Integrating Hybrid Clustering Techniques for Advanced Retail Segmentation

Integrating Hybrid Clustering Techniques for Advanced Retail Segmentation | 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 Integrating Hybrid Clustering Techniques for Advanced Retail Segmentation Dhimesh Parmar, Paresh Tanna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6152962/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 Customer segmentation theatres a key role in retail, permitting businesses to adapt marketing policies, optimize reserve allocation, and expand general customer completion. Traditional clustering methods, such as K-means and DBSCAN, have been broadly employed for this drive. However, each of these methods has its limitations, such as compassion to noise in DBSCAN or the statement of round clusters in K-means. To address these boundaries, this paper proposes a hybrid clustering approach that mixes multiple clustering methods, including K-means, DBSCAN, Spectral Clustering, Fuzzy C-means, and Hierarchical Clustering. By combination the resources of these methods, the hybrid approach distributes more careful and sensitive segmentation results, exclusively in complex retail atmospheres where customer behaviour is assorted and data structures are involved. The results establish that the hybrid approach knowingly improves division truth compared to individual methods. The combined clustering techniques allow the model to detect both distinct, well-separated clusters and more nuanced, overlapping segments, providing a more comprehensive view of customer behaviors. This enhanced segmentation authorizes retailers to make data-driven decisions, such as better-targeted marketing operations, more efficient record management, and personalized customer experiences. The paper concludes by discussing the potential for further research in applying hybrid clustering models in other sectors and integrating predictive analytics for even more refined customer insights. Hybrid Clustering Customer Segmentation Cluster Evaluation Customer Insights Retail Optimization Cluster Optimization Full Text Additional Declarations No competing interests reported. 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. 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-6152962","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":426990751,"identity":"94637284-b49e-4136-af3e-7d45bea01389","order_by":0,"name":"Dhimesh Parmar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDCCAxBKjp+9+QBjA5gNpQhpMZbsOZZAmpbEDTdyDAiphQC+283PPt2ouZfYcCPn28eZbQzy/A3MbQ/waZG8c8x4ds6xYuPGnrebZ25sYzCccYCx3QCfFoMbCcbMOWwJss3suZsZH7YxMG5gYGyTwK8l/TNzzr8ExjaGnMcgLfZEaMkxZs5tS1Ds4chhZgQ6LJGgFsk7Z4qZc/sSjCV4jhkzzjgnkTzjMAEtfLfbNzPnfEuQsz/e/Jixp8zGtr+9/RleLQwSGFxmvOoxtYyCUTAKRsEowAQAP0pNHB7yKvEAAAAASUVORK5CYII=","orcid":"","institution":"RK University","correspondingAuthor":true,"prefix":"","firstName":"Dhimesh","middleName":"","lastName":"Parmar","suffix":""},{"id":426990752,"identity":"167c338d-1392-492d-b5fd-05187dea6d4d","order_by":1,"name":"Paresh Tanna","email":"","orcid":"","institution":"RK University","correspondingAuthor":false,"prefix":"","firstName":"Paresh","middleName":"","lastName":"Tanna","suffix":""}],"badges":[],"createdAt":"2025-03-04 09:23:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6152962/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6152962/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78365833,"identity":"9eb37f6a-02ef-471f-b4d2-1180effe689e","added_by":"auto","created_at":"2025-03-12 13:14:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":568821,"visible":true,"origin":"","legend":"","description":"","filename":"IntegratingHybridClusteringTechniquesforAdvancedRetailSegmentation01.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6152962/v1_covered_96750966-58b8-4d05-afe5-06f7b53c5748.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Hybrid Clustering Techniques for Advanced Retail Segmentation","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":"Hybrid Clustering, Customer Segmentation, Cluster Evaluation, Customer Insights, Retail Optimization, Cluster Optimization","lastPublishedDoi":"10.21203/rs.3.rs-6152962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6152962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCustomer segmentation theatres a key role in retail, permitting businesses to adapt marketing policies, optimize reserve allocation, and expand general customer completion. 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