The Audience-Targeting Family Tree

  |  August 26, 2009   |  Comments

A look at different types of behavioral targeting offerings and how they're used.

Of the biggest challenges media planners and buyers face when they approach behavioral and audience targeting for the first time involves defining what behavioral targeting is and how it's used by advertisers and publishers.

Confusion stems from the many different ways behavioral targeting is defined by vendors that offer targeting solutions. In truth, a percentage of vendors who claim to be offering behavioral targeting solutions are in fact relying on more traditional targeting methodologies, such as demography, geographical targeting, and consumer purchasing patterns, to make their mojo work.

For the purposes of consistency, let's agree that true behavioral targeting solutions must include a way of tracking and saving data points based on actual individual consumer actions. As you'll see, even this definition provides a great deal of flexibility.

Let's meet the family.

Cluster Targeting

Cluster targeting, in general terms, is the process of breaking general audiences down into smaller segments defined by common need or interest. In turn, these segments can be used to provide category advertisers with a definitive target market.

In most cases, cluster targeting deals with major industry categories, such as frequent travelers, auto intenders, minority groupings, entertainment buffs, academics, and business decision-makers, among others. These segments often represent significant market sectors and consist of numbers large enough to be meaningful to a range of advertisers.

Custom Segment Targeting

Custom segments allow publishers and advertisers to select the criteria used to define and locate audience segments. Custom segment targeting vendors provide publishers with tools that allow them to tag site and landing pages and help determine what consumers look at and interact with while on those sites. These data, in turn, are used to identify which page visitors are targeting candidates based on interest and intentions toward a specific topic or activity.

For example, an advertiser looking to reach recreational tennis players can work with a publisher to identify specific consumers by setting targeting rules that best define the best candidates for an offer. The targeting criteria may include site visitors who visit specific tennis themed sites and pages, read tennis related news items, and live in proximity to specific tennis events. While custom audience segments are generally smaller than those found in cluster target groups, their refined nature also often makes them more highly relevant to advertisers.

Compared to cluster targeting, which often represents the low-hanging fruit of the population, custom segment targeting allows publishers and advertisers greater flexibility and more granularity when identifying the specific characteristics of consumers that best match the target audience for specific products and services.

Retargeting

Retargeting is the process of identifying and tagging visitors to specific Web sites and landing pages and then creating follow-up advertising and messaging to remind those consumers who didn't convert the first time about the original offers and opportunities.

Most retargeting models are based on identifying consumers who expressed initial interest in a product or service on a Web site or landing page but who didn't convert. With retargeting, advertisers can follow these tagged consumers through ad and site networks and position relevant ads in front of them to remind them of the initial offer and encourage them to follow up.

One common area of retargeting is reconnecting with online shoppers who have abandoned shopping carts to remind them of the benefits and to perhaps sweeten the deal to increase conversion rates.

Predictive Targeting

Predictive targeting is a relative newcomer to the online targeting world and focuses on using algorithms to help determine what consumers will be doing in the future by observing current behaviors.

A good deal of predictive targeting is based on life-stage models, which use a consumer's current behaviors to help identify his future behaviors. Looking for behavior patterns associated with new parents, for example, can help identify potential future behaviors of those consumers (purchasing baby and toddler swag, future educational concerns, changes in transportation needs, modified entertainment considerations, etc.).

Semantic Targeting

Semantic targeting is actually an offshoot of more traditional contextual targeting approaches. Using the assumption that consumers are attracted to content of interest to them, semantic targeting looks at visited pages' content and uses highly specialized algorithms to determine the overall meaning and value of that content. Unlike contractual targeting, which relies purely on keywords to ascertain value, semantic targeting is focused on the more holistic overview of page content to get a better sense of a consumer's areas of interest and intent.

This is not a definitive list of targeting solutions available today. But this list, which represents many of the major areas of audience, is an attempt to flush out the differences between solutions and to provide you with a better understanding of the mechanics and value of each. If the march of technology has taught us anything over the years, it's that this, too, is guaranteed to change.

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ABOUT THE AUTHOR

Rob Graham

Rob Graham is the CCT (chief creative technologist) of Trainingcraft, Inc., where he heads up development of customized training programs for a wide range of digital marketing, entrepreneurial development, and digital media clients.

A 20 year veteran of digital media, Rob has served as the CEO of a multimedia development company; an interactive media strategist; a rich media production specialist; a Web analytics consultant; a corporate trainer and seminar leader; and a chief marketing officer.

When he isn't on the road presenting training workshops, Rob teaches at Harvard University, Emerson College, and the University of Massachusetts - Lowell where he teaches classes on Digital Media Development, Web Store Creation, Software Programming, Business Strategies, and Interactive Marketing Best Practices.

He is the author of "Fishing From a Barrel," a guide to using audience targeting in online advertising, and "Advertising Interactively," which explores the development and uses of rich-media-based advertising. He has been an industry columnist covering interactive marketing, digital media, and audience targeting topics since 1999.

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