Ad networks have always faced scrutiny in the ad
tech world for a variety of reasons. There were complaints of too many ad networks, then not enough of them; concerns of high margins, opacity and unsavory inventory. But since the late ‘90s, ad
networks have weathered and adapted to every economic and technologic storm, and the successful players are still growing. In fact, I believe the explosion of programmatic media buying has made them
indispensible for marketers looking to drive performance for their brand.
However, many still insist that ad networks are on their last legs. There is
talk that data management platforms (DMPs) and demand-side platforms (DSPs) are the new tools that will finally kill networks, because they provide something networks supposedly lack: transparency.
These new platforms take advantage of marketers’ desire for transparency. But, marketers need to ask themselves if this “transparency” is really only an illusion of control, and if
they possess the right analytical and operational skill sets to leverage these tools successfully in the first place.
At the center of the discussion surrounding ad tech transparency is the issue of pricing. The proposition of a self-service DSP or DMP is to give marketers control of all the media
buying levers, and to provide them with a clear view of what they’re paying for each impression. The players condemning ad networks would have you believe that on the other side of the
transparency spectrum, ad networks are black boxes making a profit off of arbitrage and heavy markups. That sure sounds sketchy, but it’s not the case. The network cost isn’t the result of
surreptitious markup, but of the bundling of services that go along with the media buy.
Networks do not
function in the same way as DSPs or DMPs because there is a clear difference in how the services are packaged – networks sell targeting, data, RTB technology and media together. While many
technology types (DSPs, networks, etc.) programmatically access and buy from the same display inventory, each company evaluates the media differently using its own unique process and algorithms. The
efficiency of determining where, when, what ad to serve, and more importantly how much to pay for it, is affected by the technology’s ability to use a combination of algorithmic and manual
optimizations to interact with data in a holistic way. Separating each of these elements may look cheaper on paper, but marketers need to consider what the final costs are once the time and efforts
have been put in to reach the same actionable results.
Let’s say the marketer is a homeowner looking
to remodel his/her home. The homeowner can hire an architect and a contractor, or he/she can do it themselves to cut costs. Many who go the latter route fail to factor in time and other costs besides
materials. In the end, the project may cost the same and the DIY-effort often doesn’t look as good as a professional job. Homeowners are always advised to be careful where they cut corners - and
the same goes in media.
This is important, because as much as marketers gripe about the cost of
media, they rarely judge partners on the upfront cost alone. At the end of the campaign, return on ad spend is the most significant criterion on which marketers are judged. Whether the media is
purchased on a cost-per-click model or flat fee, it doesn’t matter. It’s all a math exercise. The true mark of success for a direct response campaign is whether a partner had better return
than everyone else on the media plan. For all the marketing and buzzwords that ad tech companies use, performance is what matters.
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Networks may have previously worked within the flat CPM model, but the idea that
network costs are shrouded in a black box is no longer relevant. Networks that have evolved over the years are flexible in their pricing models and do provide various levels of pricing transparency.
Much like a DSP, networks can show marketers the variability in the bid pricing landscape for each campaign. Although networks and DSPs strive for the same end goal for marketers, the differences
between them lie in the unique paths they take to get there, the additional service provided by networks and the corporate brand positioning/packaging surrounding each company.
Another misguided transparency stereotype for ad networks is about “premium” inventory. The perception is that ad networks offer
little insight into their inventory and cannot provide brand-safe environments, while DSPs allow buyers to dictate what is premium and what they want to pay for it. We seem to be missing the point
that “premium” is not defined by the buyer or seller, but by the consumer sitting on a page with content. Only they can determine what is truly premium content. Furthermore, most
technologies employ some level of ad verification and URL blocking to alleviate concerns over risky sites and ensure well-lit ad placements. The technology has evolved to address all of these
concerns.
The negative “ad tech chatter” and branding behind some of the ad tech companies in our
space have led some marketers to believe that networks are bad, that transparency is lacking and that tighter data controls are necessary. All this does is breed fear and distrust in the ad tech
industry. Transparency – or ad tech’s definition of it – doesn’t matter if there is no return on investment. Media buying is not a matter of access, but of knowing how to
connect with audiences. Regardless of who implements the service, advertisers need someone to sort out audience and data, and attach those insights to media placements to generate positive
results.