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

Laura Sáez-Ortuño (), Ruben Huertas-Garcia (), Santiago Forgas-Coll () and Eloi Puertas-Prats ()
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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

International Entrepreneurship and Management Journal, 2023, vol. 19, issue 4, No 13, 1893-1920

Abstract: 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.

Keywords: Digital marketing; Clusters; AI algorithms; Unsupervised algorithms; Supervised algorithms; XGBoost; K-means (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11365-023-00882-1

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