• by Zach Schapira , Op-Ed Contributor, January 3, 2017

Today, brands have a wide range of data to analyze that can inform their marketing and advertising strategies, which can include anything from DSP, CRM, demographic, psychographic, behavioral data, and more. The ad server, for example, is best known for—at a minimum—its frequency capping, day-parting and targeting capabilities. But if a brand marketer is looking for a bigger return on ad spend, ad server data, if manipulated strategically, presents several opportunities that can reduce campaign costs and increase performance.

Make third-party data the gift that keeps on giving. Third-party audience data is expensive, but what happens after you make a purchase for any given impression? The highly prized user gets served an ad, may or may not actually see it, and then disappears again never to be seen, until you buy that same third-party data to purchase another impression. Right?  

Conventional wisdom maintains that this information needs to be purchased and considered in a vacuum each time it is used to target an impression—but this doesn’t have to be the case. If you buy a broad combination of data for an ad campaign and then capture and segment those users via your ad server, those users become first-party audiences that you now own. It’s important to remember that these users received your ads, so you can continue to engage with them and avoid paying another tax to the data provider.

Capture your own user intent data. Most user intent data is collated from user browsing behavior, on the assumption that someone becomes targetable because they previously browsed certain topics or products. Contextual targeting is a cousin of this approach, but instead of relying on past behavior, it offers advertisers insight into the user’s mindset in real time.  So where does the ad server come into play?

When your ad server is able to capture users exposed to such a contextually targeted campaign across multiple publishers, that means you now have a first-party audience segment that could be leveraged just like a user-intent segment for either media or creative targeting.

Improve your targeting. Retargeting, as it’s mostly used today, is a simple process. If the user has a cookie, you target; if the user doesn’t have a cookie, you avoid. But do we as marketers care whether the user actually saw the ad or not? We should.  And the only way to know that for sure is to use data from the ad server, which tells you whether that user you want to retarget actually had a viewable impression or not.  

The same can also be said for any targeting technique, media or creative, which depends solely on the presence of a cookie. Sequencing and frequency capping are other examples.

Frequency cap enforcement. At the media level, frequency capping relies on the tunnel vision of the platform where buying occurs, whether it be a DSP or publisher-side ad server. But what happens if you’re using multiple DSPs? Are you limited to each one’s scope of reach?

Not if you’re centralizing your ad serving, segmenting users there, and syncing those segments with each buying platform. By setting a rule on the ad server that anyone who reaches a designated frequency is placed into the “don’t buy” segment, and then anti-targeting that segment on any integrated buying platform where you want to enforce the frequency cap, you can escape from the tunnel vision of an individual platform. If you can base this on viewability — or even video completion rates —even better.

When advertisers rent a piece of real estate on a page or app, they typically have the rights to the data generated by that impression. But when several or even tens of vendors are used to deploy advertising campaigns, valuable data can be left untapped. For marketers seeking ways to get more out of their ad spend and the data it yields, the ad server can work as a strategic starting point.