• DocumentCode
    2108052
  • Title

    Customer Segmentation Model Based on Retail Consumer Behavior Analysis

  • Author

    Han, Minghua

  • Author_Institution
    Fac. of Bus., Ningbo Univ., Ningbo
  • fYear
    2008
  • fDate
    21-22 Dec. 2008
  • Firstpage
    914
  • Lastpage
    917
  • Abstract
    Customer segmentation is the basis of the customer relationship management (CRM). For the retail business, customer segmentation through certain methods will help the good implementation of customer relationship management. The customer segmentation based on the purchase behavior may bean effective method of choice. Principal component analysis (PCA) is a method of multivariate statistical analysis. BP neural network is a multilayer feed forward neural network.In this study, we combined the advantages of principal component analysis and BP neural network and presented a customer segmentation model through analyzing the retail consumer behavior. Taking the results of PCA as the input of BP neural network, we used the VIP customer data of a retail business and verified the validity of the model.
  • Keywords
    backpropagation; consumer behaviour; customer relationship management; multilayer perceptrons; principal component analysis; retail data processing; backpropagation neural network; customer relationship management; customer segmentation model; multilayer feed forward neural network; multivariate statistical analysis; principal component analysis; retail business; retail consumer purchase behavior analysis; Business; Consumer behavior; Customer relationship management; Feedforward neural networks; Feeds; Information technology; Multi-layer neural network; Neural networks; Principal component analysis; Statistical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology Application Workshops, 2008. IITAW '08. International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-0-7695-3505-0
  • Type

    conf

  • DOI
    10.1109/IITA.Workshops.2008.225
  • Filename
    4732086