Yazarlar

• Ayçin, Ejder(1)
• Bulut, Zeki Atil(1)
• Dogan, Onur(1)
• Esmeray, Furkan(1)
• Karabatak, M.(1)
• Korkmaz, Sevcan Aytac(1)
• Varol, A.(1)
• Varol, C.(1)

• MakaleYalnız künye2018/r/munzur/4932
  CUSTOMER SEGMENTATION BY USING RFM MODEL AND CLUSTERING METHODS: A CASE STUDY IN RETAIL INDUSTRY

  Dogan, Onur; Ayçin, Ejder; Bulut, Zeki Atil

  Companies need to understand the customers' data better in all aspects. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities to customers became very important for customer-company engagement. Segmenting the customers according to their data became vital in this context. RFM (recency, frequency and monetary) values have been used for many years to identify which customers valuable for the company, which customers need promotional activities, etc. Data-mining tools and techniques widely have been used by organizations and individuals to analysis their stored data. Clustering, which one of the tasks of data mining has been used to group people, objects, etc. In this paper we propose two different clustering models to segment 700032 customers by considering their RFM values. We detected that the current customer segmentation which built by just considering customers' expense is not sufficient. Hence, models that recommended in this research are expected to provide better customer understanding, well-designed strategies, and more efficient decisions.

  AtıfWoS30

  İncele
• BildiriYalnız künye2018/r/munzur/3789
  Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering method

  Korkmaz, Sevcan Aytac; Esmeray, Furkan; Varol, A.; Karabatak, M.; Varol, C.

  In this article, the lignite coal datas in the Kalburcayi area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburcayi area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT-ANN, DWT-ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT-ANN method.

  AtıfScopus8·OpenAlex12

  İncele