I recently had lunch with Iggy Fanlo, CEO of Adbrite. He is one of
the most thoughtful people I know in the online ad business and I
always enjoy our conversations. He related to me how he sees ad
targeting falling into four flavors:
-
Geographic
Demographic
Contextual
Behavioral
He noted that the first two of these, geographic and demographic,
are black and white, and focused on the user. You are in one and only
one location. You have one and only one gender, age or income. In each
of these cases, the key is to gather a broad dataset with which to
target. As a result, the largest sites and networks, with the largest
datasets, will tend to be best at these flavors of targeting.
Contextual targeting is also black and white, but it is not user
centric. Rather it is focused on the page. You are looking at a page
that is about some topic. Search is the easiest case, where the user
tells you what the page is about. Vertical ad networks
with endemic advertisers are also pretty easy to contextually target
because they only include sites within their desired topic. But the
general case is much harder. Now the winner isn’t necessarily the one
with the largest dataset of users, but rather the one with the best
algorithm for figuring out what the page is about.
Behavioral targeting is not black and white, but rather shades of
gray. Furthermore, it is both user centric AND page centric because
behavioral targeting is the accumulated sum of historical contextual
targeting. It is based not on what page you’re looking at now, but
rather on what pages you’ve looked at in the past.
In this case there are advantages to both having more information
about users past behavior, AND better algorithms. In it’s simplest
form, retargeting, a web user who had visited Ford.com in the past will
be shown Ford banner ads while on other sites. But ad fatigue limits the frequency with which one can retarget
based on a single datapoint. Good behavioral targeting systems need
good historical data as well as good algorithms to best manage the
portfolio of advertising opportunities to a single user. Companies are
using many different sources of historical data, including search history, looking for a user on the ad network, watching a user at the ISP level and even watching offline behavior.
AdBrite recently launched an Open Targeting Exchange
where it will let any company with a targeting algorithm bid to be used
to target ads across their network. It is a very interesting idea, and
I’ll certainly be watching closely to see how it works for them.
To read more from Jeremy, go to his blog.
