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How can entrepreneurs improve digital market segmentation? A comparative analysis of supervised and unsupervised learning algorithms

Author

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  • Laura Sáez-Ortuño

    (Universitat de Barcelona)

  • Ruben Huertas-Garcia

    (Universitat de Barcelona)

  • Santiago Forgas-Coll

    (Universitat de Barcelona)

  • Eloi Puertas-Prats

    (Universitat de Barcelona)

Abstract

The identification of digital market segments to make value-creating propositions is a major challenge for entrepreneurs and marketing managers. New technologies and the Internet have made it possible to collect huge volumes of data that are difficult to analyse using traditional techniques. The purpose of this research is to address this challenge by proposing the use of AI algorithms to cluster customers. Specifically, the proposal is to compare the suitability of supervised algorithms, XGBoost, versus unsupervised algorithms, K-means, for segmenting the digital market. To do so, both algorithms have been applied to a sample of 5 million Spanish users captured between 2010 and 2022 by a lead generation start-up. The results show that supervised learning with this type of data is more useful for segmenting markets than unsupervised learning, as it provides solutions that are better suited to entrepreneurs’ commercial objectives.

Suggested Citation

  • Laura Sáez-Ortuño & Ruben Huertas-Garcia & Santiago Forgas-Coll & Eloi Puertas-Prats, 2023. "How can entrepreneurs improve digital market segmentation? A comparative analysis of supervised and unsupervised learning algorithms," International Entrepreneurship and Management Journal, Springer, vol. 19(4), pages 1893-1920, December.
    • Handle: RePEc:spr:intemj:v:19:y:2023:i:4:d:10.1007_s11365-023-00882-1
    DOI: 10.1007/s11365-023-00882-1
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