2026-04-17T11:27:58Zhttps://recercat.cat/oai/request
oai:recercat.cat:2445/2128082025-12-05T10:42:02Zcom_2072_1057col_2072_478811col_2072_478917
RECERCAT author Sáez Ortuño, Laura author Huertas García, Rubén author Forgas Coll, Santiago author Puertas i Prats, Eloi 2024-06-11T17:05:52Z2024-06-11T17:05:52Z2023-08-052024-06-11T17:05:57Z 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 cc by (c) Sáez Ortuño et al., 2023 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Emprenedoria Màrqueting per Internet Segmentació de mercat Entrepreneurship Internet marketing Market segmentation How can entrepreneurs improve digital market segmentation? A comparative analysis of supervised and unsupervised learning algorithms info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion