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 Book Chapter Faculty Publications- Book Chapter 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 Solar, Tanvir Singh Title A name given to the resource Using Machine Learning Algorithms to Personalize Customer Experience in Ghost Kitchens: Hyper-Personalized Marketing and Promotion 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 Impact of AI and the Evolution of Future Ghost Kitchens;pp.311-358 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-0432-8.ch016" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-0432-8.ch016</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004540410?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004540410?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 Solar T.S., Christ University, Delhi, India Description An account of the resource The emergence of ghost kitchens has revolutionized the food delivery industry by leveraging machine learning algorithms to enhance customer satisfaction and personalized experiences. This chapter, examines how predictive analytics identifies customer preferences, helping ghost kitchens create effective marketing strategies aligned with tastes and behaviours. It highlights real-time personalization, where offers are tailored based on past orders, location, and time, fostering relevance and loyalty. AI-driven customer segmentation is explored as a vital tool for precise targeting. At the same time, the chapter also addresses how AI assesses campaign performance to refine marketing tactics and adapt to changing demands. This research adds new fresh knowledge to the established body of knowledge in the context of restaurant food consumption behavior (Maziriri, E. T., Rukuni, T. F., &amp; Chuchu, T. (2021)). This chapter is going to explore how AI advancements revolutionize resource utilization, evolving customer preferences, sustainable growth. 2025 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833730434-2; 979-833730432-8; Language A language of the resource English Type The nature or genre of the resource Book chapter 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