Customer Satisfaction Using Data Mining Approach

Abstract

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.

Keywords

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

References

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