• Customer Value Segmentation and Profiling with RFM

    Münevver TURANLI1, Zeyneb Hüsna AKBAL2

    1. Istanbul Commerce University, Faculty of Humanities and Social Sciences, Department of Statistics, Prof. Dr., mturanli@ticaret.edu.tr
    2. Istanbul Commerce University, Faculty of Humanities and Social Sciences, Department of Statistics, Master’s Degree with Thesis, Student, zhusna.akbal@istanbulticaret.edu.tr

    Summary:

    The development of information technologies and digital transformation in the world has led to a rapid increase in data. Analyzing this large amount of data, revealing the relationships between the data and the patterns within the data and making predictions about the future is possible with data mining.

    Predicting future values and revealing patterns among data are the main objectives of data mining. Data mining applications predict future trends and behaviors of companies by using their past data and clarify their future predictions.

    Data mining applications are carried out in cooperation with statisticians. There is a strong collaboration between statistics and data mining in the process of organizing and analyzing data and preparing it for use. Data mining improves the quality of studies for statisticians, presents the results to users and provides ease of application.

    Keywords: Data mining, Customer segmentation, Customer churn method.

    Pages: 89 – 96 | Full PDF Paper