Machine Learning Systems for Predicting Consumer Behaviour

Abstract

The development and implementation of machine learning systems for predicting consumer behaviour is a crucial stage in the digital transformation of business. Machine learning offers unique opportunities for analysing large volumes of data and identifying hidden patterns, significantly enhancing the decision-making process in marketing. The use of algorithms such as neural networks, decision trees, and clustering methods allows businesses to predict future consumer preferences and create personalised offers. This, in turn, increases customer loyalty and the effectiveness of marketing campaigns. The review discusses key approaches to applying machine learning in the context of consumer behaviour analysis, along with examples of successful implementation of these technologies in business practices. The findings revealed that decision trees serve as a tool for visualising decision-making processes, helping marketers segment audiences and predict customer behaviour. Classification methods allow users to be assigned to specific categories, which simplifies the development of personalised offers and advertising campaigns.  Companies like Google and Facebook are actively developing deep learning-based tools, contributing to the further integration of AI into business processes. The ability to predict consumer behaviour with high accuracy is becoming an integral part of future marketing strategies, and deep learning is already significantly transforming approaches to product and service promotion. The high accuracy of predictions helps optimise supply chains and reduce operational risks, making machine learning a key element of modern marketing. The main findings of the study emphasise that the integration of machine learning systems enables businesses to manage inventory more efficiently, reduce risks, and adapt to changes in demand.