Product and Customer Segmentation by Purchase Behavior in E-Commerce Platforms Using Stochastic Block Model

dc.contributor.advisorAydin, Mehmeten_US
dc.contributor.advisorPerdahçı, Ziya Nazımen_US
dc.contributor.authorKafkas, Kenan
dc.date2022-06
dc.date.accessioned2023-07-26T13:49:13Z
dc.date.available2023-07-26T13:49:13Z
dc.date.issued2022
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, İşletme Ana Bilim Dalıen_US
dc.description.abstractTo attract and maintain lucrative clientele, commercial internet platforms compete with a multitude of competitors by providing appropriate goods and services, em ploying a range of marketing methods to get a competitive edge utilizing their digital trace data. Techniques include a variety of marketing tactics, many of which are based on updated versions of conventional marketing strategies. Working out what consumers want and how to meet their needs is an ongoing task on these platforms. The literature is constantly being enhanced by new theoretical and practical applica tions. Customer purchase behavior leaves digital trace data in online platforms such as clickstream, transaction, or product review forms. This thesis proposes a model that presents a novel network approach to customer behavior analytics on online transaction data to perform product and customer segmentation. We seek answers to the following research questions: Can we understand the customer behavior and preferences through network analysis? If there are several purchase behavior types, what are the underlying patterns? Are there certain special products that play a special role in the network? To support decision-makers in their endeavor to improve marketing activities such as targeted advertising, increasing brand loyalty, attract ing desired customers, and signaling more effective marketing messages. We utilize the Stochastic Block Model (SBM), which is a statistically principled community detection method on co-purchase networks to discover latent product communities, and we produce two different segmentation methods based on those communities. The outcome is a product and a customer segmentation which extends traditional data mining methods. We combine product based segmentation with Market Bas ket Analysis and customers segmentation with the RFM models. We implement our model on two empirical data sets. Lastly, we provide an executive summary for both examples.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/4404
dc.identifier.yoktezid739387en_US
dc.language.isoenen_US
dc.publisherKadir Has Üniversitesien_US
dc.relation.publicationcategoryTezen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCustomer Segmentationen_US
dc.subjectStochastic Block Modelen_US
dc.subjectCo-purchase Networken_US
dc.subjectCommunity Detectionen_US
dc.subjectDiversityen_US
dc.titleProduct and Customer Segmentation by Purchase Behavior in E-Commerce Platforms Using Stochastic Block Modelen_US
dc.typeDoctoral Thesisen_US
dspace.entity.typePublication

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