paper is a cost based pricing, regardless of brand, major supplier, etc. From another point of view pricing methods in network services are classified into two main categories including static pricing and dynamic pricing. In the static pricing method, network conditions such as quality parameters do not change, or if change do not affect on pricing, but in the dynamic pricing method, changes may occur and pricing regards them [8], [9], [10], [11]. In this research we focused on static pricing model for simplicity. In this research, in order to analyze competition in pricing strategies for providers of packet based services, a small market for packet service has been simulated and analyzed. Because of the complex nature for this analysis we restricted ourselves in packet based charging and the subject of other charging strategies like flow based and class based charging is open for further research. The main contribution of this paper is to propose two pricing strategies which are based on game theory and IIR Filtering concept. Some pricing methods based on game theory are reported in [12] and [13]. These proposed strategies are compared in companion with another simple fixed price strategy. Simulation results show that the strategy based on game theory is effective than the others. An analytical model to compute average key parameters of SP in equilibrium market is also reported in the paper. This model is used to check analytical and simulative coincidence. The remaining part of the paper is as follows. In section 2 the simulated market will be more described. A theoretical analysis of the simulated market will be reported in section 3. Three pricing strategies which have been considered in this research and applied in the simulated market will be described in the section 4. One of these strategies is a novel strategy and it will be shown that this strategy is better than the others focused here. This claim will be verified by simulation result and its comparison with theoretical analysis which will be mentioned in section 5. Finally the conclusion will be drawn in section 6. II. THE MARKET SIMULATION SCENARIO The specification of the simulated market in this research is described in this section. In this market similar to real world markets each SP has a fixed cost including content preparation cost, operation and maintenance cost, tax, etc. In addition it has a variable cost based on the amount of usage of the network resources to provide the services to customers. This is called also marginal cost and is proportional to the volume of packets interchanged between SP and customers terminals. In this simulated market there are some competitor SPs and a group of service consumers. Every service consumer subscribe at one of the SPs in the market. Any subscriber may change his SP due to changes in service prices by his SP and/or by the competitor SPs to buy the same service by less money. The tariff initially is set by SPs based on the variable cost and desired profit ratio but is modified with regard to competition situation in the market. The simulation program is set to run for a number of cycles. Each cycle is defined as a fixed time interval e.g. one month, one year, etc in which a complete market transaction is performed as described below. At the beginning of each cycle the new tariff for each SP is computed based on the SP s strategy and the subscribers are assigned service requirements as a volume of packets which satisfies their needs. Subscribers receive some money to buy service from one of the competing SPs. Furthermore at the beginning of each new cycle, two new SPs are created and given initial capital to compete with the current SPs remained from previous cycle in the market. Similarly at the end of each cycle all SPs are evaluated and those who lost their capital are withdrawn from the market. Every cycle consists of two iterations. During the first iteration every subscriber selects his SP. This SP is selected among the SPs with cheapest price randomly. Second iteration is buying process in which every subscriber buys the required packet volume from the selected SP. If the charge for the required packet volume is more than his money, he will spend all his money for getting packet volume. Otherwise he will buy the required packet volume and some money will be left for him. SDML (Strictly Declarative Modelling Language) is selected for simulation environment which is developed in Manchester Metropolitan University. III. THEORETICAL ANALYSIS OF THE SIMULATION MODEL In this section key average parameters analysis of the market is provided. Here it is assumed that none of the SPs is degenerated. This means that no SP keeps its price so low that it attracts all customers before it goes bankrupt. In the following subsections analytical approach for computation of "average number of SPs" and "average life time for SPs" are presented. A. Average Number of SPs Average number of SPs in the market depends on several parameters. To achieve the formula for average of SPs in the market we start with parameterizing total demand. The total demand of the market (d) is introduced in (1). m d = min(, r) N p In (1), m is the average money given to the customers per cycle, r is the average packet requirement of the subscribers, N is the number of subscribers which is assumed to be constant throughout the simulation, and p is the average price of unit volume of packet transfer. The min has appeared in the equation because any subscriber either has enough money to buy its required demand or his money is not enough and spends all his money to buy part of his demand. p, the average price of unit volume of packet transfer in (1) is obtained as: (1) 31

