IDEAS home Printed from https://ideas.repec.org/a/arp/tjssrr/2018p209-215.html
   My bibliography  Save this article

Probability Models for Assessing Effectiveness of Advertising Channels in the Internet Environment

Author

Listed:
  • Viktoriya I.Tinyakova*

    (Belgorod State University, Pobedy street 85, 208000 Belgorod, Russia)

  • Valeriy V. Davnis

    (Belgorod State University, Pobedy street 85, 208000 Belgorod, Russia)

  • Yaroslav B.Lavrinenko

    (Voronezh State Technical University, Moscow Avenue 14, 394000 Voronezh, Russia)

  • Larisa A.Shishkina

    (Voronezh State Agrarian University named after Emperor Peter the Great, Michurina street 1, 394087 Voronezh, Russia)

Abstract

Nowadays, marketing specialists simultaneously use several channels to attract visitors to websites. There is a difficulty in assessing not only the efficiency and conversion of each channel separately, but also in their interconnection. The problem occurs when users visit a website from several sources and only after that do the key action. To assess the effectiveness and selection of the most optimal channels, different models of attribution are used. The models are reviewed in the article. However, we propose to use multi-channel attribution, which provides an aggregate assessment of multi-channel sequences, taking into account that they are interdependent. The purpose of the paper is to create an attribution model that comprehensively evaluates multi-channel sequences and shows the effect of each channel on the conversion. The presented model of attribution can be based on the theory of graphs or Markov chains. The first method of calculation is more visual, the second (based on Markov chains) allows for work with a large amount of data. As a result, a model of multi-channel attribution was presented, which is based on Markov processes or graph theory. It allows for maximum comprehensive assessing of the impact of each channel on the conversion. On the basis of the two methods, calculations were carried out, confirming the adequacy of the model used for the tasks assigned.

Suggested Citation

  • Viktoriya I.Tinyakova* & Valeriy V. Davnis & Yaroslav B.Lavrinenko & Larisa A.Shishkina, 2018. "Probability Models for Assessing Effectiveness of Advertising Channels in the Internet Environment," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 209-215:5.
  • Handle: RePEc:arp:tjssrr:2018:p:209-215
    as

    Download full text from publisher

    File URL: https://www.arpgweb.com/pdf-files/spi5.47.209.215.pdf
    Download Restriction: no

    File URL: https://www.arpgweb.com/journal/7/special_issue/12-2018/5/4
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arp:tjssrr:2018:p:209-215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Managing Editor (email available below). General contact details of provider: http://arpgweb.com/?ic=journal&journal=7&info=aims .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.