Artificial Intelligence as a Tool for Forecasting Consumer Demand and Behaviour

Abstract

The transformation of the marketing environment driven by digitalization creates a need for the methodological reassessment of demand-forecasting tools. The relevance of the study stems from the need for businesses to adapt to the conditions of the digital economy and the modern market. The aim is to substantiate the methodological foundations for applying artificial intelligence algorithms to demand forecasting and consumer-behavior modeling. The study employs a set of complementary scientific methods. In particular, systems analysis is used to classify current approaches to the application of artificial intelligence in demand forecasting and to identify their functional features within marketing analytics. To construct and interpret the forecasting approaches, elements of economic and mathematical modeling are applied, including time-series analysis (the ARIMA approach), recurrent neural networks (LSTM), and ensemble machine-learning algorithms (Random Forest, XGBoost), which capture both linear and nonlinear dependencies in consumer data. A comparative analysis of the forecasting models is performed using standard quality metrics, namely the mean absolute error (MAE), the root-mean-square error (RMSE), and the mean absolute percentage error (MAPE), which allow the accuracy and stability of the forecast results to be evaluated under various data conditions. In addition, dedicated methods of consumer-behavior data analysis are applied, including the processing of purchase history, digital activity, and responses to marketing stimuli, which makes it possible to incorporate behavioral factors into the demand-forecasting process. The results show that artificial intelligence substantially changes the approach to demand forecasting. Whereas companies previously tended to react to trends that had already taken shape, it is now possible to anticipate such trends at much earlier stages. Machine-learning algorithms reveal complex interrelationships among price, seasonality, behavioral patterns, and external factors that are difficult to capture using traditional models. At the same time, the effectiveness of these technologies depends largely on data quality, the company’s level of digital maturity, and its ability to integrate analytical systems into management processes. It is concluded that artificial intelligence is gradually becoming a key element of contemporary marketing analytics, influencing not only forecast accuracy but also the approaches to managerial decision-making in the area of consumer demand and behavior. Further research should focus on refining forecasting models and adapting artificial intelligence algorithms to dynamic market conditions.