The GDPR (General Data Protection Regulation) storm swept through the digital advertising industry less than 3 months ago. As the dust settles, one of the main influences we’re seeing GDPR having on the adtech industry is the limited ability of marketers to target based on user data. Though one can still gain user consent for cookies, that too is quickly becoming a thing of the past as Safari and iOS bar 3rd party cookies. Not to mention Apple’s latest bold move limiting Facebook’s ability to track users. It’s just a matter of time before Google follows suit, inevitably bringing cookie tracking to final extinction. Add to that the severe limitations imposed by GDPR on cookies and advertising identifiers (e.g. IDFA and AAID), and programmatic advertising remains stripped of user insights, forced to go back to the drawing board.
Back to basics
Has the digital advertising industry become so caught up in the momentum of user targeting that it has completely neglected the other end of the spectrum?
User-targeting may no-longer be a viable option for ad campaign optimization, but we still have the good old Contextual approach that the industry has relied on for years. With contextual advertising, ads are placed on websites or site pages that are directly relevant to the ad’s context. So if you’re running an ad for hiking shoes, for example, you might want to place it on a webpage discussing new hiking trails. There is a higher likelihood that someone looking into hiking trails might be interested in hiking shoes, than someone reading a recipe for pumpkin pie. Contextual advertising has a higher probability for better CTR’s and conversions.
But, in a time when data is king, is contextual data enough to provide that pin-sharp targeting we’ve become accustomed to from user-targeting?
There is another way – a new generation of targeting has evolved.
Data collected from billions of ad campaigns and transactions, has formulated to create a Collective targeting infrastructure, enabling advertisers to capitalize on the wisdom of the masses, or in other words, statistics.
For any given placement, campaign performance statistics such as click-through-rates, completion rates and engagement metrics, are processed through advanced machine learning algorithms to form a knowledge graph. With these insights, we are able to predict the outcome of a similar campaign in that placement. Insights that can be generated from collective data include ad campaign behavior – which formats deliver the best results in which sites/inventory, to what extent people are engaging with the ad – scrolling, hovering, clicking, tapping, conversions, etc. The aggregation of the collective data and statistics, creates a backstage view of the inventory. Insights that we previously relied on user data to achieve.
The power of consolidation
When combining the old and new approaches, using Contextual and Collective data points from ad transactions, we end up with a robust targeting knowledge-graph that is far stronger than user-based targeting.
With this new methodology, the extensive knowledge graph is able to identify inventory that has met the desired KPI’s for a given campaign category, let’s say children’s clothing. Based on these insights, marketing teams for ‘look alike’ campaigns such as other children’s brands, will be able to target that same inventory to achieve similar KPI’s.
The bottom line
This new targeting model has a farther reaching effect than just targeting. Based on these new insights and targeting capabilities buyers are now able to pay by target KPI’s, distinguishing higher quality inventory from the rest. Publishers on the other hand are able to charge higher CPM’s for inventory meeting higher KPI targets. A win-win solution for all.
At Cedato, our focus from the very beginning has been on creating unique machine learning algorithms to automatically improve targeting and optimization solutions. Our algorithms have been fine-tuned over more than one Trillion transactions from all over the globe, and from leading industry brands and top publishers. Our algorithms have been processing this data from the very beginning, creating a mass of priceless insights that our buyers can now use for targeting purposes to ensure that the changes brought on by GDPR and other cookie restrictions will not affect their ability to generate the results they anticipate.