PRACTICAL REALIZATIONS OF A GENERALIZED ADVERTISING CYBERNETIC MODEL

Nowadays, a big number of various math models of advertising exists, however all of them describe only specific effects of advertising influence, thus, considering most modern advertising campaign utilize multiple ad channels for different purposes and are highly integrated, systematic and complex in its structure, math modelling of such campaigns remains a challenge for practitioners. As for today, there is no such framework that would be flexible and effective enough in solving tasks of advertising campaigns management. Hence, a framework of generalized advertising modelling and illustrations of its practical realizations for particular real cases would be a valuable addition to the research of the topic as well as a useful manual for practitioners. An objective of the current article is to illustrate various cases of generalized advertising model realizations, explain framework and algorithm of building model’s realizations for particular integrated marketing campaigns and show economic effects of proposed approach on an example of a real enterprise. Proposed framework is especially focused on modelling sequential and structured customer journeys and utilizing its systematic effects which become a modern standard of advertising activities among the enterprises. Article also includes considerations of modelling modern marketing tools such as WOMM and its integration into advertising campaigns utilizing more conventional tools which is illustrated for a first time in research literature on the topic. Proposed model supports taking into account indirect advertising tools which are not resulting in purchase directly, however, build awareness or move customers towards a purchase within a customer journey in any other way. Present research uses such methods as synthesis, generalization, case study and a real-field experiment. Results of this research can be useful for marketing practitioners as a guidance for building math models of bespoke advertising campaigns. Provided guide also shows how to turn built model into an optimization one and use it as a support for budgeting decision making process. Cases of practical application of proposed guide show improvement of profitability of advertising campaign ranging between 3.6% and 30% percent depending on market peculiarities.

Abstract licence: CC BY

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