Feature
The Gist
- Strategic innovation. Amidst privacy shifts, first-party data emerges as a crucial asset for strategic, compliant audience targeting.
- Audience engagement. Retail and commerce media, leveraging high-quality first-party data, offer new avenues for targeted audience engagement.
- Privacy and performance. Data clean rooms and advanced targeting methods balance privacy compliance with effective audience reach.
Marketing and innovation have always gone hand in hand. But with cookie deprecation finally kicking in, Apple iOS restrictions, privacy taking center stage, and evolving consumer expectations regarding personalization, the struggle between scale, relevance and privacy is getting very real. As a result, marketers are being challenged to innovate with how they will approach cookieless audience targeting and expand on proven techniques to drive the results they need.
Despite this, ad budgets are set to increase in 2024. The growth will, in part, be fueled by continued advertiser reliance on the walled gardens, which still offer advertisers the widest range of advanced audience targeting options at any budget. Of course, the catch is that you never own the data, visibility into the analytics and performance metrics remains limited, and consumers are increasingly wary of the data privacy standards on these platforms, which may negatively impact advertisers.
We can also safely assume that data providers will continue to invest in ramping up their products to give advertisers optimal reach with more privacy-compliant third-party data sets.
Brands, on their part, are increasingly taking ownership of the first party data opportunity to drive safer audience targeting. But aside from these, new techniques, channels and technologies are impacting how advertisers can best reach their audiences without violating privacy regulations.
Let’s take a deep dive into cookieless audience targeting.
Related Article: Third-Party Cookie Deprecation: Preparing for Marketing's Future
1. Rising Channels Powered by First-Party Data
Building first-party data is now non-negotiable for brands to create a sustainable and self-reliant path to reach, engage and nurture their best audiences. For companies that got an early start with building high-quality first-party data, the shrinking access to cookie-powered audience data has actually proved to be a blessing in disguise. Not only in terms of audience ownership but also in the opportunity to monetize their first-party data in various new ways in a privacy-first world.
Commerce Media
One of the most successful audience-targeting innovations in recent times, commerce media, is built on brands with wide and deep access to high-quality first-party data. Retail media, a subset of commerce media, is leading the charge, offering advertisers a balance of data quality and scale for addressable audiences. Advertisers will be further encouraged to invest in retail media with IAB’s recent release of guidelines and standard terms for the Retail Media industry for both — the U.S. and European markets.
A Growing Opportunity
No wonder then, retail media is slated to be a $60 billion opportunity for retailers by 2025. The larger commerce media pie is likely to grow even faster, with financial service providers, airlines, hotel chains, and any company with large first-party data sets gearing up to build a media network and monetize their captive digital and in-person audiences to generate high-margin revenues.
The emergence of first-party data as a strategic business asset is also bound to set off a range of new collaborations between first-party data owners across verticals and industries for both: smarter targeting and broadening the base of addressable audiences.
Related Article: The Impact of Google’s Third-Party Cookie Deprecation
2. Data Collaborations Enabled by Clean Rooms
Like real-life clean rooms designed to minimize any chance of “contamination” in industries such as aerospace and pharma, Kimberly Bloomston, chief product officer of LiveRamp, defines clean rooms as safe and neutral spaces for data collaboration and partnerships to exist without either party (or parties) having access to the other’s data.
Targeted Reach at Scale
Data clean room technology is enabling data collaborations across brands, data owners and publishers for a range of use cases — from better measurement to improved analytics. In the context of audience targeting, however, data clean rooms are a viable solution to enable targeted reach at scale via interesting new industry collaborations.
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Combining Data to Enrich Records
Clean rooms let two or more companies come together privately and securely, combine their data to enrich records, and run deeper analyses for advanced segmenting and targeting. Finding the right clean room and collaboration partner(s) however is a discussion for another story.
Walled Gardens
Walled gardens such as Amazon — well-known for not sharing data with advertisers — also now offer advertisers clean rooms where they can run their own analysis to sharpen targeting and improve ad spend efficiency. Of course, it all still occurs within the walled garden, but it’s still a step up to help reach audiences at scale and better analyze outcomes without compromising consumer privacy.
