METHODS OF MEASURING INFORMATION NOISE IN MARKETING RESEARCH

Abstract

Introduction. In the context of information overload and increasing data volumes, marketing research faces significant obstacles associated with the influence of information noise, which distorts the results of analysis and complicates management decision-making. At the same time, since information noise includes unsystematic data, distortions, excess information and false signals that make it difficult to identify real trends and consumer needs, the development of effective methods for measuring information noise is an important task for improving the quality of marketing research.

The purpose of the article. The purpose of the study is to determine methods for measuring information noise in marketing research aimed at increasing the accuracy of data analysis, minimizing the impact of information distortions, and optimizing decision-making in strategic planning of marketing activities.

Methods. The methodological basis of the study is a systematic approach, which includes the use of quantitative and qualitative methods of analyzing information noise. Content analysis was used to identify sources of noise, mathematical models to measure it and assess its impact on the results of marketing research. Comparative analysis methods were used to compare the effectiveness of different approaches, as well as logical generalization to confirm the reliability of the results obtained.

Results. The article examines the problems of information noise in the context of modern marketing research, in particular its sources, main characteristics and impact on the results of the analysis. It studies how information noise manifests itself in data collected through various channels and analyzes the key factors that generate information noise and are associated with data redundancy, irrelevance and ambiguity. It examines approaches to measuring the level of information noise based on mathematical modeling, statistical methods, machine learning algorithms and artificial intelligence tools. It proves the need to combine quantitative and qualitative methods that allow comprehensively assessing the level of noise and its impact on data quality. It explores the possibilities of integrating the latest technologies to automate data cleaning processes using intelligent filters, anomaly detection programs and algorithms for classifying irrelevant information.

Conclusions. It has been proven that correct identification and reduction of information noise contributes to increasing the accuracy of forecasts, optimizing marketing strategies and rationalizing the processes of making managerial decisions. Principles for overcoming information noise have been identified, which allow businesses to adapt to the complex conditions of the information environment, reducing the cost of time and resources for processing irrelevant information.