Customer Satisfaction Using Data Mining Approach

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 63 - 66, 26.12.2016

Burcu Oralhan , Kumru Uyar Zeki Oralhan

https://doi.org/10.18201/ijisae.266801

Cited By: 3

Öz

Customers and products are the main
assets for every business. Companies make their best to satisfy customers
because of coming back to their companies. After sales service related to
different steps that make customers are satisfied with the company service and
products. After sales service covers different many activities to investigate
whether the customer is satisfied with the service, products or not? Hence,
after sales service is acting very crucial role for customer satisfaction,
retention and loyalty. If the after sales service customer and services data is
saved by companies, this data is the key for growing companies.  Companies can add value their brand value
with the managing of this data. In this study, we aim to investigate effect of
6 factors on customer churn prediction via data mining methods. After sale
service software database is the source of our data. Our data source variables
are Customer Type, Usage Type, Churn Reason, Subscriber Period and Tariff  The data is examined by data mining program.
Data are compared 8 classification algorithm and clustered by simple K means
method. We will determine the most effective variables on customer churn
prediction. As a result of this research we can extract knowledge from
international firms marketing data.

Anahtar Kelimeler

Data Mining, Customer Churn Prediction, Customer Satisfaction, Knowledge Discovery in Database

Kaynakça

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