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

  • Authors

    • Olena Iashchenko Vice President of Glad Selena Corporation, Fort Lauderdale, FL, USA
    • Olena Chupryna Mariupol State University, Kyiv, Ukraine
    • Sergiy Maksymov Danube Institute of the National University «Odesa Maritime Academy»
    • Nataliia Zemlianska Kyiv Municipal Academy Of Circus And Performing Arts, Kyiv, Ukraine
    • Olga Afanasieva Odesa National Maritime University, Odesa, Ukraine
    • Yuliia Popova State University of Infrastructure and Technologies, Kyiv, Ukraine
    https://doi.org/10.14419/qk1f2333

    Received date: August 6, 2025

    Accepted date: August 14, 2025

    Published date: September 17, 2025

  • Information Noise; Market Research; Marketing; Marketing Strategy; Information Flows; Data

  • Abstract

    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.

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  • How to Cite

    Iashchenko, O., Chupryna, O., Maksymov, S. ., Zemlianska, N. ., Afanasieva, O. ., & Popova, Y. . (2025). Information Noise in Marketing Analytics: Implications forFinancial Reporting and Economic Decision-Making. International Journal of Accounting and Economics Studies, 12(5), 702-709. https://doi.org/10.14419/qk1f2333