Bibliographic citations
Marchena, P., Rodríguez, L. (2024). Implementación del método no supervisado PAM para la segmentación de clientes en una tienda virtual farmacéutica del año 2022 [Universidad Nacional de Trujillo]. https://hdl.handle.net/20.500.14414/21029
Marchena, P., Rodríguez, L. Implementación del método no supervisado PAM para la segmentación de clientes en una tienda virtual farmacéutica del año 2022 []. PE: Universidad Nacional de Trujillo; 2024. https://hdl.handle.net/20.500.14414/21029
@misc{renati/1045514,
title = "Implementación del método no supervisado PAM para la segmentación de clientes en una tienda virtual farmacéutica del año 2022",
author = "Rodríguez Sandoval, Lourdes Carolina",
publisher = "Universidad Nacional de Trujillo",
year = "2024"
}
Abstract Currently, companies are faced with the challenge of managing significant volumes of data, optimizing its use and converting it into highly valuable information that generates brilliant and relevant ideas for decision-making and devising beneficial strategies. Across various disciplines, a commonly used technique is cluster analysis; however, its most beneficial application is observed in the business field, addressing aspects such as customer segmentation or classification and the detection of trends in purchasing patterns. In this way, the case of the virtual pharmaceutical store addressed in this study, its focus on the customer, seeking to better understand them by exploiting the available data. We took on the work of data processing and analysis, proposing an RFM segmentation using the unsupervised PAM method and this approach allowed us to group a specific set of target customers, offering a deeper understanding of them. The unsupervised PAM method has been used, which follows an iterative partitioning process similar to the k-means algorithm. However, unlike the latter, PAM uses the median instead of the mean as the center point in the process (centroid) because the mean is more sensitive to outliers compared to the median. Data analysis and processing was carried out using the Python language in Jupyter, identifying 6 customer segments: Possible loyal, Satisfied, Maturation, Reluctant, New customer and Potential inactive. These segments will provide the company with an understanding of the transactional behavior of its customers that will serve as input in future decision-making
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