Related Article: First-Party Data: Getting Creative for Cross-Channel Identification
3. Enhanced Reach Powered by Advanced Targeting Techniques
Audience targeting techniques have come a long way from cookie-powered behavioral targeting, which served ads to audiences based on their past behavior. There is a growing acceptance that ad relevance comes from much more than a user’s past digital behavior. As a result, advertisers are experimenting with combining proven audience targeting techniques and channels in new ways, not just to reduce dependence on third-party data but also to boost addressability, relevance, scale and performance.
Relevance & Scale
More than any other targeting technique today, contextual advertising offers the relevance and scale to deal with the loss of third-party cookies. Contextual/native targeting is exciting for several reasons, says Kendl Friedman, chief operating officer of the audience targeting agency Semasio, particularly in the current landscape where privacy concerns and changes in data tracking mechanisms are reshaping the digital marketing industry.
Specifically, because it relies on the context of the content being consumed rather than individual user data, it is more privacy-compliant and less intrusive. Because the ad is delivered based on the content a user is currently engaged with, it creates a less disruptive user experience and increases the likelihood of audience engagement. Finally, she argues, it allows advertisers to have more control over where their ads appear, which helps maintain brand safety.
Combining With AI
However, the true transformation of contextual advertising comes when combined with advancements in natural language processing and AI. The new contextual targeting has come a long way from the days of being based on keyword matches, says Friedman. Contextual targeting today leverages advanced semantic targeting, NLP, and data analytics to help create detailed contextual segments based on an understanding of the meaning of the page and ensure it is the “right” context.
Going Deeper Into Context
For instance, Gretchen Smith, VP of media at audio and podcast advertising agency Ad Results Media, says her team has been increasingly relying on spoken word to leverage contextual advertising in podcasting. They have found that building smarter contextual buys means moving beyond simple keyword targeting and delving deeper into context. For example, the word “shot” can be used and interpreted in multiple ways:
- “Take a shot of alcohol”
- “He shot the basketball into the hoop”
- “Someone was fatally shot”
All three are incredibly different in context but use the same keyword, meaning an advertiser could end up serving contextually irrelevant content.
More Precise Targeting
This ability to interpret and layer context and user intent data will lead to more precise targeting and relevance without impacting scale and reach. No wonder then, AI-powered contextual advertising, including keyword, category, and advanced semantic targeting, is predicted to cross $500 billion by 2030.
Geo-Targeting
Another targeting technique coming out of its silo is geo-targeting. Smith opines that geo-targeted audiences help enable brands operating under varied state-specific legal frameworks to serve unique messages in each market. In a world where products and advertising regulations differ significantly by state, she says geo-targeting enables advertisers to scale, comply and engage audiences that may otherwise be inaccessible. Friedman agrees, adding that merging precise location data with privacy-forward targeting options like contextual advertising and layering in AI isn't just innovative; it's transformative.
Personalization
One of the most useful applications of AI and ML in marketing is personalization. Marketers can analyze vast amounts of data to understand customer preferences, behaviors and patterns and tailor ads and content to be more contextually relevant and personalized. The other important application of AI, adds Friedman, is in the data modeling process. The ability to have a predictive model learn from seed data as a proxy creates another level of accuracy in finding lookalike consumers, and the ability to predict where target consumers will be online.
A Sweet Spot for Advertisers
For example, Friedman suggests that interest-based targeting can be combined exceptionally well with contextual targeting. Aligning ads with the content that target audiences are interested in can be a sweet spot for advertisers. It’s about understanding where consumer interest and intent are headed, based on content consumption. Here too, AI can help learn what is important to an audience from cohort targeting and then model where interest and online presence intersect.
Ultimately, companies will leverage deep first-party behavioral data to train the AI models that will power contextual and interest-based targeting and model lookalike and predictive audience segments for the widest reach.
Final Thoughts on the Future of Cookieless Audience Targeting
Moving away from cookie-powered behavioral targeting doesn't have to mean losing out on relevance, scale, precision or personalization. The future of cookieless audience targeting will leverage a combination of techniques that bring the best of context, intelligence, and consent-based targeting that let advertisers strike the right balance between increasingly aggregated and anonymized audiences and first-party data.
