USING MARKETING RESEARCH METHODS TO CREATE A CONSUMER PORTRAIT
Abstract
The article examines the role of marketing research in creating a consumer profile. It analyzes data collection and processing methods, including surveys, focus groups, and observation. Special attention is given to market segmentation, identifying customer motivations and preferences. An algorithm for conducting marketing research to model consumer profiles is presented. Examples of detailed consumer portraits based on collected data are provided. It is determined that a comprehensive approach to analysis enhances the accuracy of consumer needs forecasting and contributes to the development of effective marketing strategies, business process optimization, and increased company competitiveness. Key stages of working with the target audience are outlined, including defining socio-demographic characteristics, behavioral patterns, and consumer interests. Practical recommendations for utilizing research results to personalize marketing campaigns and increase customer loyalty are highlighted. Significant attention is paid to the psychological aspects of consumer behavior that influence purchasing decisions. The role of emotional and rational factors in shaping consumer preferences is analyzed. The specifics of marketing research across various industries, including retail, e-commerce, services, and B2B sectors, are considered. The importance of a multichannel approach to data collection is emphasized, allowing for a comprehensive analysis of consumer behavior in both online and offline environments. The study concludes that a systematic approach to data analysis is essential for ensuring sustainable business development in a competitive market. The obtained results can be used to improve brand positioning, enhance advertising campaign effectiveness, and expand market opportunities. Additionally, the paper highlights the role of predictive analytics in anticipating consumer trends and adapting marketing strategies accordingly. The integration of machine learning algorithms for automated data processing is discussed as a way to improve research accuracy.
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