A Data-Driven Analysis of Library User Segmentation through the CRISP-DM Framework

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Pandaree Soonthonwarapas

Abstract

The purpose of this study was to analyze and segment library users using a data-driven approach by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The CRISP-DM framework consists of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset used in this study comprises 35,242 transaction records of information resource borrowing from the Khunying Long Athakravisunthorn Learning Resources Center, Prince of Songkla University, Hat Yai Campus, covering the period from January 1 to December 31, 2024. Data analysis was conducted using KNIME analytics software, employing the RFM segmentation technique, which is based on three key indicators: Recency (the most recent library usage), Frequency (the number of transactions), and Monetary (the volume of borrowed resources). The data were analyzed across defined time intervals to classify library users into distinct segments according to their usage behaviors. The results demonstrate that the proposed approach effectively identifies clear and meaningful user segments, enabling the library to gain deeper insights into usage patterns, needs, and potential value of different user groups. In addition, the segmentation model allows for the identification of users who are likely to continue using library services as well as those at risk of reduced or discontinued usage in the future. The findings support strategic decision-making in library management, particularly in designing targeted activities, developing user-specific services, and enhancing user relationship management. Ultimately, this data-driven user segmentation approach contributes to improving service effectiveness, increasing user satisfaction, and sustaining long-term library user engagement.

Article Details

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Research Article

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