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A Combined Approach of Clustering And Association Rule Mining For Customer Profiling

Diğer Başlık: A Combined Approach of Clustering And Association Rule Mining For Customer Profiling

Oluşturulma Tarihi: 08-12-2020

Niteleme Bilgileri

Tür: Tez

Alt Tür: Doktora

Yayınlanma Durumu: Yayınlanmamış

Dosya Biçimi: PDF

Dil: İngilizce

Konu(lar): TEKNOLOJİ,

Yazar(lar): Güney, Sinem (Yazar),

Emeği Geçen(ler): Turhan , Çiğdem (Tez Danışmanı), Peker, Serhat (Tez Danışmanı),


Yayın Tarihi: 29-06-2020


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Anahtar Kelimeler

Data Mining, CRM, Customer Behavior, Customer Segmentation, Customer Profiling, Association Rule Mining, Marketing Strategies, IPTV Service Provider, Video on Demand Services, RFM Model


Özet

Today, IPTV (Internet Protocol Television) service providers offer VoD (Video on Demand) services as part of their business initiative toward generating more revenue. To do this, they need to know about customer behaviors and expectations. Such information related to users is stored in CRM (Customer Relationship Management) systems. Against this backdrop, the present work aims to analyze customers in VoD services with applying clustering and Association Rule Mining techniques. The LRFMP (Length, Recency, Frequency, Monetary, and Periodicity) model is applied to find out the customer behaviors, whereas the kmeans clustering algorithms allow for determining the number of clusters and customer profiles. As a result, four different customer groups are identified, namely as “consuming and most valuable”, “less consuming and less valuable”, “less consuming but loyal”, and “neither loyal nor valuable”. A major source of information for this study is the content type or genre as regards the content category and rental preferences of subscribers. To this end, the association rule algorithm (Apriori) is employed to predict the customers’ potential rentals. A combined approach as such would be useful for IPTV service providers to further shed light on precise customer behaviors and preferences, thus allowing to create more targeted marketing strategies for each category of subscribers in order to improve customer satisfaction and increase revenues in the long run.


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