МЕТОДЫ КЛАСТЕРИЗАЦИИ ДЛЯ АНАЛИЗА ПОТРЕБИТЕЛЬСКОГО ПОВЕДЕНИЯ В ЦИФРОВОМ МАРКЕТИНГЕ
Keywords:
digital marketing, consumer behavior, clustering, customer segmentation, personalization, K-means, hierarchical clustering, DBSCAN.Abstract
This article examines clustering methods for analyzing consumer behavior in digital marketing. Clustering enables segmentation of customers based on their characteristics and preferences, which enhances the precision and effectiveness of marketing campaigns. The study focuses on K-means, hierarchical clustering, and DBSCAN methods, discussing their advantages and limitations and their application to data from digital channels to improve personalization and predict consumer behavior. The article also provides examples of successful application of clustering in marketing campaigns of large companies such as Amazon and Netflix. This research highlights the importance of clustering in the digital economy, helping companies adapt their marketing strategies to increase conversions and customer satisfaction.
References
Jain, A. K. (2010). Data Clustering: 50 Years Beyond K-means. Pattern Recognition Letters, 31(8), 651-666.
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Elsevier.
Романюк, Е.В. "Обзор методов кластерного анализа и оценка их применимости для решения задачи сегментации потребительского рынка." International Research Journal, no. 5(5), Oct. 2012.
Alejandro Rioja, 2022. https://alejandrorioja.com/how-netflix-uses-analytics-tools/
Matt Gavin, 4 Examples of Business Analytics in Action, 2019. https://online.hbs.edu/blog/post/business-analytics-examples
