Data Science Methods: Analyzing Approaches to Customer Segmentation

The article addresses the issue of enhancing the efficiency of marketing strategies in the context of the modern dynamic market environment, characterized by a high level of competition, information overload, and rapid changes in consumer preferences. The analysis conducted revealed that traditional segmentation methods based on demographic, geographic, and socioeconomic characteristics fail to account for individual needs, preferences, and behavioral traits of consumers, which diminishes the efficiency of marketing campaigns. It is noted that in these conditions, innovative alternative approaches based on the use of unstructured data, dynamic segmentation, and psychographic factors come to the forefront. The implementation of alternative approaches is impossible without the application of Data Science methods such as k-means, hierarchical clustering, and DBSCAN, which allow for the detection of hidden patterns in customer behavior and the formation of more accurate segments. The advantages and disadvantages of clustering methods during the implementation of alternative approaches to customer segmentation have been identified. It is specified that the choice of segmentation method should be based on a comprehensive analysis in the context of a specific task (selective approach) or hybridization, which allows for enhancing the advantages and mitigating the disadvantages of each method. It is underscored that selection or hybridization when working with unstructured data requires the transformation of data (text, audio, media) into numerical form. The combination of K-means, hierarchical clustering, or DBSCAN, along with other methods such as keyword extraction, topic modeling, word vector representations, natural language processing (NLP), or computer vision, will assist in solving this task. For dynamic segmentation, mini-batch K-means, which is a modification of the K-means algorithm used for clustering large datasets, can be employed. This significantly accelerates the clustering process, especially when dealing with substantial amounts of information. It is emphasized that psychographic factors likely hold the greatest significance, as they more deeply reflect the values and motivations of consumers. Hierarchical clustering allows for the visualization of groups of different psychographic factors, but the interpretation of such associations will require additional analysis using AI. The most appropriate method for segmenting clients based on psychographic factors is DBSCAN. This method allows for the identification of groups with similar profiles, even if they have complex shapes, as well as clusters with nonlinear boundaries. It is underlined that further research should be directed towards the development of hybrid models that combine various clustering algorithms with alternative approaches to enhance the effectiveness of the company’s marketing strategy.