• DocumentCode
    3208397
  • Title

    Analysis of the webpage advertising strategy based on the prediction model

  • Author

    Rui Yang

  • Author_Institution
    Sch. of Stat. & Math., Central Univ. of Finance & Econ., Beijing, China
  • fYear
    2015
  • fDate
    23-25 May 2015
  • Firstpage
    6345
  • Lastpage
    6350
  • Abstract
    The progress of science and technology and the rapid development of the new generation of internet integration have greatly promoted the emergence and development of the new media form - webpage advertising. Compared with the traditional media forms of advertising, webpage advertising can be more in-depth, more detailed, and more effective by using mathematical tools. This paper analyzes the factors influencing the effectiveness of the webpage advertising, introduces the principle of Probit model and grey system prediction model, and with delivery of songs as an example, they are applied in advertising, and finally the prediction model and advertising strategy are given in detail. At the end of the paper, we analyze advantages and disadvantages of two kinds of prediction methods, and further propose the combined forecasting method, which improves the disadvantages of two kinds of prediction methods. The given method is not only suitable for the advertisements with prior information, but also applicable to the advertisements with no prior information, that is to say, the method has good scalability.
  • Keywords
    Internet; Web sites; advertising; grey systems; prediction theory; Internet; Probit model; Web page advertising strategy; grey system prediction model; science and technology; Advertising; Companies; Forecasting; Mathematical model; Maximum likelihood estimation; Media; Predictive models; Combined Forecasting; Grey System Prediction Model; Probit Model; Webpage advertising;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (CCDC), 2015 27th Chinese
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4799-7016-2
  • Type

    conf

  • DOI
    10.1109/CCDC.2015.7161959
  • Filename
    7161959