ONLINE MARKETING STRATEGY FOR LOYALTY MANAGEMENT USING ARTIFICIAL INTELLIGENCE
Abstract
The article is aimed at substantiating the Internet marketing strategy for managing the loyalty of cinema customers through recommendation systems using artificial intelligence. The classification of recommendation systems is considered and the potential of their application to increase the share of loyal customers of cinemas is analyzed. The relevance and necessity of introducing artificial intelligence tools into the recommendation systems of cinemas are proved. The effectiveness of using recommendation systems of artificial intelligence in communication with cinema customers to increase the share of loyal customers is substantiated. The strategies for increasing the share of loyal customers with the help of recommendation systems for different groups of customers: potential, new and old are developed. For each proposed strategy a set of measures for the use of different in essence recommendation systems is defined. A systematic approach to determining the complex of recommendation systems depending on the type of cinema client is proposed. An Internet marketing strategy for managing the loyalty of cinema customers has been developed on the basis of determining the type of customer and applying a certain set of tools of recommendation systems to each type of customer. Implementation of the proposals of this study contributes to increasing the share of loyal customers of the cinema. It allows to develop a flexible and effective toolkit for applying the proposed Internet marketing strategy to increase the share of loyal customers through a systematic approach to customer typology and selection of appropriate recommendation systems. Artificial intelligence tools are used in various spheres of economic and social activity. Recommendation systems allow increasing the share of loyal customers and are a convenient tool for developing an online marketing strategy for a cinema. Taking into account the type of client when choosing the tools of the recommendation system will cover all areas of communication interaction between the client and the entertainment institution. Further research should focus on improving the tools of recommendation systems after the trial period of their application.
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C.C. Aggarwal (2016) Recommender Systems: The Textbook, Springer.
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R. Banik. (2018) Hands-On Recommendation Systems with Python. Packt Publishing.
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