The method of creating players in the marketing strategy

Abstract

Internet marketing takes a variety of forms and uses different formalisms and grammar [1]. In online stores, entering the keyword we receive harmoniously arranged (on the web page) offers from a given domain as well as related and quite distinct domains [2]. The greatest challenge for marketing system designers is a choice of appropriate variants of offers arrangement in terms of diversifying their types. Another significant issue is also the distribution, which is, for example, the frequency of use of particular variants of offer pages. The aim of our research is to predict client’s needs and preferences in more accurate way. The strategy that we have chosen is supposed to increase the attractiveness of an offer, which indirectly facilitates customers’ product searches and affects his or her purchase decision. Common product-offering models are extremely poor in functions and they do not allow us to determine any factors that would decide on objects which are more likely to meet expectations of a client. In those models related products of chosen category are selected randomly what is ineffective. Due to that we are going to improve it and make the access to desired products easier and quicker for customer by building an offer based on characteristics referring to demand, sales volume, trends, etc. The above issues suggest the possibility of using strategic games in which the players are variants (strategies of model structuring) of product-offering solutions applied with a particular probability distribution (frequency of using product-offering models). Strategies are also connected with the assessments of their effectiveness, that is player’s payoffs which will not be described in this article. The main focus of this work is the idea of creating players by determining their payoff [9]. In order to explain this concept precisely the authors present the structure containing formalisms (Sect. 1), the general description of the proposal of creating competing models (Sects. 2, 3), the example (Sect. 4) and summary (Sect. 5).