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AlmondNet Founder & CEO: Roy Shkedi- Roy has over 15 years of experience in inventing, developing and implementing interdisciplinary products as well as analyzing and investing in companies. Since founding AlmondNet in 1998, Roy has been recognized as a pioneer in the behavioral targeting and profile based ad exchange sectors of the Internet advertising market. Roy holds an MBA degree, an M.Sc. degree in Electronic Engineering, and received the most distinguished prize in the Israeli Intelligence community for an R&D project. Prior to founding AlmondNet, Roy was the High Technology Analyst for Ofek Securities, Israel's largest portfolio manager.

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How To Balance Scale, Granularity On the Vertical Tightrope

Written on
May 2nd 2008
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by Roy Shkedi  |
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geotarget1.jpgADOTAS EXCLUSIVE – Among the many passing industry trends, “verticalization” remains a key term in the online advertising industry. Whether it’s the promotion of vertical networks, vertical pay-per-click campaigns, or generally speaking — the verticalization of search — the terms’ use is ubiquitous.

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.



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