Fabian, Jerrick and Saraswati, Triarti and Awibowo, Setijo (2017) Predicting Customer Churn From Valuable Product Support Customers Of PT XYZ. Bachelor thesis, Swiss German University.

Preview	Text Jerrick Fabian 11307022 TOC.pdf Download (777kB) | Preview
	Text Jerrick Fabian 11307022 1.pdf Restricted to Registered users only Download (510kB)
	Text Jerrick Fabian 11307022 2.pdf Restricted to Registered users only Download (801kB)
	Text Jerrick Fabian 11307022 3.pdf Restricted to Registered users only Download (896kB)
	Text Jerrick Fabian 11307022 4.pdf Restricted to Registered users only Download (1MB)
	Text Jerrick Fabian 11307022 5.pdf Restricted to Registered users only Download (500kB)
Preview	Text Jerrick Fabian 11307022 Ref.pdf Download (427kB) | Preview

Abstract

Indonesia is ranked 10th of total coal resources in the world, making it one of the biggest coal exporter and producer. PT. XYZ contributes as the leading heavy equipment and machineries holding company in the country with most mining industries and practitioners as its customers. The hottest commodity among the company’s customers is coal mining which in 2015 suffered from the plummeted of coal price, the lowest ever recorded. This condition led mining customers to cut down production costs leading some of them to churn from the company, especially in product support services sector. To study further the cause of customer churn and its corresponding predictors, data mining is performed through this thesis to predict the possibility of churn customers. The separation of valuable customers is first applied to ensure the accuracy of the result using LRFM model and decision tree as the classification technique of data mining.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Decision Tree; LRFM Model; Data Mining; Product Support Services; Customer Churn
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) > T55.4 Industrial engineering. Management engineering
Divisions:	Faculty of Engineering and Information Technology > Department of Industrial Engineering
Depositing User:	Astuti Kusumaningrum
Date Deposited:	10 May 2020 14:20
Last Modified:	10 May 2020 14:20
URI:	http://repository.sgu.ac.id/id/eprint/230

Actions (login required)

View Item