Lack of Effective Consumer Behavior Prediction Algorithms: Problems and Solutions

In the modern business environment, consumer behavior forecasting is an important decision-making tool. However, existing algorithms often prove to be non-sufficiently effective, thus necessitating their improvement. The aim of this article is to analyze the reasons for the non-sufficiently efficiency of traditional forecasting methods and to develop recommendations for their enhancement. Objectives include studying various forecasting approaches, identifying shortcomings in data collection and processing, as well as proposing new technologies capable of increasing forecast accuracy. The research employs a comparative analysis of statistical models, machine learning algorithms, and surveys into issues related to data quality and volume. Particular attention is given to adaptive algorithms that can account for changes in consumer behavior in real-time. The carried out analysis has shown that traditional forecasting methods do not always meet the needs of the modern market. It has been found that non-sufficient integration of data from various sources and the limited adaptability of existing algorithms lead to inaccurate forecasts. New technologies, such as deep learning, are proposed, which can significantly enhance forecasting efficiency. Innovative approaches to predicting consumer behavior are critically important for improving business competitiveness. A further research of the impact of cutting-edge technologies, particularly social media, on consumer behavior is recommended. The article emphasizes the need to adapt algorithms to market dynamics, which can significantly improve forecasting results and help companies respond more efficiently to changes in consumer demand.