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Custom Audiences vs Lookalike Audiences: What’s the Difference?

Social media platforms offer multiple ways to reach users, but two of the most commonly discussed audience types are custom audiences and lookalike audiences. While they are often mentioned together, they serve different purposes and are used at different stages of a marketing strategy.

This guide explains the differences between custom audiences and lookalike audiences, how each is created, and when one approach may be more appropriate than the other.


Understanding Custom Audiences

A custom audience is built from users who have already interacted with a business in some way. These interactions may come from customer lists, website visits, app activity, or engagement with content.

Custom audiences rely on existing signals rather than predictive modeling. Because of this, they are often used when relevance and familiarity are important.

Common sources of custom audiences

  • Uploaded customer contact lists

  • Website or landing page visits

  • App installs or in-app actions

  • Engagement with videos, posts, or forms

Custom audiences are typically used to reach people who already recognize a brand or have shown some level of interest.


Understanding Lookalike Audiences

A lookalike audience is created by identifying users who share characteristics with an existing audience. Platforms analyze patterns from a source audience and then find other users who exhibit similar behaviors or attributes.

Unlike custom audiences, lookalike audiences are designed for expansion, not re-engagement.

How lookalike audiences are created

  1. A source audience is selected (often a custom audience)

  2. The platform analyzes common traits and signals

  3. New users with similar characteristics are grouped into a lookalike audience

The result is an audience that has not necessarily interacted with the brand before, but statistically resembles those who have.

Custom audiences and lookalike audiences are often discussed together, but they serve different roles within a broader audience strategy. For readers who want a deeper foundation, the guide What Are Social Media Custom Audiences and How Do They Work? provides a more detailed explanation of how custom audiences are created and used.

For additional context on how audience strategies are evolving, see The Shift From Third-Party Data to First-Party Audiences.


Key Differences Between Custom and Lookalike Audiences

Although they are related, custom audiences and lookalike audiences serve distinct roles.

Source of data

  • Custom audiences are based on direct interactions or provided data

  • Lookalike audiences are based on modeled similarities

Familiarity with the brand

  • Custom audiences include users who already know the brand

  • Lookalike audiences include users who are new but similar to known users

Primary use cases

  • Custom audiences are commonly used for re-engagement and retention

  • Lookalike audiences are commonly used for discovery and reach


When to Use Custom Audiences

Custom audiences are most effective when the goal is to reconnect with users who already have context.

They are commonly used for:

  • Sharing updates with existing customers

  • Reminding users about content they viewed

  • Following up after an interaction or sign-up

  • Delivering educational or informational messages

Because these users are already familiar with the brand, messaging can often be more specific and contextual.


When to Use Lookalike Audiences

Lookalike audiences are most effective when the goal is to reach new users who are likely to be relevant.

They are commonly used for:

  • Expanding reach beyond existing contacts

  • Introducing a brand to new but similar users

  • Supporting awareness or early-stage discovery

Lookalike audiences work best when the source audience is high quality and representative of the desired outcome.


Audience Size and Precision Trade-Offs

Both audience types involve trade-offs between size and precision.

Custom audiences

  • Tend to be smaller

  • Offer higher relevance

  • Limited by available data

Lookalike audiences

  • Tend to be larger

  • Offer broader reach

  • Precision depends on modeling quality and source data

Understanding this trade-off helps determine which approach aligns with specific goals.


Using Both Together in a Strategy

Custom audiences and lookalike audiences are often used together rather than in isolation.

A common approach includes:

  • Using custom audiences to re-engage known users

  • Using lookalike audiences to reach similar new users

  • Adjusting messaging based on audience familiarity

This layered approach allows for both continuity and expansion.


Data Quality and Privacy Considerations

Both audience types depend on responsible data use.

Important considerations include:

  • Ensuring data was collected with appropriate consent

  • Maintaining accurate and up-to-date source audiences

  • Following platform policies and regional regulations

Strong data practices improve audience quality and reduce compliance risk.


Limitations to Be Aware Of

Neither approach guarantees results.

Potential limitations include:

  • Low match rates for custom audiences

  • Overly broad lookalike audiences

  • Changes in platform algorithms

  • Reduced signal availability over time

Regular review and adjustment are important for maintaining effectiveness.


Final Thoughts

Custom audiences and lookalike audiences serve different but complementary purposes in social media marketing. Custom audiences help businesses reconnect with users they already know, while lookalike audiences help extend reach to new users who share similar characteristics.

Understanding how each audience type works, and when to use them, allows marketers to make more informed decisions and design strategies that balance relevance with reach.

📘 Further Reading

If you’re looking for a deeper, systems-level explanation of how modern platforms build, learn, and refine audiences using signals, data, and inference, see:

Foundations of Social Media Audiences in the AI Era
A 70+ page reference guide explaining the AI systems that shape audience creation, targeting behavior, and performance outcomes.

👉 Available here:
[View the guide →]