Use of Data Mining for Prediction of Customer Loyalty

• Main Authors: Andri Wijaya, Abba Suganda Girsang
• Format: Article
• Language: English
• Published: Bina Nusantara University 2015-05-01
• Series: CommIT Journal
• Subjects: Customer loyalty; Attribute analysis; C4.5; Naiv¨e Bayes; Nearest Neighbor Algorithmghbor algorithms
• Online Access: https://journal.binus.ac.id/index.php/commit/article/view/1660

This  article  discusses  the  analysis  of  customer  loyalty  using  three  data  mining  methods:  C4.5,Naive Bayes, and Nearest Neighbor Algorithms and real-world  empirical  data.  The  data  contain  ten  attributes related to the customer loyalty and are obtained from a national  multimedia  company  in  Indonesia.  The  dataset contains 2269 records. The study also evaluates the effects of  the  size  of  the  training  data  to  the  accuracy  of  the classification.  The  results  suggest  that  C4.5  algorithm produces   highest classification   accuracy   at   the   order of  81%  followed  by  the  methods  of  Naive  Bayes  76% and  Nearest  Neighbor  55%.  In  addition,  the  numerical evaluation  also  suggests  that  the  proportion  of  80%  is optimal  for  the  training  set.