It has never been more crucial for brands to maintain meaningful connections with their audiences at scale.
Advertisers need resilient, agile and durable audience strategies that build consumer trust and achieve campaign objectives. With the 2024 deprecation of third-party cookies, there’s an opportunity now to refine these strategies for continued performance.
The concept of “durability” will be key. Advertisers can think of durability as the ability of audience strategies to stand the test of time. That means advertisers should be confident that they’ll continue to connect with the right customers, regardless of the deprecation of third-party cookies. But it also refers to audience strategies that can endure the ebbs and flows of larger changes, like developments to the macroeconomic climate or the evolution of shopping behaviors.
The importance of first-party signals
It starts with making the most of first-party signals.
According to the IAB, 41% of U.S. advertisers are expected to increase their spend in relation to using their own first-party signals. Unlike third-party cookies, a brand’s first-party signals aren’t contingent upon changes made by web browsers or device manufacturers and can be joined with signals from ad tech providers such as Amazon Ads to further extend reach.
Amazon Ads works to make meaningful connections with customers across a wide variety of experiences and touchpoints. From there, advertisers can get a breadth of shopping, browsing, and streaming signals that enable them to understand more about their customers and their interests—whether they’re marketing a product sold on or off Amazon. These signals can then provide additional interests-based segments to add to campaigns, while machine-learning capabilities can help extend reach in ways that go beyond traditional look-alikes.
In fact, first-party signals can work even harder. Clean rooms have become a necessary part of a brand’s tech stack for many, but largely for measurement purposes. Today, however, it’s possible to leverage the power of clean rooms—such as Amazon Marketing Cloud—for fully customized audience building and activation.
For example, Amazon Ads recently helped a retail advertiser drive incremental sales from existing buyers who had previously shopped for related category products. Amazon Marketing Cloud allowed the advertiser to combine and enrich their own first-party signals with Amazon signals, uncover machine learning-based path to purchase insights, and create a tailored audience strategy that was then activated on Amazon DSP for programmatic campaigns. This approach led to a 152% spend increase and 188% purchase unit increase compared to standard audience strategies.
Moving into modeling and contextual
While first-party signals are incredibly valuable, they can be limited in scale, which is why their potential can be further unlocked with tactics like modeling and contextual advertising, that are becoming more popular.
Using machine learning capabilities, Amazon Ads audience-building models are continuously evolving and improving. By their nature, those models are taking into account all sorts of changes, whether they’re related to a new streaming channel or device, changes to shopping patterns, and more.
In fact, in recent Amazon Ads tests, durable strategies like contextual targeting have driven 29% better consideration rates and a 27% increase in return on ad spend, compared to traditional cookie-based strategies.
Recently, agency Flywheel Digital leveraged Amazon DSP contextual targeting for a global consumer electronics brand and saw a 4X ROAS increase, a 108% increase in engagement and 50% improvement in campaign delivery on previously non-addressable environments. Willie Hall, senior media manager at Flywheel Digital shared that “Contextual targeting will continue to play a key role in our always-on media strategy for our clients. The ability to efficiently and effectively reach incremental, qualified shoppers at a moment in time when they are consuming online content that complements our brand or product is invaluable. This targeting capability unlocks a new method for brands to generate incremental demand, and conversions without relying on ad ids.”
In other tests, advertisers that employ modeled audience strategies also saw an increase in relevant reach in more cost-efficient ways, including a 25% increase in impression delivery and 12% decrease in CPM impressions. Explained Paul Connor, marketing manager for Amazon digital advertising, “[Thanks to modeled audiences,] Amazon Fashion was able to reach larger audiences. We were happy to drive incremental campaign scale, while still hitting our campaign goals in a more cost-efficient way.”
In conclusion, as brands navigate the post-cookie landscape, the focus must be on establishing audience-building strategies that are both resilient and flexible—strategies that not only build consumer trust but also adapt to the evolving digital landscape. By leveraging first-party signals and enhancing them with advanced ad tech solutions like Amazon Ads, brands can achieve a deeper understanding of consumer behaviors. Combining these insights with sophisticated machine-learning models and contextual targeting ensures that advertisers remain connected with relevant audiences
As brands look ahead, those that prioritize agility, consumer trust and innovative targeting will likely find themselves at the forefront of effective digital advertising, ready for whatever the future holds.
—
Ona Prat and Arin Khan are senior product marketing managers at Amazon Ads. Prat spearheads thought leadership and drives adoption of Amazon DSP and its suite of addressability solutions. Khan is responsible for leading the global strategy for insights, audiences, media planning and pubtech.
