DIGITAL TRANSFORMATION OF INTERNATIONAL MARKETING

Abstract

Modern marketing activities rely heavily on the use of large volumes of customer data to create personalized offerings and improve targeting accuracy. One of the primary data analysis methods is clustering, which allows segmentation of consumers based on their characteristics and behavioral patterns. This article explores the application of cluster analysis in developing customer profiles and examines the practical aspects of its implementation in marketing processes.

The article presents the theoretical foundations of clustering. Key clustering algorithms most commonly used in marketing, such as hierarchical clustering and density-based methods, are discussed. Each of these approaches is analyzed in terms of its suitability for various marketing tasks, including market segmentation, consumer behavior analysis, and personalized service.

The stages of customer profiling are explored, including data collection, preparation, and selection processes, as well as demographic data, purchase frequency, spending volume, and communication channels. The importance of relevant characteristics in forming segments that help identify key behavioral patterns within each customer cluster is examined. The article explains how the use of clustering algorithms can automate analysis processes and optimize working with large data sets.

The article also discusses the application of cluster analysis through software tools, describing how marketers can implement customer data clustering using platforms such as Python, specialized CRM systems, or analytical platforms that enable quick processing and interpretation of results. Data visualization techniques are examined as they assist in analyzing obtained clusters and distinguishing between customer segments based on income levels, purchase frequency, and other characteristics.

The article considers practical applications of clustering in marketing strategies, such as developing personalized advertising campaigns, optimizing product assortments for specific customer groups, enhancing communication efficiency, and creating loyalty programs. Clustering enables the formation of more precise customer profiles, opening opportunities for personalized approaches and helping marketers better understand the needs of each segment.

Possible challenges associated with clustering are also examined, including the issues of collecting high-quality data, the high computational demands of working with large datasets, and the ethical considerations regarding data privacy protection.