Browsing by Author "Aydin, Mehmet"

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Product and Customer Segmentation by Purchase Behavior in E-Commerce Platforms Using Stochastic Block Model
(Kadir Has Üniversitesi, 2022) Kafkas, Kenan; Aydin, Mehmet; Perdahçı, Ziya Nazım
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.
Using the Network-Based Theory for Monitoring Sustainable Interconnectedness on e-Business
(2020) Aydin, Mehmet; Perdahçı, Ziya Nazım
Online Interactive Platforms offer a variety of services including communication, information exchange, and connections that can create hundreds of thousands of interconnected users. Recent studies suggest that growth and sustainability of platfom users’ interconnectedness, that is, users' collective tendency to interconnectedness, are the two essential ingredients to succeed in such e-Businesses. This paper aims to explore the notion of “network correlation” in e-Business context in an attempt to provide managers of online interactive platforms with an analytical tool for monitoring growth and sustainability of users’ interconnectedness. We leverage Doktorsitesi.com as a digital trace data source and user metadata (role, gender) source to conduct both static and temporal network correlation analysis, and show that network correlations regarding the extent to which users are interconnected and network correlations regarding user metadata can help us to monitor and to suggest ways to quantitatively sustain growth of users’ interconnectedness. Taken together, our results suggest that network correlation analyses is capable of offering analytics to better describe, prescribe, and predict sustainable users’ interconnectedness on online interactive platforms. Accordinly, platform managers may consider the addition of a platform feature that helps newcomers with growing their interconnectedness based on the interconnectedness analytics.