AIM² Framework for Smart Marketing Innovation: AI-Driven Consumer Analytics Using SOR, Neural Networks, and XGBoost in Saudi Retail

AIM² Framework for Smart Marketing Innovation: AI-Driven Consumer Analytics Using SOR, Neural Networks, and XGBoost in Saudi Retail | 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 Article AIM² Framework for Smart Marketing Innovation: AI-Driven Consumer Analytics Using SOR, Neural Networks, and XGBoost in Saudi Retail Fawaz Khaled Alarfaj, Mohamed Badouch, Hikmat Ullah Khan, Mehdi Boutaounte This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8295953/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract The study introduces the AIM² (AI-Integrated Marketing Innovation Model) framework by integrating the Stimulus–Organism–Response (SOR) model with advanced machine learning methods for making sense of consumer analytics in Saudi retail. Using real data from Tamimi Markets, clustering methods put products into budget, intermediate, and luxury categories with a 92% silhouette score. Predictive analysis showed that XGBoost had a 14% smaller error margin than traditional regression and 9% more accuracy than simple Neural Networks. These results go beyond the current retail analytics methods that report less than 80% accuracy and highlight the value of incorporating AI-powered techniques with SOR. The study contributes to both theory and practice by demonstrating the AIM² framework in a real retail context and providing practical tips for retailers who want to keep up with the modern marketing goals. Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files Table2.docx Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Jan, 2026 Reviews received at journal 12 Jan, 2026 Reviews received at journal 10 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviews received at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor invited by journal 26 Dec, 2025 Editor assigned by journal 21 Dec, 2025 Submission checks completed at journal 21 Dec, 2025 First submitted to journal 06 Dec, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8295953","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":571707343,"identity":"2a3b99aa-f199-4ef8-946c-8c96be5b4028","order_by":0,"name":"Fawaz Khaled 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