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title

Data mining for mobile app users identify based on improved RFM model in a case of social network

Credit to Download: 1 | Page Numbers 10 | Abstract Views: 123
Year: 2017
COI code: IIEC14_040
Paper Language: English

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Authors Data mining for mobile app users identify based on improved RFM model in a case of social network

  Amir Mashayekhi - Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  Maryam Amir Haeri - Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran
  Ali Azadeh - Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract:

Despite the high demand and the accelerated growth of mobile apps during the last years, only a few studies have been done to the multi-dimensional evaluation of the app users. In this paper, the RFM General Model refers to the critical gap in the literature and illustrates how the users of mobile apps behave in terms of novelty, frequency, and financial from both the messaging and financial perspective. Clustering and ranking of mobile app users have been developed in the social networking market for the first time. This study examines a wide range of customers with different characteristics in the same cluster categories and then ranks them with a simple weighting method. The most important results of the research are the three clusters of customers, with only 814 customers, that consists only 0.04% of the total customers, and in fact are the best and most profitable customers for the company. They involve 2% of total transactions and 5% of total messaging. The second group, which consists 20.6% of the total customers and involve 10% and 15% of total transactions and messaging, may lead to competing transactions. Finally, the third group, with 76% of the total customers, has a moderate downward behavior of every two perspectives, and the weakness of the users of this cluster is clearly evident. The most important usages of this article are the explanation of specific marketing policies for each cluster.

Keywords:

App users; RFML analysis; Data mining; Customer segmentation; SAW ranking

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COI code: IIEC14_040

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Mashayekhi, Amir; Maryam Amir Haeri & Ali Azadeh, 2017, Data mining for mobile app users identify based on improved RFM model in a case of social network, 14th International Industrial Engineering Conference, تهران, انجمن مهندسي صنايع ايران - دانشگاه علم و صنعت ايران, https://www.civilica.com/Paper-IIEC14-IIEC14_040.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Mashayekhi, Amir; Maryam Amir Haeri & Ali Azadeh, 2017)
Second and more: (Mashayekhi; Amir Haeri & Azadeh, 2017)
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Type: state university
Paper No.: 55403
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