Abstract
In the paper it solves the problem of modeling complex economic processes. There is presented an analytical review of economic models, their advantages and disadvantages. The use of integral models for simulation of nonlinear dynamic processes in marketing tasks is substantiated. The mathematical expressions for the writing of integral models in the form of multidimensional weighted functions are given. The computational method of identification of integral models in the form of multidimensional transient characteristics is developed with the help of measuring the reaction of the market to the input influence. The results of modeling of the advertising audience on the example of the advertising campaign of the higher educational institution are presented. Keywords: digital marketing, marketing model, nonlinear models, integral dynamic models DOI: 10.15276/mdt.2.1.2018.5 Korbicz, J. & Kościelny, J.M., (eds). (2010). Modeling, Diagnostics and Process Control: Implementation in the DiaSter System. Springer: Berlin. Balasubramanian, S., Gupta, S., Kamakura, W.A., & Wedel, M. (1998). Modeling large datasets in marketing. Statistica Neerlandica, 52 3, 303–324. Leeflang, P.S., & Wittink, D.R., (2000). Building models for marketing decisions: Past, present and future. International Journal for Research in Marketing 17, 105–126. 4. Doll, J., & Eisert, U. (2014). Business Model Development and Innovation, a Strategic Approach to Business Transformation. The Business Transformation Journal, 11, 7–15. Gassmann,O., Frankenberger, K., & Csik, M. (2015). The Business Model Navigator: 55 Models That Will Revolutionise Your Business. FT Press. Bishop, W. (1996). Strategic Marketing for the Digital Age. HarperBusiness. Celaya, J., Vazquez, J., & Rojas, M. (2014). How the new business models in the digital age have evolved, Kindle Edition. Oklander, M.A., Oklander, T.O., & Yashkina, O.I. (2017). Tsifroviy marketing – model marketingu XXI storichchya [Digital marketing – a model of marketing ХХІ century]. M.A. Oklander (Ed). Odesa: Astroprint [in Ukrainian]. Masri, M, & Pavlenko, V. (2016). Intelligent Technology of Nonlinear Dynamics Diagnostics using Volterra Kernels Moments. International journal of mathematical models and methods in applied sciences, 10, 158–165. Antoshchuk, S.H., & Fomin, O.O. (2017). Model marketynhu, yaka keruietsia danymy [Data-driven marketing model]. Marketynh i tsyfrovi tekhnolohii [Marketing and digital technology], 1 (2), 92–101 [in Ukrainian].
Highlights
The mathematical expressions for the writing of integral models in the form of multidimensional weighted functions are given
Dynamic models of the economy – economic and mathematical models that describe the economy in development, in contrast to the static, characterizing its state at a certain point
As a description of the OK of an unknown structure, it is advisable to use nonlinear nonparametric dynamic models based on Volterra integro-power series (PB), which describe the properties of OK in the form of a sequence of invariant to the form of the input signal of multidimensional weight functions (IMF)
Summary
The mathematical expressions for the writing of integral models in the form of multidimensional weighted functions are given. To obtain dynamic models, nonparametric identification methods are used, based on the results of monitoring a process or an object when test input influences are applied to it.
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