Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 46, Pages: 1-14
Roman Rashydovich Tolstyakov and Natalya Vasilyevna Zlobina*
*Author for correspondence
Natalya Vasilyevna Zlobina
Tambov State Technical University, Russian Federation; [email protected]
Objectives: The article deals with one of the modern methods of marketing communication that is viral marketing. Users of social networks act both as target audience and distributors (recipients) of viral videos. Methods: The synergetic approach should be applied in the research, which makes determination and clear formalization of a viral video campaign almost impossible. The author’s propose to classify the users into three basic groups: active, interested and passive. The proportion of these types of recipients in the network determines the efficiency and quality of the viral video campaign. The iteration method should be used to establish ranges of the acceptable values. Findings: The article presents a formalized model of a social network consumer that takes into account the non-linear nature of the virus development. The authors define the ranges for the parameters of the proposed activity (watching videos, sending videos, comments, likes, deletion). The article proposes ways to evaluate communication media activity, the latter represented by social networks. The classification of social networks operating in the Runet was conducted after the criteria of thematic content and popularity according to search queries. The authors considered the most popular networks and estimated their communication activities according to the concentration of various types of network users. The experimental part of the article identifies the target group and the efficiency of a predictive model of the viral video campaign. Improvements/Novelty: It is planned to create the predictive model of the viral video life cycle with focus on days where the activity occurred in the communication medium and the range of the criteria for each group of users.
Keywords: Improving the Viral Campaign Management, Internet Marketing, Marketing, Predictive Model, Social Networks, Viral Marketing
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