Ad networks long have been a core component of any media plan. While in the early days of online advertising ad networks struggled to justify their existence, they now are welcomed in every major advertising budget thanks to their reach and scalability. Traditional ad networks can provide immediate results — and working with an ad network facilitates understanding of the marketing challenges, the ever-changing dynamics and the science behind driving greater value from online advertising.
By now, we’re pretty familiar with the traditional ad networks that have been around since the early ’90s, when (the then-independent) DoubleClick developed the first network of advertising sites and servers. The company developed and refined a targeting methodology that worked so well it became the de facto standard for how ad serving would work going forward. As far as it went, it was a good, easy-to-understand model. Advertisers paid publishers for every click. DoubleClick “owned” the targeting data, but only in the sense that it resided on its servers in order to execute campaigns and keep accounts. Just for clarity, the ubiquitous Wikipedia defines the ad network as follows: “Advertising network refers to an infomediary, which serves between a group (network) of Web sites (which want to host advertisements) and advertisers [that] want to run advertisements on those sites.”
Traditional ad networks
The traditional ad networks can be broadly categorized according to how the individual prospects are selected or targeted, and the range of publishers’ Web sites on which the advertisements can appear. Irrespective of these differences in emphasis, organization and approach, most networks offer a range of options, including demographic, geographic, (increasingly) behavioral and day-parting — which refers to the practice of dividing the day into several parts, during which a different type of radio or TV programming, or, in this case, Web advertising is aired. Many also will allow marketers to mix and match various criteria of selection, creating a near infinite range of possibilities for finding specific categories of prospects and serving them targeted messages.
An overview
Let’s take a quick overview of the core characteristics of the more effective traditional networks.
Contextual networks. Simply put, a contextual network will ensure that an ad will show up in a spot most appropriate to the purpose of the ad itself, thus allowing contextual relevance to drive the likelihood of a response. A simple example of this would be an ad for the new Ford Edge on a car-enthusiast site. The technology, analytics and obtainable degrees of granularity involved vary, but they generally involve semantic keyword matching and content indexing. However contextual matching is implemented, the core idea is that contextual ads are more likely to be considered useful and valuable as opposed to interruptive or annoying.
Contextual ads have been referred to as the next best things to search in terms of aligning with customers’ intentions.
Behavioral targeting. An alternative approach to contextual targeting is offered by the behavioral networks. Behavioral targeting refers to messaging targeted to a specific individual based on his previous online behavior. Take, for example, the aforementioned car enthusiast. Since we know that he regularly visits automotive-focused sites, why wouldn’t we serve him an ad related to car insurance or winter tires in September? These ads could be presented anywhere on the network — in the middle of an online newspaper article concerning restaurants in New York, for example. The objective here is not contextual relevance but to target an individual based on a given set of behaviors at a given time.
Studies have shown that conversions are higher when people are targeted through behavior rather than content because behavior can determine a person’s actions. The behavioral networks are able to segment their visitors based on behavior over a given period — this observation of behavior allows the networks (with a high degree of accuracy) to predict a visitor’s sex, age, income level, etc., and to use these variables in determining which ads to show when. Any direct marketer will tell you that recency of action is a great predictor of future actions. Behavioral targeting allows us to react to recency of action in nanoseconds.
