Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource George, Jossy P.; Muttungal, Peter V.; Davidson, Benny Godwin J. Title A name given to the resource AI and Big Data: Harnessing Data Science for Enhanced Consumer Insights Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.293-300 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4880-1_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4880-1_23</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019300718?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019300718?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant George J.P., School of Business and Management, Christ University, Bangalore, India; Muttungal P.V., Department of Psychology, Christ University, Bangalore, India; Davidson B.G.J., School of Business, University of the Fraser Valley, Abbotsford, BC, Canada Description An account of the resource Data science is revolutionizing modern marketing strategies. These strategies enhance consumer intelligence and streamline decision-making processes. Data science techniques are helping businesses to get deep insights into consumer behavior, preferences, and emerging trends. In this paper, we focus on how the trident of artificial intelligence, machine learning, and quantum computing is reshaping marketing practices. Additionally, we focus on how artificial intelligence, machine learning, and quantum computing will impact data processing capabilities in future. The paper emphasizes the need for responsible data practices and discusses ethical issues such as data privacy and algorithmic bias. Several case studies focused on personalized marketing to improve customer satisfaction for companies like Netflix, Amazon, and Spotify are used. The findings suggest that businesses aiming to stay competitive will need to integrate data science in the complex data-driven world. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Consumer Intelligence; Data Science; Hyper-personalization; Machine Learning; Marketing Strategy; Predictive Analytics Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964879-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online