Smart Ads for Better User Targeting

Smart Ads for Better User Targeting

Albert Asmaryan (Bauman Moscow State Technical University, Russia), Alexey Levanov (Bauman Moscow State Technical University, Russia) and Irina Borovik (Bauman Moscow State Technical University, Russia)
DOI: 10.4018/978-1-5225-8188-8.ch003


Currently, there is a problem of contextual advertising. Advertisers want to be able to clearly target the audience because user experience and revenue depend on the relevance of the displayed ads. Today's technology makes it easier not only to collect a wealth of information but also to ensure that it accurately reflects on your target audience, industry, and ad campaigns. Contextual advertising supports much of the web's ecosystem today online but not offline. The task was to develop a system prototype and scale it, which gives the ability to display advertising based on user interests in real life based on the best techniques of contextual advertising.
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Literature Review

Contextual advertising has been of significant interest for a number of literatures. In various fields has been published numerous articles and research. They are all agree on one that today, the use of advanced digital tracking software within the performance marketing industry is widespread. By engaging solutions that capture detailed data points and analyze every step a consumer takes on the path to a conversion, advertisers can better understand which ads, offers and channels are driving the most profitable results, and more successfully allocate their digital spend. Because their success depended on it, performance marketers began to seek out more robust solutions capable of tracking large volumes of data, rapidly, and at a very granular level. This is because the multiple data points now available for analysis hold valuable clues that decision makers can leverage to assess and refine their strategies (Albert, Rajagopal, & Sevlian, 2011).

Another topic that has received much attention in this field has been the analyze of person data in real-time. In the fast-paced world of digital advertising, even the most sophisticated data tracking and analysis is basically useless if it can’t be accessed, shared and acted on in real-time. The shelf life of online data is short, so an ability to capture and analyze information at lightning speed is key (Evans & Chi, 2008). Technology’s ability to quickly process and extract useful information from raw data has improved exponentially in recent years. Businesses that don’t take advantage of real-time tracking and analytic innovations will be left behind.

Extensive literature exists on social behavioral advertising (SBA) (Yan, Liu, Wang, Zhang, Jiang, & Chen, 2009), advertising networks profile a user based on his online social activities. Using this profile, advertising networks show ads that are more likely to be of interest to a particular user. SBA presents both benefits and downsides to users. If their interests have been accurately profiled, users will receive more relevant advertising. However, collecting data about users’ online activities can potentially violate their privacy.

However, until recently most such studies could only use collected data for online contextual advertising and did not use it to identify common interests of the users and display advertising on billboards and other types of city ads.

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