3 p C i= = C pisi 1 (2) i= 1 s i In this formula p i is the price of the unit volume of packet transfer for a subscriber at ith cycle and s i is the amount of packet consumption for that subscriber at that cycle. At each cycle every SP has a fixed expenditure, f, and a variable cost for each packet transfer unit, v. If we take O as the average number of SPs in equilibrium, the total SPs expenditure in C total number of cycles during simulation is equal to: Total _ Expenditure = C( Of + dυ) (3) In these C cycles SPs gain some money. When ith SP is generated, the initial capital K i is awarded to it (with the average of initial capital K). If O n is the number of SPs entering the market at each cycle, the total investment is O n K at each cycle. Meanwhile they sell d amount of packet on the average price of p for each packet unit. Therefore they earn dp at each cycle. The sum of these two items in C cycles for simulation is the total gain which is: Total _ Gain = C( On K + dp) (4) In the simulation model the life time of each SP is limited and there are limited numbers of SPs in the market at each cycle. This market is assumed to be a balanced market therefore total inserted money in C cycles is equal to amount of money withdrawn. In other word the total expenditure in C cycles is equal to the total gain. Thus (3) and (4) yield: C( Of + dυ ) = C( On K + dp) (5) From (5) the average number of SPs, O, is obtained: On K + d( p υ) O = (6) f In order to evaluate average number O we must obtain the values of d and p from (1) and (2) and replace them in (6). In the next subsection the average life time of SPs is calculated. B. Average Life Time for SPs Assume that an SP is generated at the ith cycle and becomes bankrupt at cycle i+l then L is the average life time of this SP. Fig. 1 shows the capital behavior of a typical SP. As this figure shows its capital at time i is equal to K and at the time i+l its capital is equal to zero or -ε. The average income of SP, I, at each cycle is: dp I = O (7) Capital of SP j K -ε i i+1 i+2 i+l Cycle Fig. 1 Capital Behaviour of a Typical SP In (7), dividing the total demand to average number of SPs in the market yields the average amount of packet sold by each SP and multiplying it to average price results the average income. The average expenditure of SPs, e, at each cycle consists of a variable cost and a fixed cost which can be written as: d e = υ + f (8) O Calculation of life time is based on this fact that the total incomes plus initial capital plus ε is equal to the total expenditure which can be written as: i+ L j= i e + K + ε = j i+ L j= i I j Substituting I and e from (7) and (8) respectively into (9) yields: Ldυ Ldp ( Lf + ) ( ) = K + ε (10) O O Therefore K + ε L = d( p υ) (11) f O In (11) ε is a small positive value which causes L to be an integral value. Thus (11) can be rewritten as: K L = d( p υ) f O (9) (12) IV. PRICING STRATEGIES FOR SIMULATION MODEL In this section three different pricing strategies for SPs which have been simulated and compared in this research are introduced. The three strategies comprised of fixed price, a game theory based Learning From Competitors (LFC), and IIR Filter. In the simulation runs identical rules have been used for subscribers to buy service from SP. Furthermore fixed cost, variable cost, and initial capital of SPs have been produced by 32

4 identical uniform random number generator. Thus the market situation was identical for every SP. Every SP uses the same method to calculate its initial service price when it enters the market. In this method for each SP an initial profit percent, ProfitPercent, is set randomly, which is a random number between zero and 0.2 percent. Thus initial price is calculated using variable cost (v) and ProfitPercent as: = (1+ProfitPercent)* v (13) In the next cycles each SP uses its pricing strategy to change the price. Three pricing strategies are used for SPs which are explained in this section. In the simulation program number of SPs using each pricing strategy is equal. A. Fixed Strategy In the fixed price strategy, SP computes its initial service price and does not change it during its life time. This is the simplest strategy and SP decides about price just based on the variable cost and expected profit and regardless of other SPs prices. B. Game Theory Based Learning From Competitors (LFC) The key point in LFC strategy is considering the competitor s price and profit in the previous cycle and decide to increase or decrease the price based upon it. Of course profit of the competitors is usually unavailable. But if competitor s profit is available, it would be very valuable information to compete. In this research it is assumed that this sort of information could be accessed from competitors. The LFC strategy is based on game theory and its flowchart is shown in Fig. 2. As shown, SP s profit (Profit) is compared with competitor s profit (Cprofit). Competitor is one of the other SPs selected randomly. If SP s profit is more than competitor s profit then moves prices to make more difference with competitor s price, otherwise moves price toward the competitor s price. The