Forecasting the Behavior of Target Segments to Activate Advertising Tools: Case of Mobile Operator Vodafone Ukraine

Tetiana Zatonatska, Oleksandr Dluhopolskyi, Tatiana Artyukh, Kateryna Tymchenko
2022 Economics (Bijeljina)  
Today, the use of machine learning technology in combination with the use of big data are topics that are actively discussed in business around the world. This topic has long gone beyond the information sphere, as it now applies to almost every sphere of life: economic, telecommunications, education, medicine, administration, and especially defense. Predicting customer behavior based on scoring models is in its infancy in Ukrainian companies, the main ones being the introduction of artificial
more » ... telligence technologies and machine learning, which will be the leading catalyst that will facilitate decision-making in business in the nearest future. The aim of the study is to develop a scoring model that predicts the behavior of target segments, namely, updating their activity to activate advertising tools. To achieve the goal of the work a set of research methods was used: dialectical – to reveal the theoretical foundations of models and types of forecasting models; analytical – in the study of the functioning of the environment SAS, Anaconda; optimization methods – to choose the best model and generate features. Scientific novelty and theoretical significance lie in the development of a scoring model for predicting the activity of subscribers of the telecommunications company "VF Ukraine", on the basis of which marketing campaigns are conducted. With the help of the built-in scoring model, the company "VF Ukraine" can target its campaigns to retain subscribers. The marketing directorate of the enterprise can choose the TOP-20 or TOP-30 of the most prone subscribers to non-resumption of activity, i.e., tend to switch to other mobile operators, and hold promotions for them – providing additional gifts and bonuses, money to mobile account.
doi:10.2478/eoik-2022-0005 fatcat:lt7dlxtlvfcidbhvm6vsiribzq