If you thought GDPR and CCPA added complexity to the digital advertising ecosystem, brace yourself: they were just the tip of the iceberg. Fortunately, there are really effective solutions out there.
Privacy Won’t Kill Data-Driven Advertising
Today, 69% of countries have adopted consumer data privacy legislation, and 10% have legislation under review. Only 5% of the world has no legislation whatsoever. These regulations seek control over data collection to one degree or another.
Do these developments spell the end of digital advertising? Does that, in turn, mean the end of free, and low-cost content for everyday people? Fortunately, the answer is no. For two key reasons.
First, thanks to next-generation companies like 1plusX, publishers and marketers can deploy AI to understand users better and predict behavior, enabling them to target and engage their ideal audiences in privacy-compliant ways. Second, while third-party data is increasingly prohibited, plenty of highly relevant and privacy-compliant data is still available. What’s more, this data will better serve all parties involved, including the publisher, advertiser, and most of all, the user.
The Hyper-Valuable Data Publishers Don’t Know They Have
Publishers, especially those with large media properties, already have a wealth of data they can leverage. However, it’s different from the typical third-party datasets that aggregators have traditionally collected and sold.
A small portion of that first-party dataset is deterministic. This is the data the user opts to disclose during the registration workflow (e.g., age, gender, household income, specific interests, and offers to redeem).
But let’s not get hung up on size; the girth of the deterministic dataset is irrelevant. When mixed with the publisher’s media asset data, its actual value is revealed. When these two data sources are combined the result is unique, high-quality audience segments that can drive precise targeting across channels.
“AI can step in where deterministic data ends. When deployed correctly, it can predict unknown user demographics and psychographic attributes with a high degree of accuracy,” explained Jürgen Galler, CEO of 1plusX.
What’s meant by media asset data? This is data that goes beyond the metadata that categorizes the subject of a piece of content and is the publisher’s secret sauce. Media asset data incorporates the content the users read or engage with, the specific words, personalities, and sentiments expressed in the article, images, and videos, as well as the user signals that such engagement sends. Capturing this data represents untapped potential for both publishers and advertisers.
Unlike third-party data, which is always predefined, AI can make a deeper set of predictions about a user’s behavior based on the content they engage with (or opt to ignore) within a specific media property. By running this data through myriad models, publishers can predict a huge array of attributes, including demographic, socio-economic, and psychographic insights. Publishers can also score a user’s likely level of interest in a category.
Things get interesting when publishers begin to layer on the results in precise ways, leading to better understandings of an audience and, ultimately, highly granular profiles for targeting.
Powering the Future of Contextual Targeting
Many people rightly claim that a privacy-centric world will need to innovate on contextual targeting. However, a current challenge in advertising today is attitude. Specifically, the industry tends to look solely at users for profiling and targeting; we don’t look at media assets as legitimate cohorts for targeting purposes. I guess we’ve been scarred by the faux pas of early contextual targeting.
This is a mistake. Rather than seek users who match an advertiser’s targeting requirements, the industry should also consider targeting media assets that attract users who also fit a campaign’s criteria. This distinction will ultimately allow us to reach a more significant percentage of the desired audience.
To reach audiences at scale, we need to stop thinking of contextual targeting as a tactic that’s limited to the keywords contained in an article or video; we need to expand it so that it incorporates what content triggers (e.g., female readers, readers who’ll be more inclined to click on a specific ad later on).
This approach has many benefits, beginning with it being truly omnichannel. In addition, the same profile definition of an audience can be deployed to target across all digital ecosystems, including CTV.
Can you envision a future in which publishers and advertisers invest in data management platforms for media assets that allows for rich contextual targeting capabilities?
Predictions for an AI-Driven Future
What will an AI-driven, cookie-free future look like for both publishers and advertisers? To begin, both the sell-side and the buy-side will enjoy expanded reach. For example, a publisher might not have deterministic data for all users who are interested in organic wellness products, but by deploying AI to its media asset data, it can identify and monetize all of its users who share that interest. The result will be more efficient campaigns for the advertiser, and higher CPMs for the publisher.
What’s more, AI will drive e-commerce opportunities for the publisher, a trend more media companies are seeking to exploit these days. For example, NBCUniversal’s checkout enables businesses to sell directly to consumers from its article and video content. Imagine if it deployed AI to its media asset data so that its advertisers could reach and engage the entire pool of potential audience members for a specific product.
Galler envisions a scenario in which publishers are positioned to obtain more well-rounded views of users, which has been the Holy Grail of marketing. Users have, on average, 20 different CX touchpoints as they switch between connected devices several times throughout the day. Publishers and brands have struggled to capture all of those signals and tie them to a single user.
AI can leverage probabilistic matching and stitch all of the multiple IDs of a single user together. This AI use case will ensure that when, for example, a user updates their privacy preferences on a mobile device they’ll be honored on all 20 touchpoints. And it’ll enable both the publisher and the marketer to obtain more nuanced audience profiles.
Stronger, Better Relationships Ahead
Ultimately, by combining all sorts of data — and deploying AI for data enrichment to obtain complete pictures of a user — everyone will benefit, including the consumer.
This can be achieved by using a next-gen data management platform, such as 1plusX, that combines first-party data with contextual media strategies and DMP tactics marketers already deploy to expand audience reach. It also enables data collaboration between publishers and advertisers to meet consumers they might have otherwise not been able to target.
Going forward, this will transform the publisher/marketer relationship, prompting both to form deeper connections and better cooperation.