Behavioral targeting comprises a range of technologies and techniques used by online
website publishers and advertisers aimed at increasing the effectiveness of advertising
using user web-browsing behavior information.
In particular, "behavioral targeting uses information collected from an individual’s
web-browsing behavior to select advertisements to display".
When a consumer visits a web site, the pages they visit, the amount of time they view each
page, the links they click on, the searches they make and the things that they interact
with, allow sites to collect that data, and other factors, create a 'profile' that links
to that visitor's web browser.
As a result, site publishers can use this data to create defined audience segments based
upon visitors that have similar profiles.
When visitors return to a specific site or a network of sites using the same web browser,
those profiles can be used to allow advertisers to position their online ads in front of those
visitors who exhibit a greater level of interest and intent for the products and services being
offered.
On the theory that properly targeted ads will fetch more consumer interest, the publisher
can charge a premium for these ads over random advertising or ads based on the context of
a site.
Behavioral marketing can be used on its own or in conjunction with other forms of targeting
based on factors like geography, demographics or contextual web page content.
It's worth noting that many practitioners also refer to this process as "Audience Targeting".
Onsite behavioral targeting
Behavioral targeting techniques may also be applied to any online property on the premise
that it either improves the visitor experience or it benefits the online property, typically
through increased conversion rates or increased spending levels.
The early adopters of this technology/philosophy were editorial sites such as HotWired, online
advertising with leading online ad servers, retail or other e-commerce website as a technique
for increasing the relevance of product offers and promotions on a visitor by visitor basis.
More recently, companies outside this traditional e-commerce marketplace have started to experiment
with these emerging technologies.
The typical approach to this starts by using web analytics to break-down the range of all
visitors into a number of discrete channels.
Each channel is then analyzed and a virtual profile is created to deal with each channel.
These profiles can be based around Personas that gives the website operators a starting
point in terms of deciding what content, navigation and layout to show to each of the different
personas.
When it comes to the practical problem of successfully delivering the profiles correctly
this is usually achieved by either using a specialist content behavioral platform or
by bespoke software development.
Most platforms identify visitors by assigning a unique id cookie to each and every visitor
to the site thereby allowing them to be tracked throughout their web journey, the platform
then makes a rules-based decision about what content to serve.
Again, this behavioral data can be combined with known demographic data and a visitor's
past purchase history in order to produce a greater degree of data points that can be
used for targeting.
Self-learning onsite behavioral targeting systems will monitor visitor response to site
content and learn what is most likely to generate a desired conversion event.
Some good content for each behavioral trait or pattern is often established using numerous
simultaneous multivariate tests.
Onsite behavioral targeting requires relatively high level of traffic before statistical confidence
levels can be reached regarding the probability of a particular offer generating a conversion
from a user with a set behavioral profile.
Some providers have been able to do so by leveraging its large user base, such as Yahoo!.
Some providers use a rules based approach, allowing administrators to set the content
and offers shown to those with particular traits.
Network behavioral targeting Advertising networks use behavioral targeting
in a different way than individual sites.
Since they serve many advertisements across many different sites, they are able to build
up a picture of the likely demographic makeup of internet users.
An example would be a user seen on football sites, business sites and male fashion sites.
A reasonable guess would be to assume the user is male.
Demographic analyses of individual sites provided either internally or externally allow the
networks to sell audiences rather than sites.
Although advertising networks used to sell this product, this was based on picking the
sites where the audiences were.
Behavioral targeting allows them to be slightly more specific about this.
Theoretical research on behavioral targeting In 2006, BlueLithium in a large online study,
examined the effects of behavior targeted advertisements based on contextual content.
The study used 400 million "impressions," or advertisements conveyed across behavioral
and contextual borders.
Specifically, nine behavioral categorieswith over 10 million "impressions" were observed
for patterns across the content.
All measures for the study were taken in terms of click-through rates and "action-through
rates," or conversions.
So, for every impression that someone gets, the number of times they "click-through" to
it will contribute to CTR data, and every time they go through with or convert on the
advertisement the user adds "action-through" data.
Results from the study show that advertisers looking for traffic on their advertisements
should focus on behavioral targeting in context.
Likewise, if they are looking for conversions on the advertisements, behavioral targeting
out of context is the most effective process.
The data was helpful in determining an "across-the-board rule of thumb," however results fluctuated
widely by content categories.
Overall results from the researchers indicate that the effectiveness of behavioral targeting
is dependent on the goals of the advertiser and the primary target market the advertiser
is trying to reach.
In the work titled An Economic Analysis of Online Advertising Using Behavioral Targeting,
Chen and Stallaert study the economic implications when an online publisher engages in behavioral
targeting.
They consider that the publisher auctions off an advertising slot and is paid on a cost-per-click
basis.
Chen and Stallaert identify the factors that affect the publisher’s revenue, the advertisers’
payoffs, and social welfare.
They show that revenue for the online publisher in some circumstances can double when behavioral
targeting is used.
However, increased revenue for the publisher is not guaranteed: in some cases, the prices
of advertising and hence the publisher’s revenue can be lower, depending on the degree
of competition and the advertisers’ valuations.
They identify two effects associated with behavioral targeting: a competitive effect
and a propensity effect.
The relative strength of the two effects determines whether the publisher’s revenue is positively
or negatively affected.
Chen and Stallaert also demonstrate that, although social welfare is increased and small
advertisers are better off under behavioral targeting, the dominant advertiser might be
worse off and reluctant to switch from traditional advertising.
Privacy and security concerns
Many online users and advocacy groups are concerned about privacy issues around doing
this type of targeting.
This is a controversy that the behavioral targeting industry is trying to contain through
education, advocacy and product constraints to keep all information non-personally identifiable
or to obtain permission from end-users.
AOL created animated cartoons in 2008 to explain to its users that their past actions may determine
the content of ads they see in the future.
Canadian academics at the University of Ottawa Canadian Internet Policy and Public Interest
Clinic have recently demanded the federal privacy commissioner to investigate online
profiling of Internet users for targeted advertising.
The European Commission has also raised a number of concerns related to online data
collection, profiling and behavioral targeting, and is looking for "enforcing existing regulation".
In October 2009 it was reported that a recent survey carried out by University of Pennsylvania
and the Berkeley Center for Law and Technology found that a large majority of US internet
users rejected the use of behavioral advertising.
Several research efforts by academicians and others have demonstrated that data that supposedly
anonymized can be used to identify real individuals.
In March 2011, it was reported that the online ad industry would begin working with the Council
of Better Business Bureaus to start policing itself as part of its program to monitor and
regulate how marketers track consumers online, also known as behavioral advertising.
Case law In re DoubleClick
FTC regulation of behavioral advertising See also
Personalization Profiling
Digital traces Reality mining
Notes and references