USE OF RECOMMENDATION SYSTEMS FOR OPTIMIZATION OF THE COMPANY’S MARKETING STRATEGY
Abstract
The article examines the features of digitalization processes impact on user behavior and the reorientation of companies to the digital environment. The expediency of using advanced digital marketing tools to establish communication with the target audience on a regular basis has been proved. The effectiveness of using referral systems on companies’ web resources in the process of increasing conversions in the long run has been established. The main advantages of using recommendation systems in accordance with scientifically sound approaches are revealed. Examples of the use of relevant content in recommendation systems by technology companies in developed countries are given. The main sources of information used in the process of building recommendation systems are revealed. Methods of stimulating users to provide personal information, which is used in the process of building effective referral systems, are presented. The expediency of using the system of indicators (KPI) about the studied phenomenon in the process of customer identification in the recommendation system is proved. The classification of recommendation systems used in modern conditions is given. Prerequisites for the creation of a large number of machine learning algorithms focused on the creation of recommendation systems, due to the active development of computer equipment and the improvement of specialized programming languages (Python, R). The specifics of using machine learning methods in the process of optimizing the functioning of recommendation systems are presented. It is proved that thanks to the application of advanced data science approaches it is possible to turn valuable information into effective management decisions that will maximize profits in the long run. It is established that in real conditions the identification of an effective machine learning algorithm and selection of model parameters is carried out to a certain level of accuracy, which should be achieved to ensure acceptable communications with the target audience and economically justified level of profitability. The expediency of using neural networks to build recommendation systems as one of the most effective approaches is proved.
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