MODELING THE DYNAMICS OF CONSUMER AUDIENCE UNDER THE INFLUENCE OF COMPETING ADVERTISING STRATEGIES

Abstract

The article investigates the competitive informational influence of two advertising flows on a common potential consumer audience in a changing digital environment. Modern advertising competition is shaped by digital communication channels, social media engagement, consumer trust, and organic information spread. Under such conditions, final audience distribution between competing brands shifts dynamically based on environmental variations.
The study aims to analyze consumer audience dynamics under competing marketing flows by applying an averaging scheme to a model with fast random environmental changes. A Lotka–Volterra-type model divides the potential audience into consumers influenced by the first brand, those influenced by the second brand, and a neutral audience. Parameters reflect direct advertising intensity and internal amplification from social interaction and brand trust.
Digital environment variability is described by a fast Markov process with states corresponding to different marketing activity levels. The averaging scheme replaces the initial system of fast random switching with an effective model of averaged characteristics. Three competition scenarios are evaluated: balanced competition, dominance of Brand A, and viral amplification of Brand B.
The results show that close advertising parameters yield an even audience distribution, while a stable advantage in communication intensity significantly increases final market share. Furthermore, short-term information surges can strengthen a brand’s position without initial advantages, highlighting the economic importance of consumer trust and organic content distribution in volatile digital markets.