Application research of data mining technology in customer relationship management

2021 ◽  
Author(s):  
Dili Song ◽  
Chunmei Liang
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Application Research ◽  
Mining Technology

The Research of Data Mining Technology in Customer Relationship Management

2014 ◽  
Vol 945-949 ◽  
pp. 3360-3363
Author(s):  
Tian Song ◽  
Guang Jian Chen ◽  
Yu Mei Luo
Keyword(s):  
Data Mining ◽  
Customer Satisfaction ◽  
Organizational Structure ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Technical Support ◽  
Customer Relationship ◽  
Mining Technology ◽  
Satisfaction Degree ◽  
Customer Satisfaction Degree

Customer relationship management (CRM) focuses on the customer and also aims to reestablish the organizational structure, optimize the business procedure as well as carry out the research upon the customer so as to enhance the customer satisfaction degree and improve the efficiency and profit of the enterprise. The technology of data mining has provided the powerful technical support for the CRM. This paper will make an analysis on the thinking of CRM, the procedure of data mining as well as the application of data mining to the CRM.

API Standardization Efforts for Data Mining

2011 ◽  
pp. 39-43
Author(s):  
Jaroslav Zendulka
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Customer Relationship Management ◽  
Real World ◽  
Relationship Management ◽  
Scientific Data ◽  
Customer Relationship ◽  
Mining Technology ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Data mining technology just recently became actually usable in real-world scenarios. At present, the data mining models generated by commercial data mining and statistical applications are often used as components in other systems in such fields as customer relationship management, risk management or processing scientific data. Therefore, it seems to be natural that most data mining products concentrate on data mining technology rather than on the easy-to-use, scalability, or portability. It is evident that employing common standards greatly simplifies the integration, updating, and maintenance of applications and systems containing components provided by other producers (Grossman, Hornick, & Meyer, 2002). Data mining models generated by data mining algorithms are good examples of such components.

scholarly journals Penerapan Algoritma Fuzzy C-Means Dipadukan Dengan Model Fuzzy Recency Frequency Monetary (RFM) Untuk Customer Relationship Management (CRM) (Studi Kasus Di TokoSweet Amirah Medan)

2019 ◽  
Vol 15 (1) ◽  
pp. 7
Author(s):  
Yohanni Syahra ◽  
Yusnidah Y ◽  
Beni Andika
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Fuzzy C Means

Konsumen merupakan aset yang sangat penting bagi perusahaan retail.Hal ini adalah alasan mengapa perusahaan retail harus merencanakan dan menggunakan strategi yang cukup jelas dalam memperlakukan konsumen.Dengan banyaknya jumlah konsumen yang dimiliki oleh suatu perusahaan retail, maka masalah yang dihadapi adalah bagaimana menentukan konsumen potensial.Dengan menerapkan konsep CRM (Customer Relationship Management), perusahaan dapat melakukan identifikasi konsumen potensial dengan melakukan segmentasi konsumen. Tujuan dari proses segmentasi konsumen adalah untuk mengetahui perilaku konsumen dan menerapkan strategi pemasaran yang tepat sehingga mendatangkan keuntungan bagi pihak perusahaan. Penelitian ini membahas tentang bagaimana proses data mining dari data konsumen di Toko Sweet Amirah, yaitu perusahaan retail yang khusus menjual perlengkapan dan peralatan bayi serta underwear pria dan wanita dewasa dan anak-anak dan berlokasi di Jalan Gedung Arca No. 29 B-C, Medan.ProsesData Miningini menggunakan data yang berasal dari data penjualan pada Toko Sweet Amirah dan bertujuan untuk mencari konsumen potensial.Model RFM merupakan model segmentasi yang umum digunakan pada perusahaan retail. Selanjutnya melakukan proses clustering menggunakan algoritma Fuzzy C-Means (FCM). Pada FCM jumlah cluster ditentukan. Hasil clustering dari algoritma tersebut digunakan untuk Aplikasidata miningmenggunakan MATLAB versi 7.10.0 dan memanfaatkan beberapatoolboxyaituFuzzy Logic ToolboxdanDatabase Toolbox

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