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http://hdl.handle.net/2445/212808
Title: | How can entrepreneurs improve digital market segmentation? A comparative analysis of supervised and unsupervised learning algorithms |
Author: | Sáez Ortuño, Laura Huertas García, Rubén Forgas Coll, Santiago Puertas i Prats, Eloi |
Keywords: | Emprenedoria Màrqueting per Internet Segmentació de mercat Entrepreneurship Internet marketing Market segmentation |
Issue Date: | 5-Aug-2023 |
Publisher: | Springer Verlag |
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 cap tured 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 entre preneurs' commercial objectives |
Note: | Reproducció del document publicat a: https://doi.org/10.1007/s11365-023-00882-1 |
It is part of: | International Entrepreneurship and Management Journal, 2023, vol. 19, p. 1893-1920 |
URI: | http://hdl.handle.net/2445/212808 |
Related resource: | https://doi.org/10.1007/s11365-023-00882-1 |
ISSN: | 1554-7191 |
Appears in Collections: | Articles publicats en revistes (Empresa) |
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