Advances in advertising platform technology have dramatically changed behavioral targeting. Better data from across multiple networks and third-party providers as well as automation and cost savings have all made behavioral a more accurate marketing tool. However, results of two studies released in December illustrate that industry perception and understanding of behavioral targeting has not kept pace with shifting demands for greater insight into data.

Advertise.com and SEMPO found that some publishers were "skeptical of behavioral targeting and often misunderstood how it worked." Econsultancy and the Rubicon Project found that advertisers have a "lack of awareness" of behavioral targeting. Both publisher skepticism and low advertiser awareness indicate that companies offering behavioral insights will have to do a better job in 2010 of educating the industry. Here's a start.

Sequential messaging: Tell your story on your site, or across a network
Sequential advertising, or storyboarding, tells a story and pulls consumers through a series of related messages. To develop context, sequential advertising uses data gathered from website visits, campaign exposure, clicks, and engagement history, all creating continuity of message. Each engagement touch point with sequential messaging informs the next message sent to the consumer creating a deeper, unique, and compelling dialogue.

Video-enabled banner units are popular examples of sequential messaging. When a viewer clicks to play or stop the video, the consumer receives a specific call-to-action. When the user moves away from the banner ad, the story can follow the user within a new creative unit that serves the next part of the video story with the same call to action. The key to success is continuity of the call-to-action. The benefit to sequential advertorial is that with every impression, the consumer receives new information, thereby creating deeper engagement.

Sequential messaging serves creative based on what is known about a consumer's stage in the conversion cycle or based on the consumer's engagement with the brand. Sequential advertising can be used on a website, or anywhere on the web.

Look-alike modeling: Optimize your site traffic to engage similar audience profiles
You probably know from shopping on Amazon.com that every time you return to its website, Amazon makes recommendations of other items you might want to purchase. Amazon is inferring your interests from your past purchases, but it's also making recommendations based on similarities between you and other Amazon.com shoppers. Look-alike behavioral recommendations isolate and categorize attributes unique to specific user groups and create "like" profiles; a user who shares the same profile as your brand evangelists might be one of your very best prospects.

Look-alike profiles are developed based on affinity data collected each time users visit a website, click on creative, or make purchases. With each touchpoint, a "like" data set is enhanced. Marketers can then target and optimize their messages to sub-segments of specific audiences, and "like" profiles can be matched with relevant offers. Dynamic creative solutions automate the process of identifying which messages, offers, or call to actions resonate best with different classes of look-alike audiences.

Look-alike modeling can increase click-through rates by as much as 40 percent over using a network alone. Look-alike models can be defined by specific demographic, psychographic, and behavioral attributes. The most effective models are able to find similarities across unique users down to their event streams. Through optimizing data traffic on their own websites, look-alike modeling enables advertisers to better understand the profile of their visitors while developing offers that appeal to specific audiences.

Personalization: Learn from past actions to pinpoint your message
Look-alike modeling, sequential messaging, as well as geo-targeting, site-retargeting, multivariate optimization, and collaborative filtering (i.e., recommendations) all come together for personalization. Personalization leverages website and third-party data to define promising audiences, targeting consumers with informed messages catered to their specific needs or interests. Personalization platforms learn from past actions and use the collective behavior of people on websites to identify the best offers and products to recommend.

With personalization platforms, advertisers can optimize based on the observed performance or action of each audience segment. Take, for example, an advertiser who wants to target college football audiences broken down by each college football team. Ticket buyers can be segmented into team audiences according to actions taken on previous visits to online ticketing outlets or college team websites. Based on the observed performance of each potential buyer, ad calls contain ticket buyer data used to optimize the personalized creative, including the profile of the user, context of the impression, frequency, and day part. Using these specific profiles, retargeting allows fans to only see logos, jerseys, messages, and players that are relevant to that fan.

New advertising platforms using personalization techniques and dynamic creative units have made behavioral as measurable as search for advertisers. With the ability to look holistically at campaigns across all sources of data and inventory, behavioral targeting is more reliable than ever before. Advances in platform technology have changed behavioral targeting efficacy and lowered costs to advertisers looking to purchase audience by segment. There are so many ways to incorporate behavioral targeting into your campaigns; more and better data is only part of the reason behavioral will continue to strengthen in 2010.

David Jakubowski is chief revenue officer and general manager of Aggregate Knowledge.

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