Pricing in the concept of cognitive marketing in the context of globalization: theoretical, methodological and applied aspects

Abstract

The study used the following general scientific methods of theoretical and empirical research: a systematic approach – to justification for parametric index at set prices based on the additional premium for quality, based on the use of quantitative and qualitative indicators; methods of theoretical generalization – for grouping the principles and functions of prices in the context of the concept of cognitive marketing; the method of dispersion analysis – to detect and assess the price formation dependence not only on quantitative characteristics (material costs), but also on the qualitative characteristics of the product (parametric quality index); the method of correlation-regression analysis – for constructing a regression model of pricing. The main results of the study were to identify the differences in the marketing mix in the context of the concepts of cognitive and traditional marketing. The evolution of cost categories in the concept of cognitive marketing was substantiated. It was proposed the concept of cognitive value. Innovative demand was defined as a new factor influencing pricing. The formation of value through the use of methods for the formation of demand for innovative products was proposed. A new price function and pricing algorithm in the concept of cognitive marketing was substantiated. An approach is proposed that involves the creation of potential demand at the expense of the value already specified by the manufacturer. It was substantiated that the value of the factors of production should be transferred to the value of the goods, which involves taking into account the value of both the material and the intangible component. It is suggested to take into account the parametric quality index when setting the price as an additional price premium. This approach has been tested in the formation of predictive values of the price of products of machine-building enterprises with the help of regression-disaggregated analysis.