What is Lookalike audience and how it helps in advertising and sales

In advertising campaigns, the quality of the audience is often more important than its size. For business owners, a properly selected target audience reduces the cost of customer acquisition and increases conversion. Therefore, in this article, we will consider what a lookalike audience is, how and why it works, and what benefits it brings to businesses. We will describe step-by-step how to create one in Meta (Facebook) Ads, as well as highlight the key features of using LaL audiences in 2026.

What is a lookalike audience?

Lookalike (LAL) audience is a tool of advertising platforms that allows you to find new users who are similar to your existing customers or to another source audience (seed audience). You provide the platform with a «sample» – a list of customers, website visitors, app users, or other signals – and the algorithm finds users with similar characteristics and behavior. The goal is to scale traffic while maintaining relevance.

Why lookalike audiences are effective

The algorithm analyzes the behavioral and demographic patterns of the audience: purchases, interactions with content, conversion events, etc. It then finds users with similar profiles that you would be unlikely to reach with simple interest targeting. This gives you two key benefits: first, better relevance, and second, budget savings for testing large segments. Meta is also introducing automated modes (e.g. Advantage+ / system alarms) that combine LaL with machine learning to optimize impressions in real time.

When to use and what are the benefits

Lookalike audiences should be used when you already have a high-quality source segment with signs of a «quality» customer, i.e. one who has made repeat purchases, has a high average check, and actively interacts with the product or company.

So, the main advantages of using lookalike audiences are

• Increased reach of the relevant audience without manual selection of interests;
• better CPA (cost per action) compared to broad targeting at the initial stages;
• the ability to scale winning creatives: when a creative shows results on LaL, it is easier to scale;
• compatibility with various data formats: CSV customer databases, pixel segments, lists of offline events.

For a better understanding, let’s take a look at the types of lookalike audiences and the recommended use:

For optimal performance, the audience source must be of high quality and representative, as the algorithm learns better from clear examples of «quality» customers.

How to create and set up a lookalike audience in Meta

The process of creating a lookalike audience in Meta consists of several logical steps that should be followed sequentially. The main idea is to create a seed database correctly and set up the search parameters for «similar» users correctly.

1. Go to Ads Manager.

The first step is to open Ads Manager and go to the Audiences section. Here you can create Custom Audiences and Lookalike Audiences. If you don’t have an existing database, you should prepare it in advance. For example, export the list of customers for the last period. For an online clothing store, this is often a database of users who have already made purchases – in this case, Meta focuses on those who have a real commercial history.

2. Create a new type of audience.

After clicking «Create Audience» and selecting «Lookalike Audience,» the system will offer to select a source and set parameters. It is worth making sure that the source database contains exactly those people whose behavior is most valuable to you.

3. Selecting a seed audience.

Lookalike is built exclusively on the basis of an existing segmented group. Audiences with a clearly defined value are best suited for this purpose: for example, buyers, users with high LTV, or active participants. If, for example, a mobile learning app forms a group of users who have not only installed the app but have completed the first lessons, the system will search for people with similar usage characteristics. This way, you immediately increase the likelihood of attracting users who are ready to take action, not just browse.

4. Geographic parameters.

The next step is to choose a country or region. Lookalike is created separately for a specific country. For a better understanding, let’s imagine that an online platform already has a high-quality segment of buyers in Ukraine. Then you can create a LAL for Poland or the Czech Republic, testing the potential of new markets and adjusting the budget depending on the first results.

5. Choosing the size of the audience (1-10%).

A segment of 1-3% usually provides higher accuracy because the algorithm selects the most similar users. These are the ranges most often used to start, and when scaling up, wider ranges (for example, 4-7%) are added to increase coverage.

6. Saving and forming.

After confirming the settings, Meta automatically builds a lookalike model. It usually takes a few hours. After changing the status to Ready, the audience appears in the list of available segments for selection at the ad-set level.

7. Adding to an ad campaign.

After creating a lookalike, go to the ad-set settings and select a new audience. As a rule, additional narrowing by age or interests is not recommended, as it can limit the algorithm. This is especially important during the testing period when Meta needs to collect conversion data.

8. Testing variants.

In most cases, it is advisable to create several lookalike audiences in parallel. For example, one by purchases, another by subscribers, and the third by behavioral signals, such as adding to the cart, and spending a long time on the site. This approach allows you to compare segments and choose the most effective one. For example, a mobile app creates a lookalike first for those who have installed the app, and then separately for users who return regularly. In practice, the second option often provides better results.

9. Regular data updates.

The audience of the original database should be updated when new users appear or the customer profile changes. If your business has scaled up and entered new markets, it makes sense to create separate Lookalike segments for new regions. This way, the algorithm works with relevant signals and builds models based on the latest commercial results.

Features of the use of lookalike audiences in 2026

In 2026, lookalike audiences can no longer be seen as a simple «tick» in advertising settings – it is an element of a strategic approach to data that requires systematic organization, control, and constant testing. Therefore, let’s focus on important nuances.

Quality of source data

High-quality input signals are the basis of a successful LaL model. In addition to prioritizing events with high intent, such as purchases, payments, or subscriptions. You also need to ensure that the data is clean and correct: remove duplicates, normalize contact formats, and eliminate anomalous transactions. Regular validation of sources, conducted every month or quarter, helps to protect the model from signal drift and maintain stable forecast quality in the long run.

The role of automation and algorithms

Automated modes significantly speed up the search for relevant groups, but they work like a black box: the system makes decisions based on many internal signals. That is why it is important for advertisers to combine automation with hypothesis testing – to plan experiments, record changes in settings, and make decisions based on statistically significant results. In addition, you should track how automation affects LTV and long-term ROAS, not just short-term CPA.

Combination of signals and cross-channel aggregation

The wider and deeper the set of signals, the more stable the model. The combination of web behavior, mobile activity, video interactions, and offline transactions gives a more complete picture of the user and reduces the risk that the model will «redirect» untargeted traffic. The practical step is to set up an ETL process to collect and bring heterogeneous data into a common format so that the algorithm has access to end-to-end signals and better separates relevant patterns from noise.

Systematic testing of the audience scale

Testing should be based on an approved plan. There should be control and experimental groups, clear KPIs, and data collection periods. Regular comparisons of narrow (1-3%) and wide (4-10%) segments in different geographies and niches provide insights into the effectiveness of scaling. It is useful to use separate tests that show whether advertising really gives an additional result, and not just «drags» users who would have come from your other channels. That is, you see the net effect of advertising, not a mixture of existing traffic.

Ethical and legal aspects

Compliance with local and international personal data requirements is not an option, but an obligation. This includes documented data retention policies, anonymization processes, and transparent consent mechanisms. It’s also important for businesses to have internal guidelines for restricting access to databases, auditing logs, and regularly checking compliance with applicable laws and platform policies.

In 2026, the effective use of lookalike audiences requires businesses to be systematic: clean data, thoughtful testing, automation control, and a responsible attitude to privacy. Only such an approach will ensure a steady increase in traffic relevance and long-term benefits from advertising investments.

Olha Tyshchenko

Editor

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