Churn and Customer Segmentation Analyses with Data Mining Techniques for a Bookstore Company
Ozlem Odabas1, Mustafa Cem Kasapbasi2
1Ozlem Odabas, Department of Computer Engineering, Istanbul Commerce University, Turkey.
2Mustafa Cem Kasapbasi, Department of Computer Engineering, Istanbul Commerce University, Turkey.
Manuscript received on 15 February 2016 | Revised Manuscript received on 20 February 2016 | Manuscript Published on 28 February 2016 | PP: 1-6 | Volume-5 Issue-9, February 2016 | Retrieval Number: I2262025916/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data mining, through piles of very large data is the process of obtaining meaningful data. Nowadays, rapidly developing technique. In this technique; data are grouped, classified according to the relationship, the model is created. In the last stage; the generated models reviewed. Impacts of data mining are widely used, one of the areas allocated to the customer analysis and segmentation of customers. In this study, bookstore customer groups and customer of segment showing the tendency to leave are analyzing; campaigns and marketing strategies that are appropriate to the groups identified. Classification techniques are used for Churn Analysis, clustering techniques are used for Customer Segmentation, and then the appropriate model was created. WEKA software was used to determine the model to be created.
Keywords: Data Mining, Churn Analysis, Customer Segmentation, Classification, Clustering.
Scope of the Article: Classification