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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



The Impact Of Social Network Analysis On Telecommunication Customers

[Full Text]

 

AUTHOR(S)

Ramiz Assaf

 

KEYWORDS

Social Network Analysis; SNA; Authority; Dissemination; SPSS Molder; data mining; Data Analysis; SQL: SPSS Modeler.

 

ABSTRACT

Telecommunication industry has proven itself as a chain of socio-economic impact. Market leaders should take advantage of data analysis consecutively as the need to sustain customers the longest possible time. This research is based on community detection analysis through identifying group members and influencers in the telecommunication industry. It is motivated by two major research questions: Who are the influencers in subscribers for churn and new products adoptions? (2) Who are subscribers whose loyalty is threatened by churn around them? The use of data analysis and data mining algorithms and standard process in today’s challenges was set to increase the efficiency of solving not only technical problems but business as to move toward insights and analysis based exercises. This research proposes a model based on the Similarity, Authority and Hub algorithms used in Social Network Analysis (SNA) to group members who share same patterns together, to identify network leaders in terms of authority and dissemination leaders and to extract group level insights that can be used in understanding subscribers’ patterns and ways of communications (in-degrees and out-degrees) between them using IBM SPSS Modeler as the base of data mining process and Oracle as the base of corporate data warehouse. This study estimated how much reliable is to use SNA in order to shift from the traditional ways of targeting subscribers, understanding their pattern, enhancing offering efficiency and improving the process of launching new product by selecting the right target. It has contributed to shifting marketing leaders’ way of thinking from individual view to network view. It has also contributed to providing decision makers with a systematic and scientific approach as to focus on network influencers, to save as much as they can and to build their offers based on it.

 

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