In life, there are no guarantees. This old adage holds a convincing level of business application as, like most things in life, there are no absolute promises until money’s finally exchanged and goods are sold.
Marketing is no exception. There are no guaranteed campaign results, regardless of how carefully marketers plan beforehand. Murphy’s Law is a force constantly challenging all planners and marketers.
The communications business is a game of probability that could be described as calculated likelihood. The game’s general purpose is to utilize and synthesize as much valid research and derived insights to interpret a business challenge and, ultimately, to devise strategies that will likely increase the possibility of a conversion, however that’s defined.
How, then, is the probability game relevant to behavioral targeting online?
Birds of a Feather…
The school of behavioral-focused planning has been gradually reshaping the way media is planned. This has less to do with targeting technology hype than with fundamental changes in communications. As consumer behaviors change, marketers must redefine planning rules to ensure the calculated likelihood of reaching the audience is sustained.
The Atlas Institute introduced a tool called Behavior-Based Media Planning (BBMP) a few years back. This method’s premise of planning uses past traditional planning parameters (e.g., demographic and psychographic definitions, such as age, gender, and income) and encourages defining the target group by behavior-based profiles to identify users who have actually performed a desired action or set of actions. In other words, it segments the audience by output and tangible actions rather than by input and proxy actions.
The Atlas BBMP media planning method can also be interpreted as likelihood-based behavioral planning. It identifies audiences that have performed predefined actions and, based on associated and likely probability, seeks to identify other placements across the Web that most likely have the highest concentration of similar target users. A site that attracts a high concentration of target users probably attracts others like them.
Cluster-Based Behavioral Visits
We all have our daily routines. Most of us go to the office, turn on the computer, grab a coffee, and check e-mail accounts and regularly visited sites for news and entertainment. Logically speaking, instead of one specific behavior, we can say there’s actually a cluster of behaviors across a group of sites taking place in a sequential, interconnected manner.
Effectively speaking, marketers can establish visit-based clusters in behavioral targeting. A user segment that visits sites similar to another segments’ sites is likely to share similar interests through exhibited behaviors.
With Amazon’s “Customers who bought [X] also bought:” recommendations, for example, someone who responded positively to one product (or messaging) probably shares similar interests to another person who bought the same product. Through likelihood rationalization, people interested in similar things probably share similar clusters of behaviors.
What Does This Mean for Online Media?
Media has evolved. Old planning methods are no longer sufficient to reach modern-day consumers. Although this isn’t news to those in the frontlines of the communications revolution, many have yet to fully adopt any new planning methods.
Prior to including behavior in the targeting mix, media planning was at best limited to generic demographic segmentation or psychographic profiling. New technology captures near-real-time consumer data, allowing advertisers to target and segment audiences with precision.
Demographic limitations erode with the Internet’s democratization of consumer lives, yet most planners still struggle to clearly embrace new ingredients that should be included in the planning process.
In the new world of BlueCasting, digital billboards, and a blossoming array of new interactive channels, consumer behaviors aren’t only becoming more complex but also increasingly perplexing. As clear lines between media continue to blur (e.g., accessing the Internet on an iTV vs. watching TV via the Internet on a PC), most old planning parameters will evolve into something we can’t even predict.
Though successful marketing campaigns thrive on this uncertainty, ultimately it comes down to how to intelligently use various marketing tools to create sound strategies most likely to engage the intended audience. In the online game of calculated likelihood, in other words, behavioral targeting is one of the key new ingredients that make this happen.
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