A Data-mining Approach to Library User Segmentation: Clustering Readers by Perceived Marketing Communication
Year: 2026
Type: article
Study design: Observational Study
Abstract: This paper applies unsupervised data mining to segment users of information-library centers by their perception of digital and marketing communication. The empirical basis is a survey of readers from fifteen information-library centers in Uzbekistan. From 213 responses, 180 complete records with seven Likertscale perception variables were retained. After z-score standardization, k-means clustering was used, and the number of clusters was selected by the silhouette coefficient with additional validation by the CalinskiHarabasz and Davies-Bouldin indices. A stable four-cluster solution was obtained: Engaged Enthusiasts, Moderately Satisfied, Loyal-in-spite-of and Disengaged. The main finding is a perception-loyalty paradox: some users remain satisfied even when mediated communication is weakly perceived. The proposed segmentation provides a reproducible instrument for differentiated communication planning in library management.
Author: Martin V. Grebenyuk
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7202264780