The issue of determining the effectiveness of advertising is still open, because the process is dynamic and multifactorial. There is no generally accepted methodology for determining criteria and performance indicators for advertising communication. At the same time, advertising is an integral part of the marketing strategy. Evaluation of the effect of the advertising campaign makes it possible to make adequate managerial decisions, so the use of the most informative and reliable method of determining the effectiveness of the functioning of advertising is necessary. Against this background, it is important to develop an alternative to the classical method for determining the results of advertising activities.
The analysis of the latest research and publications confirms the relevance of the issue under consideration with respect to techniques for evaluating the effect of advertising. In their scientific publications M. Chumachenko, A. Amosha, Yu. Makogon, I. Buleev, A. Martyakova, V. Novitsky, G. Skudar, V. Gospodin, A. Novikova, N. Jankowski, J. Gohberg and others Concern current marketing issues and focus on addressing individual market problems through the activation of the marketing mechanism and its tools.
Under market conditions, any economic entity in its activity inevitably faces uncertainty. Even high-class specialist is unable to predict the changes that may occur in the external environment. Planning is one of the components of the controlling of business processes, it is the way to reduce uncertainty and risk.
When you are working with precise parameters and systems, everything is quite simple. In another case with fuzzy systems. It operates so-called principle of incompatibility: to obtain definite conclusions about the behavior of a complex system should be involved in its analysis of the approaches that utilize the principles of fuzzy logic.
Fuzzy logic - is a branch of mathematics which deals with complex classical logic and the theory of fuzzy sets.
Often the output data for the solution of economic problems are the opinions and conclusions of the experts presented by phrases and words, or linguistic data, so there is a need to transform the linguistic parameters in numeric expressions. That is the problem and solve the theory of fuzzy sets.
Limitations and disadvantages of the use of "classical" formal methods in solving semistructured problems are the result of articulated founder of the theory of fuzzy sets, LA Zadeh's principle of incompatibility: «... the closer we come to the solution of real world problems, it is clear that with increasing complexity of the system our ability to make accurate and confident conclusions about its behavior is reduced to a certain threshold, beyond which precision and confidence are almost mutually exclusive» [5, p. 165].
Business Economics is a multifactor system which, moreover, is focused on the end user, to predict the behavioral characteristics of which are quite problematic. In addition, the economy is quite sensitive to the social trends of the industry. Moreover, to predict changes in the economic activity under the influence of external and internal factors in the majority of cases it is possible only in terms linguistic (or fuzzy) concepts. Based on this priority in the economy is the use of fuzzy logic and fuzzy modeling.
References:
1. Чернов, В. Г. Модели поддержки принятия решений в инвестиционной деятельности на основе аппарата нечетких множеств / В. Г. Чернов. — М.: Горячая линия — Телеком, 2007. – с. 312.
2. Риск — анализ инвестиционного проекта / Под. ред. М. В. Грачевой. — М.: ЮНИТИ, 2000. – с. 344.
3. Недосекин, А. О. Нечетко-множественный анализ риска фондовых инвестиций / А. О. Недосекин. — СПб.: Типография «Сезам», 2002. – с. 181.
4. Рыбак, В. А. Методологические основы принятия решений для управления природоохранной деятельностью: монография / В. А. Рыбак. — Мн.: РИВШ, 2009. – с. 274.
5. Заде, Л. А. Понятие лингвистической переменной и его применение к принятию приближенных решений: пер. с англ. / Л. А. Заде. — М.: Мир, с. 1976. – с. 165.
6. Ходашинский И.А. Идентификация нечетких систем: методы и алгоритмы // Проблемы управления. – 2009. – № 4. – с. 15–23.
7. Штовба, С. Д. Проектирование нечетких систем средствами MATLAB / С. Д. Штовба. — М.: Горячая линия — Телеком, 2007. - с. 288.
8. Espinosa J., Vandewalle J., Wertz V. Fuzzy logic, identification and predictive control. — London: Springer-Verlag, 2005. - p. 263.
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