Information Noise in Marketing Analytics: Implications forFinancial Reporting and Economic Decision-Making

This study explores the phenomenon of information noise as a critical impediment to the accuracy and reliability of marketing analytics and ‎economic decision-making in data-intensive environments. The research is economically oriented, addressing the adverse effects of informa-‎tional distortions on the quality of forecasts, strategic planning, and the financial efficiency of market-oriented enterprises. Information noise ‎is examined as a multidimensional construct that arises from data redundancy, irrelevance, ambiguity, contradictions, and temporal incon-‎sistencies. These distortions hinder the interpretability of marketing data, reduce the validity of econometric analysis, and contribute to ‎suboptimal business decisions. The paper develops a comprehensive methodological framework for identifying, measuring, and mitigating ‎information noise through the integration of statistical analysis, mathematical modeling, semantic diagnostics, and artificial intelligence tools. ‎A particular focus is placed on the economic consequences of noise, such as resource misallocation, increased forecasting error, and de-‎creased return on marketing investments. The empirical section presents a correlation-regression model based on data from a real enterprise, ‎which quantifies the impact of specific noise factors on sales forecast accuracy. The model reveals that trust in data sources, content redun-‎dancy, and channel fragmentation significantly affect the reliability of forecasts, highlighting the need for information quality control in fi-‎nancial planning. The findings emphasize the importance of structured data filtering, entropy-based anomaly detection, and adaptive noise ‎management strategies for improving economic performance in digital markets. The study contributes to the theoretical conceptualization of ‎information noise in marketing economics and offers practical recommendations for enhancing the quality of decision-making through intel-‎ligent data preprocessing. As a result, it provides a foundation for the development of more resilient and analytically sound business models ‎in the context of digital transformation and informational complexity‎.