amount of moves is an input parameter of simulation run and 0.1* has been selected in the runs reported in this paper. Increasing the amount of moves causes more fluctuations in price and fast movement to the final price. C. IIR Filtering Strategy IIR filtering concept has been used to create pricing strategy in this research. In this strategy at the beginning of every cycle new price is set by revising price and profit at the previous cycle. Then price is changed multiplying by coefficient c according to the flowchart of Fig. 3. Some notations are used in the flowchart of Fig. 3. is price at previous cycle and is assigned new value at the end of flowchart for price at the new cycle. Profit is profit at previous cycle. v 1 is equal to v(1+ε) where v is the SP s variable cost which is a constant and 0<ε<<1 which in the simulation has been set to >C Increase Decrease Profit : SP s Profit Cprofit : Competitor s Profit : SP s Cprice : Competitot s Fig. 2. LFC Pricing Strategy Flowchart < v 1 Q n Profit c 1-ε 2 c 1+ε 2 c 1+ε 2 *c : SP s Profit :SP s Profit v 1 : v(1+ε) Q n : As equation 14 ε 2 : Change Coefficient Profit>CProfit >C Increase Decrease Fig. 3. IIR Filter Pricing Strategy Flowchart Q i is the IIR filter output which is used to compare with Profit to decide about new price. Q i is calculated as: Q i = a*profit + (1-a) Q i-1 where 0< a <1. (14) 33

5 In (14), a is parameter of IIR filter and is also input parameter of simulator and in this research has been set to 0.2. In the flowchart of Fig. 3 ε 2 <<1 is used to increase or decrease the price according to the sign of difference between filter output and profit and has been set to 0.1. In (14) Profit is calculated as: Profit = (- v)* PacketVolume-fixedCost (15) Where PacketVolume is amount of packet volume transmitted in the previous cycle. V. SIMULATION RESULTS AND THEORETICAL COMPLIANCE Three different simulation runs with input parameters shown in table I are reported and discussed in this section. The input parameters of table I include Customer Money which is the amount of money given to the customers to pay for packet transfer;. of Customers which is the total number of customers in the market; Variable Cost which is the average of variable cost for SPs to deliver the unit of packet rate to the customers; Fixed Cost which is the expense of SPs to fulfill operation cost (i.e. Operation Expenditure, OPEX); Initial Capital which is the money given to any new SP to start its business; New SP. which is the number of new SPs start their business in every cycle; and requirement which is the maximum amount of packet transfer by each customer. The input parameters of the simulation runs have been selected to make three different experiences e.g. Sim#1, Sim#2, and Sim#3. Generally the parameters at Sim#1 are relatively low, at Sim#2 they are medium, and at Sim#3 they are relatively high. Table II shows average of price and customer demands which are produced as outputs of simulation runs. As shown, average price for Sim#2 is the highest and therefore SPs' revenue is relatively high and average life time is longer than two others. In table III life time and number of SPs in each simulation run have been computed according to the analytical model described in section III. Simulation outputs of table II are also used to compute these analytical parameters. In table IV life time, capital and the values of Figure of Merit (FoM) for the three pricing strategies including Fix, LFC, and IIR filtering are shown. FoM is computed as: LifeTime FoM = Capital (16) FoM shows efficiency of SP in using its capital to be active in the market. More value for FoM shows the better pricing strategy. In order to compute FoM, we divide the LifeTime of SPs for each pricing strategy to Capital parameter. Capital is the sum of initial capitals (K) dedicated to any class of SPs that follows one of the three pricing strategies during the simulation. TABLE I. INPUT PARAMETR SETS OF THREE SIMULATION RUNS M :Customer Money N:. of Customers v : Variable Cost f : Fixed Cost K : Initial Capital On : New SP r : Requirement TABLE II. OUTPUT PARAMETERS OF THREE SIMULATION RUNS p: d: Demand TABLE III. THEORETICAL RESULTS OF THREE SIMULATION RUNS Life Time of SPs Fix LFC IIR TABLE IV. COMPARISON OF THREE PRICING STRATEGIES Life Time Capital FoM 5.3e e e-03 Life Time Capital FoM 11.2e-3 9.0e e-3 Life Time Capital FoM 7.0e-3 6.0e-3 9.0e-3 As table IV shows the best FoM belongs to LFC pricing strategy which is based on game theory and the fixed price strategy is the worst strategy. This is predictable that the fixed price strategy to be worst because SPs do not care about competition in the market and consequently do not change their price. Fixed price strategy is the simplest possible strategy and has been used in this research as a reference strategy to compare the proposed strategies. On the other hand LFC has the best FoM because SPs benchmark the market and adapt their price based on knowledge of the profit of other competitors. This strategy performs better than IIR filter pricing. REFERENCES [1] J. Cushnie, D. Hutchison, H. Oliver, Evolution of Charging and Billing Models for GSM and Future Mobile Internet Services, Published as Reference number HPL-IRI in HP Laboratories Technical Report, March [2] Charging, Billing and Payment Views on 3G Business Models, by UMTS Forum, Report 21, April

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