DocumentCode
260374
Title
Predicting and clustering customer to improve customer loyalty and company profit
Author
Alhilman, Judi ; Rian, M. Moch ; Marina, Wiyono ; Margono, Kuntjahjo
Author_Institution
Indusrial Eng. Fac., Telkom Univ., Bandung, Indonesia
fYear
2014
fDate
28-30 May 2014
Firstpage
331
Lastpage
334
Abstract
A PT X is a state-owned enterprise that provides the largest telecommunications services and network in Indonesia. By the growing challenges in the telecommunications industry, PT X must carefully take care of their customers by improving its services in order to make them satisfied and loyal. One of the effort that can be done by PT X is determining and predicting their customer´s category, so that PT X knows their customer´s behaviour and can better treat them. Some algorithms in data mining, as well as databases including customer data bank, usage, revenue, and payment were collected and merged to form a master data, are brought into play for this purpose using IBM SPSS Modeller software. The outcomes are customer´s category prediction and customer´s cluster who moved from productive (generating revenue) category to unproductive category (not generating revenue), and based on these outcomes we then recommend actions to be taken, like up selling, cross selling, customer education, switching to other packet best suited customer´s need.
Keywords
customer satisfaction; data mining; prediction theory; production engineering computing; profitability; telecommunication industry; IBM SPSS Modeller software; Indonesia; PT X; company profit; customer clustering; customer education; customer loyalty; customer prediction; data mining; state-owned enterprise; telecommunications services; Accuracy; Companies; Data mining; Data models; Databases; Predictive models; cross-selling; customer´s category; customer´s cluster; data mining; modelling; up selling;
fLanguage
English
Publisher
ieee
Conference_Titel
Information and Communication Technology (ICoICT), 2014 2nd International Conference on
Conference_Location
Bandung
Type
conf
DOI
10.1109/ICoICT.2014.6914087
Filename
6914087
Link To Document