However, one key area in which we have not seen an overindulgence in the use of the term is within behavioral targeting. The verticalization of behavioral targeting (BT) is where some of the greatest opportunities exist. As BT companies collect a growing amount of data, this data needs to be packaged in an optimal way to enable media owners and advertisers to leverage it.

The challenge lies in balancing scale and granularity. Many providers have divided their collected data into vertical categories to help advertisers reach an audience tailored to their industry. These main, broader categories may encompass Auto, Travel, Business, etc. Different vertical categories of purchase intent data can be made available to ad networks and large publishers seeking to maximize their targeting capabilities. Data can easily be categorized on an advertiser-friendly and privacy-sensitive basis.

However, many advertisers are looking to strike a balance between reaching prospects expressing interest in their category and prospects expressing interest in a specific product. This optimized mix could be created to an extent through the development of subcategories that fall within the main categories. This allows interactive advertisers to choose between targeting a larger group and/or targeting a smaller group based on their specific campaign needs. For example, within campaign X the advertiser may need to reach all “Travel” users, while simultaneously, within campaign Y they need to run a campaign that reaches only users that are in the market for “flight to LAX.”

It is important to remember, however, that creating an optimal category mix is only one minor piece of the puzzle. To get the best results, advertisers must be able to access vertical-specific data that demonstrates purchase intent. While sophisticated in their own way, many behavioral targeting companies focus mainly on content that, while falling within a specific vertical category, is not indicative of the reader’s intent to purchase.

Leveraging purchase intent data provides advertisers with access to users that are further along within the purchase cycle. Potential customers are more focused on finding exactly what they want because they have spent far more time on vertical sites researching a possible purchase.

Moving forward, the challenge is to provide advertisers with purchase intent data within larger vertical categories, with a deeper granularity in those verticals. This will enable advertisers to leverage a far wider scale of data in highly defined niches. To-date most networks and sites have been able to offer either scale with little granularity or some granularity without scale, which is why it makes sense for most of them to work with external data providers.

While in theory social networking sites have terabytes of monetizable data, the reality is that much of that data is irrelevant for advertisers. Why does it matter, for instance, who consumer X is friends with? True, one person could be recognized as ‘influential’ over others but what does it mean? Does it mean that when someone is looking to buy a DVD, they will ask that person for his or her opinion? What are the odds that, even if asked for his or her opinion, that influencer bought a DVD recently and has any idea what DVD his ‘influenced’ friend should buy?

Take the most grandiose effort so far, Facebook’s Beacon – where do they get data as to prospects’ purchases and purchase intent? They get it from advertisers’ sites. If they knew consumer X was buying a cell phone, then why did they need the advertiser data?

Leveraging social networking sites’ audience member data is a ‘low-hanging-fruit’ approach that will inevitably lead to enhanced ad space monetization for those sites. Nevertheless, there is a better solution that stands to benefit consumers, advertisers, and the sites themselves.

It all comes down to purchase-intent. When a user goes to Google, or any other search or vertical site for that matter, and types in “Laptop” or “Car Insurance” or “flight to Atlanta,” in most cases they are declaring their intent to purchase one of the aforementioned products in the near future.  This is one of the main benefits of advertising on a search engine or a vertical site. Google’s success stems from it being the world’s #1 aggregator of consumer purchase-intent data.

Internet users spend roughly 5% of their time declaring purchase intent (i.e. searching) on search results pages that seize approximately 40% of Internet ad spending, and 95% of their time browsing ad-supported content (i.e. social networking sites, news sites, Web-based email sites) that are mainly unsold or sold for relatively low rates and difficult to monetize via contextual means. 

While leveraging their own audience member data via increased ad content relevance (behavioral targeting) will undoubtedly reap short-term benefits for social networking communities, the long-term monetization opportunity lies with the monetization of purchase-intent data, which they will either need to buy or acquire through partnerships. 

The enhanced targeting that the acquired data will provide will enable social networks to deliver better targeted campaigns that maximize advertiser ROI and the networks’ own effective CPMs.  This is because purchase-intent data has been proven to deliver results.

On that note I offer a word of advice to Facebook and Myspace.  Facebook – you were right to reach outside your properties for purchase-intent data. You were wrong as to the way you used the data. Rupert Murdoch – I have no idea whether and what shape a partnership with Yahoo will look like but I would advise both Myspace and Facebook to partner with a company or companies that can provide you with access to lots of purchase-intent data– data that will be instrumental for monetizing your ad space.