EVALUATING CONSUMER DEMAND AS A KEY FACTOR IN THE DEVELOPMENT OF THE PROCESSING INDUSTRY BASED ON A MARKETING MODEL

Abstract

The article substantiates that evaluating and forecasting consumer demand is a strategic imperative for the development of the processing industry, as an accurate demand assessment is a critical key factor that directly affects the competitiveness, innovative potential, and sustainability of enterprises in the modern customer-centric economy. Inaccurate understanding of consumer needs leads to suboptimal resource utilization and financial losses. The goal of the research is the development and substantiation of a comprehensive marketing model that integrates the analysis of macroeconomic factors, consumer behavior research, and quantitative forecasting. The analysis revealed that, despite significant scientific achievements, the task of integrating multi-level factors (macroeconomic, micro-market, and behavioral) into a unified, flexible model remains insufficiently resolved in the scientific literature. Specifically, a clear mechanism for adaptive quantitative correction of forecasts, which would allow for promptly accounting for unpredictable external shocks without the need for complex econometric modeling, is lacking. In the course of the research, two complementary models were developed and substantiated: a comprehensive marketing model, which provides a multi-level factor decomposition (macro-, micro-, consumer levels) for strategic forecasting, and an adaptive factor forecasting model, which serves as an operational tool for quick correction of the baseline forecast. The comprehensive model uses econometric demand modeling, PESTEL factor analysis, competition assessment using Porter's Five Forces Model, and primary marketing research (surveys, focus groups). The adaptive model applies time series decomposition methods (moving average, seasonality indices), operational market monitoring, and heuristic correction based on expert interviews and scenario planning to account for external risks. The presented models and their tools provide the necessary systematization of methodologies and enhance the adaptability of forecast estimates. Further research prospects focus on the empirical verification of the models and the development of tools that account for non-linear demand elasticity.