Behavioral targeting

Behavioral targeting

Behavioral targeting or behavioural targeting is a technique used by online publishers and advertisers to increase the effectiveness of their campaigns.

Behavioral targeting uses information collected on an individual's web-browsing behavior, such as the pages they have visited or the searches they have made, to select which advertisements to display to that individual. Practitioners believe this helps them deliver their online advertisements to the users who are most likely to be influenced by them.

Behavioral marketing can be used on its own or in conjunction with other forms of targeting based on factors like geography, demographics or the surrounding content.

Examples of behavioral targeting in advertising targeting systems include: Predicta BT, AdLINK 360, Adaptlogic, Avail, Boomerang, Criteo, DoubleClick (prior to 2002) [cite web | url=http://news.cnet.com/2100-1023-803593.html | title=DoubleClick turns away from ad profiles | date=2002-01-08 | author=Olsen, Stefanie |work=CNET News.com ] , Leiki, nugg.ad, prudsys, ValueClick [cite web | url=http://www.clickz.com/showPage.html?page=3630287 | title=Two Large Ad Networks Embrace Behavioral Targeting | date=2008-07-21 | author=Arun, Fred |work=clickz.com ] , and wunderloop.

Behavioral Targeting allows site owners or ad networks to display content more relevant to the interests of the individual viewing the page. On the theory that properly targeted ads will fetch more consumer interest, the seller may ask for a premium for these over random advertising or ads based on the context of a site.

Onsite Behavioral targeting

Behavioral targeting techniques may also be applied to any online property on the premise that is either improves the visitor experience or it benefits the online property, typically through increased conversion rates or increased levels of spend. The early adopters of this technology/philosophy are primarily within a 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 best know example is [http://www.netmining.com Netmining] .

The typical approach to this starts by using web analytics to break-down the visitor mass into a number of discrete channels. Each channel is then analysed 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 is 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, behavioral data can be combined with demographic and past purchase history in order to produce a greater degree of granularity in the 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 to individual sites. Since they serve many adverts 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 (user surveys) or externally (Comscore netratings) allow the networks to sell audiences rather than sites. [ iMedia Connection article on Behavioral Targeting for Networks in the USA [http://www.imediaconnection.com/content/3297.asp] ] 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.

This service is offered by (among others):Front Porch, cite web | url=http://www.clickz.com/3628256 | title=Toby Gabriner to Helm ISP-Based Behavioral Ad Firm Adzilla | date=2008-01-29 | author=Rodgers, Zachary ] Tacoda, Burst, NebuAd, Phorm, ValueClick.

Concerns

Many online users & 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 & product constraints to keep all information non-personally identifiable or to obtain permission from end-users. [cite news|url=http://www.seoserpent.com/2008-09/isp-behavioral-targeting/|title=ISP Behavioral Targeting versus You|date=2008-09-26] 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. [cite news|author=Story, Louise|title=AOL Brings Out the Penguins to Explain Ad Targeting|url=http://bits.blogs.nytimes.com/2008/03/09/aol-brings-out-the-penguins-to-explain-ad-targeting-ok-saul-and-louise-post-with-article/|date=March 10, 2008 in cite news|author=Story, Louise|title=To Aim Ads, Web Is Keeping Closer Eye on You|url=http://www.nytimes.com/2008/03/10/technology/10privacy.html|work=The New York Times|publisher=The New York Times Company|date=March 10, 2008|accessdate=2008-03-09] 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. [cite news|url=http://www.ctv.ca/servlet/ArticleNews/story/CTVNews/20080728/internet_privacy_080728/20080728?hub=TopStories|title=Academics want watchdog to probe online profiling|date=2008-07-28]

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