What are lookalike audiences?

Lookalike audiences: Definition

Lookalike audiences (also referred to as "lookalikes") describe a targeting strategy where brands serve ads to people who share behaviors and traits with their existing customers. This allows the company to extend their reach to a broader — yet similar — audience.

By focusing their advertising strategy on individuals who resemble their existing customer base, brands can connect with the people most likely to be interested in the brand’s products or services. This enhances ad relevance and increases the likelihood that ad efforts will result in improved return on ad spend.

However, as the digital advertising ecosystem undergoes fundamental shifts, the role of these audiences is expanding and adapting.

The article explores the role of data in lookalike audiences, their use before and after third-party cookies were a main data source, and the crucial aspect of data privacy and compliance.

The role of data in building lookalike audiences

The quality, quantity, and level of detail of data from both publisher and advertiser are central to creating an effective lookalike audience. 

As their name suggests, these audiences comprise groups of new individuals who share similar characteristics with a predefined audience. This original audience is known as the "seed audience." The seed audience typically consists of existing customers or engaged users. It serves as a foundation for identifying common traits and patterns within the organization’s customer base.

The process of creating a this type of audience therefore begins with carefully selecting appropriate seed audiences. This is normally done at a product or channel level, either because there is a belief that the consumer profile differs greatly or that the quality of data collection varies between channels. 

Lookalike models use data in a very different way than traditional targeting methods to determine which new audiences are the most promising. In traditional targeting, audiences are often chosen based on demographics like age and gender. This model is an advanced machine learning or AI calculation that analyzes all available pieces of data in the seed audience. 

Then, it makes a set of decisions about what indicates a high-potential new customer — rather than just assuming that gender or age plays a role. Factors could be browsing behavior, purchasing patterns, and more. The model then seeks out individuals in the prospective audience that share these traits. This is why having rich, reliable data on your seed audience is crucial to a well-performing lookalike — the more factors the model can calculate, the better quality the result. Beyond just data, the model used within the platform that identifies the lookalikes plays a vital role in pinpointing the traits of the best target audience. All this to say: The success of the model relies on both its sophistication and on high-quality data given to it.

Lookalike audiences with third-party cookies

Before the looming threat of cookie deprecation forced brands to look elsewhere, lookalikes were mainly available on platforms within walled gardens. These platforms put their extensive user data to use to create sophisticated models. For instance, Facebook became synonymous with this approach through its "Facebook lookalike audiences" product. This offer allowed advertisers to enhance their targeting precision by reaching Facebook users who closely resembled their existing audience.

In addition, various demand-side platforms (DSPs) apply their own methodologies enabling their users to create these audiences. For example, some DSPs use machine learning algorithms to identify patterns within the seed audience. The algorithms then map these patterns to a broader audience. Other DSPs may prioritize contextual targeting, focusing on the content and context associated with the seed audience to identify similar individuals.

While walled gardens excel within their closed ecosystems, DSPs offer a more customizable, expansive approach to lookalike creation. However, the methods they used to create lookalikes have — until now — relied heavily on third-party cookies. This means significant changes on the horizon for these audiences outside walled gardens.

Adapting to a strategy that doesn't rely on third-party data

Brands wanting to dedicate advertising budgets to open web campaigns (as opposed to relying only on walled gardens for the creation of their lookalike audiences) face the challenge of adapting to a more privacy-centric landscape. In addition, using poor quality data based on third-party cookies will have a negative effect on both the accuracy and reach of this type of targeting on the open internet. 

Nevertheless, the world outside of walled gardens is still attractive for advertisers: 66% of users’ time online is spent on the open web, and only 34% in walled gardens.

But DSPs that continue to rely on third-party cookies for targeting will experience a steep plummet in the quality of their lookalike audiences. And even if they are able to reduce their reliance on third-party data to generate these audiences, they are unlikely to do so in a privacy-preserving way.

The role of data privacy and compliance in lookalike audience creation

In an era of heightened consumer awareness and strict privacy regulations, striking the right balance between harnessing the power of lookalike audiences and respecting data privacy is more important than ever.

For that reason, creating lookalikes using a solution with privacy guarantees by design makes an important statement about how an organization prioritizes its customers’ data. It also makes this type of targeting much easier to set up and get approved by the legal department.

Strategies, tips, and real-world examples

To maximize the potential of lookalike audiences, start with a seed audience based on high-quality data. Of course, the highest-quality data you have available is your first-party data. This robust seed audience will ensure a solid foundation for the model. Then, adopt strategies that align with your campaign goals, such as running A/B tests to measure campaign effectiveness and continually updating your seed audience for relevancy. 

As brands navigate a new era that leaves unreliable cookies behind, the importance of solutions beyond walled gardens, but which also don’t depend on third-party data, becomes evident. Decentriq’s data clean rooms for advertising are ideal for advertisers seeking a comprehensive and privacy-conscious approach to lookalike audience creation. Contact our team to learn about how you can unlock the full potential of your campaigns with our solution.