• DocumentCode
    2603231
  • Title

    Construction and testing of customer value identification model

  • Author

    Dai-zhi, Jin ; Chun-xia, Wang

  • Author_Institution
    Harbin Univ. of Commerce, Harbin, China
  • fYear
    2010
  • fDate
    24-26 Nov. 2010
  • Firstpage
    595
  • Lastpage
    601
  • Abstract
    Customer value is the value that based on customer\´s perceived value, which is a comprehensive comparison between perceived benefit and perceived cost. There are closely relationship of customer value and enterprise performance. The improvement of customer value contribute to enterprise performance. Based on the above theoretical analysis, the customer value was divided into 3 types that is "good", "medium" and "poor". We choose customer value scale indicators that has passed pre-tested as input nodes, and built the customer value identification model based on enterprise performance with BP neural network. The data of food (beverage) industry that include 34 listed companies financial report and questionnaire were selected. The data were input to the model we built, and simulation was acted. The result show that the identify correct rate of the model was high, and it is suit for customer value identification.
  • Keywords
    backpropagation; beverage industry; customer profiles; customer satisfaction; neural nets; BP neural network; beverage industry; customer perception; customer relationship; customer value identification model; enterprise performance; food industry; perceived benefit; perceived cost; Artificial neural networks; Biological system modeling; Companies; Industries; Mathematical model; Reliability; Testing; BP neural network; customer value; enterprise performance; identification model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering (ICMSE), 2010 International Conference on
  • Conference_Location
    Melbourne, VIC
  • ISSN
    2155-1847
  • Print_ISBN
    978-1-4244-8116-3
  • Type

    conf

  • DOI
    10.1109/ICMSE.2010.5719863
  • Filename
    5719863