Digital advertising is characterized by constant change, challenges and – fortunately – innovation.
One intriguing and enduring industry plotline is the tension between data-driven targeting and user privacy. The narrative commenced with stricter data regulations. Now, it’s reached a critical point with the deprecation of third-party cookies and reduced access to mobile advertising identifiers.
There is no real replacement for third-party cookies and mobile IDs. So, we are starting to see a patchwork of targeting solutions emerge. We won’t entirely shift away from an identity even as we edge closer to a privacy-first age. Rather, success will be determined by a combination of solutions.
Privacy has been a prominent topic for many years. So has a demand for targeting. The reality is that some sort of ID solution will continue to be used for the foreseeable future. What we’re likely to see, however, is a move from precision-based marketing to prediction-based marketing. Thus, advertisers will increasingly rely on audiences built on the basis of having the highest probability to purchase their products.
Publishers, as the gatekeepers of first-party data, are poised to take a stronger position in the digital ecosystem. This is particularly true for those supported by artificial intelligence (AI) powered predictive analytics. These solutions will allow them to make the most of that precious data and achieve the scale advertisers need.
First-party data, identity, both, or more?
Unlike most participants in the digital advertising supply chain, publishers have direct consumer contact. And their advertising partners will benefit from first-party data drawn from that interaction.
Advances in data enrichment techniques enable publishers to generate user insights directly from that first party data. This allows publishers to offer the scale and quality marketers need to achieve their goals. It is also worth noting that solutions that allow data processing and the extraction of value closer to the source of the data also reduce the risks associated with data sharing.
To make use of their first-party data for targeting purposes, publishers can begin by creating basic reach among users that are known to them through log-in details or subscriptions. They can then form larger addressable audience segments by syndicating those user profiles through identity networks and matching them to a wider identity connectivity layer using AI-powered technologies to orchestrate and consolidate their data.
While the result of this process is a pool of audience insight ready for activation, these steps will still only deliver limited reach. The reality of extracting value from first party data is that established publishers need to balance paywalls and login screens with ensuring a positive user experience. Also, without being able to tap into predictive modelling on top of their first-party data strategies, publishers will struggle to achieve true scale.
From your current targetable audience reach to 100% (really!)
AI-powered predictive modelling allows publishers to monetize their inventory through targeted advertising in a way that is privacy compliant. That’s because it relies on logical, predicted — rather than declared — attributes. To fill the gaps in their data and expand reach, publishers can use algorithms to analyze on-site activity for users that have consented to this practice. Publishers can build detailed user profiles and intricate attribute patterns that include interests, habits, and preferences.
This information can be used for advanced audience expansion. It also enables targeting users with similar profiles, even if they aren’t logged in. It can also be used to predict attributes of anonymous users, such as gender, age, and interests. By combining these privacy-compliant profiles with real-time context and content information, impressions can be made addressable without user-level data.
This approach can even be used for retargeting by matching publisher and advertiser audiences based on similarities, using clean-room technology. With a clear picture of audience trends and preferences, publishers will be able to bring 100% of users within targeting range.
Contextual Targeting supercharged and cross-platform
A third element in the mix will be contextual targeting. However, it will be a more sophisticated AI-powered version of the contextual targeting we’ve known in the past. Using advanced analytics, publishers can determine what content users are interacting with across multiple digital properties. They can use this information to unearth granular interest areas far beyond high-level keywords. Targeting against these specific interest patterns will be particularly useful for native mobile channels, as well as connected TV and podcasts. Critically, it will allow brands to reach users when they are absorbed in relevant content.
This multi-layered approach to audience targeting in a world with no third-party cookies and reduction of mobile IDs will be beneficial across the supply chain. It allows publishers to effectively monetize inventory through relevant advertising that their audiences find engaging and applicable rather than annoying and disruptive. And it can do all this while respecting user privacy. For advertisers, predictive analytics enables them to spot the most receptive audiences at every stage of their digital journey and understand where they are most likely to be next.
As the targeting vs. privacy story heads towards its culmination, industry innovation will see a multitude of targeting solutions emerge. Overall, user-level targeting will shrink and both audience-level and contextual targeting will grow. Predictive marketing powered by AI and publisher first-party data will lead the way into a new age of targeting.