Open AccessCustomer segmentation for churn management by using ant colony algorithm
Author(s) -
Batuhan Gulluoglu,
Evren Arifoglu,
Adem Karahoca,
Dilek Karahoca
Publication year - 2020
Publication title -
global journal of computer sciences
Language(s) - English
Resource type - Journals
ISSN - 2301-2587
DOI - 10.18844/gjcs.v10i1.4749
Subject(s) - ant colony optimization algorithms , gsm , euclidean distance , computer science , customer satisfaction , business , operator (biology) , segmentation , shortest path problem , cluster analysis , data mining , marketing , operations research , telecommunications , artificial intelligence , engineering , graph , biochemistry , chemistry , theoretical computer science , transcription factor , gene , repressor
It is extremely important for companies to set customer priorities and act in line with these priorities. The ant colony algorithm is used to perform customer segmentation. To do this, the shortest path approach was chosen. Besides, clustering is done by the Euclidean distance formula in the ant colony algorithm. The customer segmentation attributes are mostly related to the satisfaction factors, but some of them were eliminated by using ranker. These results are mostly related to the customer’s income, tenure, equip call card and reside. These attributes are the most important satisfaction factors not to lose customers as expected. There are many reasons in changing GSM operator for subscribers, and it is very important for companies to predict if subscriber will change GSM operator or not. For this reason, companies that give GSM services have to monitor subscribers’ behaviour and predict one step forward. In this study, changing subscribers’ GSM operator will be predicted by using data mining techniques.
Keywords: Ant colony, churn management, customer segmentation, data mining.
Categories: I.2.1, I.2