By: Mike Hemmings
New regulations such as GDPR and CCPA have been one of the most significant catalysts for change the online ad industry have seen since its beginnings in the mid 90’s.
The proposed purge of 3rd party cookies has created a raft of opportunity for the development of new targeting solutions, but it’s also brought forth a degree of uncertainty around the future of more broadly available targeting solutions for brands, agencies, publishers, and ad-tech vendors that need to be addressed.
For those solutions now coming into market, some of the core needs to be met include:
Oracle Advertising solves these challenges with its latest solution, Content Affinity Targeting. This new offering delivers privacy-friendly targeting segments based on the evolving content consumption patterns of people-based cohorts. Oracle’s best-in-class Contextual Intelligence engine analyzes what a given audience views online to build a profile of the content types they’re most likely to engage with and what they tend to steer away from. The result is a powerful combination, leveraging cohort insights from audience data to find your target customer across a broad array of content.
Leveraging Oracle’s sophisticated deep learning algorithms and statistical semantic natural language processing, the segments place your ads in front of buyers meeting specific behaviors or a range of demographic attributes based on audience data without the need for identity for ad delivery.
Let us imagine we’re a bicycle brand looking to drive awareness for our new model road bike .
Step 1: Oracle calls upon its comprehensive list of proprietary audiences or those from participating. branded data providers, to form the foundational cohort insights. These audiences are grouped into behavioral, buyer or demographic cohorts. In this case we’re looking at the group; cycling enthusiasts
Step 2: We collect the content ingestion patterns of this cohort and disconnect the identity people-based seed from the specific content consumption. We analyse the online content consumed by this audience and compare with the ‘average’ content consumption of a broader audience – this exposes the highly correlated and specific pages for our ideal cohort. For our cycling enthusiasts , these pages could include topics such as green economics and luxury short-haul travel – these would be just two of a multitude of corelated themes.
Step 3: Now that we have our highly correlated pages (and topics), we utilize deep learning algorithms to identify additional content aligned to our cycling enthusiasts , creating a Content Affinity Targeting segment that finds potential customers wherever they might be.
These segments, just like human behavior, will evolve and this why we re-map and recreate these segments every single day to ensure they are continually changing with audience content consumption.
Content Affinity segment are available today across both MediaMath and TheTradeDesk where they can be activated immediately. We currently offer more than 130 lifestyle, retail and demographic segments and are continually adding to this library of targeting options and DSP availability.
To learn more about Content Affinity Targeting, or how to build a comprehensive targeting plan that incorporates 1st and 3rd party audiences, contextual, and content affinity segments, please contact The Data Hotline.
Byline: Mike Hemmings
Mike Hemmings leads the Insights service for Oracle Advertising in EMEA & JAPAC. This consultative service is helping to bring actional insights to brands and agencies to facilitate more effective audience reach and campaign efficiencies. Mike has worked across a multitude of media channels in B2B advertising and marketing for 20+ years as a consultant, as well as leading teams in marketing leadership roles for the likes of CBS, Emap and Grapeshot.