This is part two of a multi-part series. Part one can be found here.
Online advertising is a multi-billion dollar business. Google has become one of the worlds most recognized brands by selling ads on its search result pages and putting context ads on websites. Yahoo and Microsoft are working hard, and spending billions to compete with Google in this same space. Most online ads are targeted to the content of their residing website assuming that the ad is being viewed by someone who has an interest in a related subject. For example, a blog article on Ruby on Rails is going to have ads relating to development tools and book on the Ruby language. The click-through rates for these kinds of ads are pretty low, less than a few percent. This mean that most of the ads displayed on the site are being seen by people that are not interested in the products advertised. This isn’t much better than standard broadcast radio or television ads.
Years ago, some websites started collecting demographic information on their users through registration forms and mailing lists. This allows them to target ads to your demographic and charge more money for those ads (and you thought it was because they like you?). Click through rates are higher for these ads, but forcing users to register alienates them quickly and reduces your inventory. The more web savvy internet users become the less likely they’ll give out personal information, so this model will eventually fail.
This is why Yahoo! and Microsoft are pumping billions into behavioral targeting engines. Since ad networks can plant a cookie on a user’s desktop and they have ads on thousands of sites throughout the net they can track a user’s movement through any of those sites. By building a profile of these movements and using pattern recognition systems to put them in to groups, they can watch which groups are most likely to click on a particular ad. These targeting engines can serve up an ad that user is most likely to click on, even if that ad does not relate to the content on that site.
The big technology here is not in the ad serving software, but in the profiling of users. Ad networks have such a fantastic amount of data and it’s so diverse that normal data mining techniques break down or take longer to complete than can be useful. The number of methods applied to this problem is extensive: neural networks, factor analysis, Markov-chain Monty Carlo, logical methods and many combinations. These methods have been used by scientists for years to model aspects of our world and (partly through the field of bio-informatics) these techniques are going main stream.
Stay tuned for part three