What Are Lookalike Audiences?
A Lookalike Audience is a targeting tool available on several digital advertising platforms. It uses machine learning to identify new people who are similar to an existing group of users—often called a source audience. The source audience could be, for example, your current customers, people who have visited your website, or users who have previously interacted with your ads on social media.
The platform analyzes a vast amount of data points from your source audience. These can include gender, age, interests, geography, purchasing behavior, scrolling behavior, or even device type. Based on these signals, the system builds a mathematical model that identifies new users throughout the platform’s database who share the same characteristics. The higher the quality of your source audience, the more accurate the results will be. The same applies if the data is up-to-date and reflects users’ current behavior.
You can use Lookalike Audiences on platforms such as Meta (Facebook and Instagram), Google Ads, LinkedIn, HubSpot, and TikTok. Although the method is essentially the same, each platform has its own algorithms and options for fine-tuning. This means you can strategically adjust precision, reach, and geographic targeting depending on the campaign’s objectives.
How do you use Lookalike Audiences?
When you create a Lookalike Audience, you start by selecting your source audience. This can be a remarketing list, an email list imported from your CRM system, or users who have filled out a contact form. Next, select the region you want to target—for example, Denmark or Scandinavia—and define how closely you want the audience to match your source. Many platforms let you choose percentage levels from 1 to 10, where lower levels provide greater precision and a smaller reach, while higher levels scale up your visibility.
In other words, you can choose whether to target a narrow audience with high conversion potential or a broader audience for branding and awareness. Small adjustments to percentage allocations have a significant impact on performance, so it pays to experiment systematically. However, this requires that you have a well-structured data foundation—typically from a CRM like HubSpot or through pixel data from your website—and that you work diligently to ensure data consistency across channels such as social media marketing and Google Ads.
An example of how the platforms use data:
That's Why You Should Use Lookalike Audiences
The primary purpose of Lookalike Audiences is to increase reach without sacrificing relevance. You avoid wasting ad spend on overly broad segments because the algorithm identifies users who resemble your best customers. This means you can scale your campaigns effectively while maintaining a high conversion potential. This is particularly valuable when you want to expand your customer base or increase the number of leads without significantly changing your existing strategy.
A Lookalike Audience therefore complements both your SEO efforts and your social media ads. While SEO focuses on long-term organic traffic, Lookalike Audiences help you scale quickly across paid channels. This combination provides data you can use to improve your strategy and budget allocation across your digital channels.
What types and varieties are available?
Although the concept is the same, there are different versions depending on the platform. Meta uses precision percentages ranging from 1 to 10, where 1% corresponds to a very narrow match group and is used for performance campaigns, while 10% covers a broad reach segment. In Denmark, 1% typically corresponds to about 48,000 users, while 10% can cover up to half a million. Google Ads uses Similar Segments, which are automatically generated from your existing remarketing lists in campaign types such as Demand Gen. On HubSpot, you can build Lookalike Lists or Segments based on CRM data, website visitors, or past campaign engagement, while LinkedIn and TikTok offer similar features, targeting B2B and lifestyle segments, respectively.
Example of a combination of platforms:
How do you use Lookalike Audiences in practice?
In practice, it’s all about letting the data work for you. Start with a high-quality source—this could be a list of people who have made a purchase or submitted a qualified inquiry. Make sure your data is up to date and that you comply with applicable GDPR regulations regarding consent and data retention. Next, build lookalike segments with different percentage levels so you can test performance across different campaign types. For smaller budgets in Denmark, 1–3% typically yields the best results.
You can use the new audiences in a variety of contexts. In social media marketing, you can test ad formats such as video or images, while in SEM campaigns, you can use the segment to optimize display targeting in Search or Demand Gen. Regardless of the channel, you should continuously evaluate the segment’s performance using metrics such as CTR, CPC, and conversion rate, and then adjust your strategy so that you’re always working with the best-performing data.
What should you keep in mind?
A Lookalike Audience is only as good as the data you feed it. If your source includes users who don’t represent your core business, the algorithm will learn from an incorrect basis, and your ad performance will decline. You should therefore always keep your source audiences up to date and segment them based on actual metrics such as purchases, leads, or interactions with specific pages. Also, avoid overlap between your existing remarketingand lookalike segments so that your campaigns don’t compete with each other in the auction.
Finally, it’s important to evaluate how Lookalike Audiences fit into the rest of your digital strategy. When used wisely, they can be an effective way to find new customers while continuing to build on your existing performance. This is precisely where you’ll get the most value from combining technical data analysis with strategic marketing insights across everything from design and brand strategy to paid social and Google Ads.
