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

dc.contributor.advisor Aydin, Mehmet en_US
dc.contributor.advisor Perdahçı, Ziya Nazım en_US
dc.contributor.author Kafkas, Kenan
dc.date 2022-06
dc.date.accessioned 2023-07-26T13:49:13Z
dc.date.available 2023-07-26T13:49:13Z
dc.date.issued 2022
dc.department Enstitüler, Lisansüstü Eğitim Enstitüsü, İşletme Ana Bilim Dalı en_US
dc.description.abstract To 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.uri https://hdl.handle.net/20.500.12469/4404
dc.identifier.yoktezid 739387 en_US
dc.language.iso en en_US
dc.publisher Kadir Has Üniversitesi en_US
dc.relation.publicationcategory Tez en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Customer Segmentation en_US
dc.subject Stochastic Block Model en_US
dc.subject Co-purchase Network en_US
dc.subject Community Detection en_US
dc.subject Diversity en_US
dc.title Product and Customer Segmentation by Purchase Behavior in E-Commerce Platforms Using Stochastic Block Model en_US
dc.type Doctoral Thesis en_US
dspace.entity.type Publication

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