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Users Segmentation Based on Google Analytics Income Using K-Means

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Information and Communication Technologies (TICEC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1456))

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Abstract

Business intelligence (BI) is a set of techniques and practices to extract, transform and handle large volumes of data from a business, for analyzing and turning them into actionable business insights, to improve the profit and the most important is getting to know the customers better. Market segmentation (MS) is one of the BI strategies which converts the heterogeneous market into homogeneous subgroups, with similar characteristics that will define the marketing strategies. There are machine learning techniques that allow the most efficient segmentation in the market. One of these techniques is the k-means, which is an unsupervised learning technique that allows the segmentation of a data set from the Euclidean distances between each data and the centroids established by the method. This research aims to segment the customers of a web service based on the behavioral characteristics of the users. A company dedicated to helping small businesses and new entrepreneurs increase their customer base through the use of digital marketing strategies provides a database, which registers user behavior through events on their web application. A k-means method was applied to segment our client’s list based on their interactions with the application. The k-means performed segmentation of 15 clusters that were organized into four groups, the most important group reported a very varied behavior pattern of usage of the web service, the 64.38% of clients are within this group, which suggests that the client’s profile could be attached to the characteristics of this group.

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Acknowledgment

IMOLKO C.A. financed a part of this research work, as did the Research and Development Department of the Simón Bolivar University (DID-USB) and the Research Department of the Ibagué University.

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Authors and Affiliations

  1. Faculty of Engineering, Universidad de Ibagué, Ibagué, Tolima, Colombia

    Alexandra La Cruz

  2. Dept. of Thermodynamics and Transfer Phenomena, Universidad Simón Bolívar, Caracas, Venezuela

    Erika Severeyn

  3. Imolko.com, Panamá, Panama

    Roberto Matute & Juan Estrada

Authors
  1. Alexandra La Cruz
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  2. Erika Severeyn
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  3. Roberto Matute
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  4. Juan Estrada
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Corresponding author

Correspondence to Alexandra La Cruz .

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Editors and Affiliations

  1. Politecnica Salesiana University, Cuenca, Ecuador

    Juan Pablo Salgado Guerrero

  2. Universidad Técnica Particular de Loja, Loja, Ecuador

    Janneth Chicaiza Espinosa

  3. Politecnica Salesiana University, Cuenca, Ecuador

    Mariela Cerrada Lozada

  4. CEDIA, Cuenca, Ecuador

    Santiago Berrezueta-Guzman

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La Cruz, A., Severeyn, E., Matute, R., Estrada, J. (2021). Users Segmentation Based on Google Analytics Income Using K-Means. In: Salgado Guerrero, J.P., Chicaiza Espinosa, J., Cerrada Lozada, M., Berrezueta-Guzman, S. (eds) Information and Communication Technologies. TICEC 2021. Communications in Computer and Information Science, vol 1456. Springer, Cham. https://doi.org/10.1007/978-3-030-89941-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-89941-7_16

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  • Print ISBN: 978-3-030-89940-0

  • Online ISBN: 978-3-030-89941-7

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