{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T02:39:27Z","timestamp":1769913567307,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing\u2019s unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace.<\/jats:p>","DOI":"10.3390\/informatics11020019","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T10:24:26Z","timestamp":1713176666000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Machine Learning as a Service (MLaaS) Approach to Improve Marketing Success"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5440-3225","authenticated-orcid":false,"given":"Ivo","family":"Pereira","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, University Fernando Pessoa, 4249-004 Porto, Portugal"},{"name":"E-goi, 4450-190 Matosinhos, Portugal"},{"name":"ISRC\u2014Interdisciplinary Studies Research Center, ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0264-4710","authenticated-orcid":false,"given":"Ana","family":"Madureira","sequence":"additional","affiliation":[{"name":"ISRC\u2014Interdisciplinary Studies Research Center, ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1767-8240","authenticated-orcid":false,"given":"Nuno","family":"Bettencourt","sequence":"additional","affiliation":[{"name":"ISRC\u2014Interdisciplinary Studies Research Center, ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2665-8057","authenticated-orcid":false,"given":"Duarte","family":"Coelho","sequence":"additional","affiliation":[{"name":"E-goi, 4450-190 Matosinhos, Portugal"},{"name":"ISRC\u2014Interdisciplinary Studies Research Center, ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0786-3362","authenticated-orcid":false,"given":"Miguel \u00c2ngelo","family":"Rebelo","sequence":"additional","affiliation":[{"name":"E-goi, 4450-190 Matosinhos, Portugal"},{"name":"i3s, Rua Alfredo Allen 208, 4200-135 Porto, Portugal"}]},{"given":"Carolina","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"E-goi, 4450-190 Matosinhos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8225-5984","authenticated-orcid":false,"given":"Daniel Alves","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"E-goi, 4450-190 Matosinhos, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"ref_1","unstructured":"Statista (2023, November 03). 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