• DocumentCode
    151865
  • Title

    Advertising.com: Mobile optimization and predictive segments

  • Author

    Strauss, Adam ; Hayes, Roy ; Leslie, Christopher ; Stettinius, Elizabeth ; Tewari, Suchismita ; Valeiras, James ; Scherer, William

  • Author_Institution
    Dept. of Syst. & Inf. Eng., Univ. of Virginia, Charlottesville, VA, USA
  • fYear
    2014
  • fDate
    25-25 April 2014
  • Firstpage
    222
  • Lastpage
    226
  • Abstract
    Today the advertising industry is becoming increasingly dependent on the Internet to deliver advertisements to viewers. Third party advertising networks such as Advertising.com, a division of AOL Inc., are utilizing targeted advertising strategies to make Internet advertising more profitable. Targeted advertising utilizes cookies to track users and target them with advertisements based on attributes such as site history and past advertisements viewed. Chad Gallagher, the Mobile Team lead for Advertising.com, projects that mobile Internet usage will surpass computer Internet usage by 2015. This major shift will render the traditional cookie based targeting model obsolete, as the majority of mobile devices do not store cookies. Consequently, the development of new mobile targeting strategies has become a top priority for companies such as Advertising.com in order to increase the profitability of online advertising. Predictive segments power the Internet advertising targeting strategy, but with cookies no longer available new variables need to be used for the predictive segments aimed at mobile users. This analysis seeks to determine the predictive power of the information unique to mobile users, in particular cellular provider and model of phone. This analysis was conducted utilizing data from two telecom companies´ advertising campaigns. The findings indicate that for telecom advertising, service provider and model of phone are statistically significant predictors of a consumer´s likelihood to convert. From these findings, the authors of this paper recommend the incorporation of mobile variables into predictive segments as they provide significant insight into consumer patterns. With the addition of the team´s work, Advertising.com will be able to boost their revenue per thousand impressions (RPM) from their mobile Internet traffic, which is currently their least valuable but most rapidly growing business segment. Future researchers should analyze the significanc- of these variables on advertising for nontelecom products, but as telecom companies are a major driver of mobile advertising, their campaigns proved to be a logical starting point.
  • Keywords
    Internet; advertising data processing; consumer behaviour; mobile computing; Advertising.com; Internet advertising; RPM; advertising campaigns; cellular provider; consumer patterns; mobile Internet traffic; mobile Internet usage; mobile optimization; mobile targeting strategies; online advertising; phone model; predictive segments; revenue per thousand impressions; service provider; site history; targeted advertising strategies; telecom advertising; Advertising; Educational institutions; Industries; Internet; Mobile communication; Mobile handsets; Telecommunications; Advertising; Mobile; Optimization; Predictive Segments;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Information Engineering Design Symposium (SIEDS), 2014
  • Conference_Location
    Charlottesville, VA
  • Print_ISBN
    978-1-4799-4837-6
  • Type

    conf

  • DOI
    10.1109/SIEDS.2014.6829906
  • Filename
    6829906