Augmenting Advertiser Decision Support with Generative AI and Interactive Analytics

Augmenting Advertiser Decision Support with Generative AI and Interactive Analytics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Augmenting Advertiser Decision Support with Generative AI and Interactive Analytics Shujian Chen, Minhui Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6528623/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the competitive landscape of advertising, decision-making can be complex and multifaceted. To support advertisers in navigating this landscape, we introduce a novel framework that integrates Generative AI with interactive analytics. This innovative approach aids in enhancing decision support by delivering actionable data insights and enabling automated content generation tailored to specific marketing campaigns. By merging user-friendly analytics tools that facilitate data visualization with Generative AI's capabilities, advertisers can access real-time data to inform their strategies. This system not only enhances efficiency in decision-making but also promotes personalized marketing content creation that captures audience interest more effectively. Comprehensive evaluations highlight the framework's effectiveness in improving advertising performance and user engagement. The integration of Generative AI with interactive analytics tools marks a significant advancement in the advertising sector, providing marketers with the resources needed to effectively tackle intricate decision-making challenges. Theoretical Computer Science Augmenting Advertiser Recommend System Data Visualization Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6528623","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447983576,"identity":"fd71707d-e10c-4ebd-ac4f-3467d0e1a538","order_by":0,"name":"Shujian Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYHCC9A8fKmwYGNgZGJgZDEACCQS1PGOccSYNpJ5oLYzPmHnbDkO1MBChRb79cNpjnjPn5fmZeUw3FxTYMfCz5xjg1WJwJi3dcE7FbcOZzTxmt2cYJDNI9rwhoEWCJ0HizZnbCQaHgVp4DIDeuUHAFvkZ/B8keNvOwbTUM9gT0sJwgyFNkrftAEzLYaC9BP2SkGw440wy0C9sZUC/HOeROPOsAL/D2g8kPvhQYSfPz9687XbBn2o5/vbkDfgdhg54SFM+CkbBKBgFowArAACDTEVMrOuWDAAAAABJRU5ErkJggg==","orcid":"","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Shujian","middleName":"","lastName":"Chen","suffix":""},{"id":447983577,"identity":"1c63bd20-5533-47e5-a6a6-b6ecee0489df","order_by":1,"name":"Minhui Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACZgY2BgYDBjkGdhDPACrKQ4QWYyCDWC0MIC0MDIkNzMhi+LTotvMee8xTcDi9v5nHdHNBAYO87owExgdv23BrMTvMl27MY5CWO+Mwj9ntGQYMhttuJDAbzsWrhcdMmsfAJrcBpIXHgCHB7EYCmzQvYS0S6fJIWth/E6HFJsEA2RZmQlok5xikGW48zFYG1CJhuO3Mw2bJOefwaDl/xkzizZ/D8nLHm7fd5vljI292PPnghzdluLWgAwkgZmwgXv0oGAWjYBSMAqwAAOvGRN0OgpCnAAAAAElFTkSuQmCC","orcid":"","institution":"Carnegie Mellon University","correspondingAuthor":true,"prefix":"","firstName":"Minhui","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2025-04-25 11:43:28","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6528623/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6528623/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81501065,"identity":"4925c123-d80d-4956-8622-6feca936c255","added_by":"auto","created_at":"2025-04-28 04:06:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":451962,"visible":true,"origin":"","legend":"","description":"","filename":"AugmentingAdvertiserDecisionSupportwithGenerativeAIandInteractiveAnalytics6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6528623/v1_covered_6fda5aef-9ddf-4e36-82d3-bd07baf1e133.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAugmenting Advertiser Decision Support with Generative AI and Interactive Analytics\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Augmenting Advertiser, Recommend System, Data Visualization","lastPublishedDoi":"10.21203/rs.3.rs-6528623/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6528623/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the competitive landscape of advertising, decision-making can be complex and multifaceted. 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