ANALYSIS OF THE PROJECT OF A MARKETING CAMPAIGN TO PROMOTE ROBOTIC SOLUTIONS USING RANDOM FOREST CLASSIFICATION

Abstract

The object of research is marketing strategies for promotion in social networks, which are the basis for achieving the basic requirements of brands: audience commitment, brand loyalty, awareness, positioning, conversion and reputation. Because of this, a significant number of modern companies that manufacture robotic complexes are considering the possibility of implementing such strategies using a project approach. The work is aimed at analyzing data and evaluating marketing campaigns for the promotion of robotic solutions, carried out using Random Forest classification, in order to identify patterns and increase the effectiveness of such campaigns. The analysis was conducted on the example of three advertising campaigns. The analysis showed how the criteria taken into account when displaying advertisements on social networks, namely the age category of a person, gender, interest group of a person (according to the public profile of the social network), the number of ad impressions affect the number of clicks on the corresponding advertisement. As well as the total number of people who became interested in the product after seeing the advertisement, the total number of people who bought the product after watching the advertisement. The essence of the results obtained is that the study showed the possibility of assessing the effectiveness of marketing campaigns at the early stages, the measurability of performance indicators in terms of audience reach, level of interaction and conversion into reverse actions. The results of the study reflect the complex relationship between the conversion indicators of advertising campaigns and the main criteria for their implementation, emphasizing the importance of a project approach and the use of machine learning for building marketing campaigns. The study focuses on practical aspects. From a practical point of view, mastering the basic metrics of data mining, segmentation, the ability to use A/B testing and the use of machine learning methods, in particular the Random Forest classification algorithm, allows to increase the effectiveness of campaigns. And also reduce the risks of losing money due to incorrect conclusions regarding the segmentation of target audiences. The results of the study can become the basis for the formation of new strategies for conducting marketing campaigns when promoting robotic systems, adjusting existing ones, capable of effectively and flexibly adapting depending on the target audience and the dynamics of working with it.