Using Machine Learning to Predict the Effectiveness of Email Campaigns in Marketing

The topic's relevance is because in the context of the rapid development of digital marketing, email communications are one of the most important ways for businesses to interact with their audience. High competition and information overload of subscribers encourage marketers to search for new approaches to accurately predict user reactions to received emails. Given this, the use of modern analytical tools, particularly machine learning technologies, is of particular importance, as they contribute to the effective prediction of the effectiveness of marketing campaigns. The article aims to study the potential of using machine learning technologies to predict the effectiveness of email campaigns in digital marketing and to substantiate their practical feasibility for enterprises in the context of increased market competition. The study uses an analysis of scientific sources, systematisation of approaches to applying machine learning technologies, modelling user response to emails using the gradient boosting algorithm, and a comparative analysis of existing models. The study has revealed that a personalised approach, determination of the optimal time of sending, and analysing the target audience's behavioural models are important factors for accurate forecasting. The main difficulties that hinder the active use of such models are highlighted, including problems with the availability and quality of data, the complexity of interpreting the results of algorithms, the shortage of qualified specialists and the issue of personal data protection. In addition, a predictive model has been developed and practically tested to accurately predict user behaviour regarding opening emails, clicking on links, conversions, and unsubscribes. The conclusions confirm that the proposed model, developed based on the gradient boosting algorithm (XGBoost), has demonstrated high accuracy in predicting the opening of emails and link clicks, which indicates its practical effectiveness. Prospects for further research are related to improving the transparency of predictive models and expanding their application in integrated solutions for automating enterprises' marketing processes.