Applying Cluster Analysis to Enhance Personalization Strategies

The article explores the application of cluster analysis in improving personalization strategies in marketing and other business areas aimed at tailoring offerings to user needs. The objective of the study is to analyze methods by which clustering facilitates accurate customer segmentation and the development of individualized interaction strategies. Various clustering algorithms, such as hierarchical clustering and the K-means method, were evaluated for grouping customers based on similarities in consumer preferences, behavior, and demographic data. The methodological foundation of the article is based on the analysis of scientific literature, enabling an exploration of how cluster analysis identifies user preferences and improves prediction accuracy. The integration of this method into existing recommendation systems is demonstrated, highlighting its ability to enhance the precision of suggestions. Results indicate that cluster analysis improves the customer experience and increases company profitability by enhancing the effectiveness of marketing strategies targeted at specific user groups. The material is intended for data analysts, marketers, product managers, developers of personalization systems, and those involved in creating and implementing recommendation solutions. In conclusion, the application of cluster analysis confirms its effectiveness in optimizing personalized strategies, allowing for audience segmentation and the development of offerings that accurately address user interests.