Contextual Targeting

This is the summary of a behavioral targeting article by Kaifu Zhang and Zsolt Katona, which studies the strategic aspects of contextual advertising. You can get the pdf of the original behavioral targeting article here: Contextual Advertising.

The New York Times wrote in 2002 that the biggest challenge for Google is to have a business model which is even a fraction as good as the technology it uses for search. Well, eight years later, the annual revenue of Google has gone beyond 20 billion dollars, thanks to advertising, specifically from AdSense and AdWord search advertising. There were criticisms for social networking site as well, regarding how they will earn money from their users, and once again, advertising was the solution to that problem. The advertising business model, in particular, the contextual advertising approach works well for search engines, social networks, micro blogging websites, and video sharing sites.

Contextual targeting is delivering targeted advertisements to consumers depending on the content they viewed. The world wide web has the most potential for this kind of targeted advertising because of two reasons. First, sites such as YouTube and Google AdSense has massive content which are heterogeneous, which allows the delivery of targeted ads to a huge number of people on the Internet. Second, improved targeting technology and algorithms has allowed for highly efficient contextual targeting.

Methodology

There are three key investigations in this study about contextual targeting. First is the imperfectness found in ad targeting. Specifically, contextual targeting precision is related to content base and product interests. Search engines, and Google AdSense for that matter, is good at determining a consumer’s product interests, but have to use data mining techniques to know the content of a certain website. In contrast, Youtube is not so good at determining the product interests of a consumer. This study will determine the relationship between the profits of an advertisers and product – content preference.

This study will also model the auction that happens for advertising slots, and figures out what determines the profits by the intermediary responsible for the auction and the hosting of content. Furthermore, this study also investigates the strategies used by the intermediary as it implements a quality score, optimizes its content base, among others.

An analytical model is used which defines a market with two advertisers that are differentiated horizontally. Internet users will know about products through advertising, and will buy only if they are aware of a product. The Internet content which will be browsed on by consumers is hosted by the contextual advertising intermediary, and consumers have heterogeneous preferences for both Internet content and products. The firms, or advertisers, will engage in a bidding process to win advertising spaces in various content topics, and a price competition will occur.

Results

The findings in this study show that contextual targeting can help reduce the competition in the product market. Furthermore, in a setting where firms advertising using various content topics, it doesn’t help the firm, in terms of profits, when preference for products and preference for content line up without flaw. In this situation, imprecise targeting can be beneficial to advertisers. Another result shows that if the preferences of consumers are not so heterogeneous, i.e. a consumer likes two products equally, contextual targeting can provide “Informational differentiation” by showing only an ad for one of the products.

Further results show that for high competition in the product market, a firm has a high insistence of bidding for its competitor’s more relevant content topic, in what is known as competitive preemption. This results in topic shelving equilibrium, where an advertiser may advertises for the most relevant and the least relevant content topic.