The Life Cycle Identification Model For New Service Of Mobile Communication Industry

Comparing to the slowing-down mobile communication industry's income growth, new service of the mobile communication industry (new service for short as followed) maintains high and steady rate of rise on income .Therefore, the new service becomes to be the motivation of the whole industry's general income and the focus of the competition.The new service is composed of many kinds of sub-service. There are many differences between the new service and the traditional voice service. Also, they have different lengths of life cycles and been in the different phases of their life cycles. Therefore, their marketing ways and the consumers'habits are dissimilar. However, the new service's current management is based on the management pattern of the traditional voice service; it can not get on with the new service's development.Product Life Cycle (PLC) is the base of marketing strategy establishment. That is, new service's marketing strategy should be made according to PLC. However, PLC and its identifying model can hard be applied to new service. There are two aspects of reasons. First, PLC has been an abstract theory for many years, rarely applied to practice. Current researches have three aspects of disadvantages, imperfect index system, imprecise consideration on economic environment surrounding and hard to apply. Second, new service has the attribute of the product, but it is some kind of service itself. PLC theory and its identification methods are research for industrial products.Based on the reviewing of the PLC theory, this thesis selected the PLC which was researched from the distributing rule's point of view and the fuzzy math based on relative concept as the theoretic foundation and methods of the life cycle identifying model for new service.Fuzzy math was based on fuzzy set theory. It is use to described the sets which are not distinctly defined. Thus it is suitable to settle that PLC had no distinct lines between its phases. Accordingly, fuzzy math can be used as a tool to identify new service's life cycle based on the index system.There have been two methods related to the fuzzy math which has been applied to identify PLC——the fuzzy methods with absolute concept and the fuzzy methods with relative concept. The fuzzy method with relative concept is based on the absolute one. The relative one added the macroeconomic, the microeconomic and the industrial influence to the absolute concept. The relative definitions of PLC phases were proposed with considering the three aspects. The relative one has made a great progress in the PLC indexes'definitions. But the definitions of the product relative rising index and the industrial life cycle influence index have to be redefined more precisely, so that they will not be separated into two phases.In the process of setting up the index system of new service's life cycle, this thesis selects the fuzzy math with relative concept. The more important is that this thesis will set up a new index system combined with the new service's characteristics, in which the product relative rising index and the industrial life cycle influence index have been redefined more precisely and described afresh.After the index system has build up, the subjection functions should be built subsequently to describe the indexes in math language and form the fuzzy set for each of the new service's life cycle phase. So that the new service life cycle can be separated into different phases and the highest subjection degree principle can be used to identify which phase the new service mostly lies in.Thus, the subjection functions'selecting is the key problem of the identification model's building. There fore, this thesis analyzes and compares the advantages and disadvantages of the current methods in selecting the subjection functions to settle the key problem. Considering the advantages and disadvantages of each method, this thesis decides to use referring method to build the subjection functions for each of the new service's indexes.Subsequently, the subjection functions are integrated to be fuzzy set which can describe each phase of the new service's life cycle. Then the highest subjection degree principle can be used to see which phase the new service mostly lies in.Finally the model is applied and examined in a case and the result from the application is analyzed. In the process of the model application, how to choose the experiential parameters is the main problem. Because different experiential parameters lead to different results. A series of selecting methods are proposed to settle this problem in this thesis. What's more, idiographic methods are provided to set the parameter for new service's profit margin, according to its wide range and uneven raise. According to the result of the model application, the conclusion is drawn that this thesis has set up a new service life cycle model for new service of mobile communication with mature index system, which can be seen as strong support for mobile communication operation decision-making due to its easy application.