Synthetic Audiences Explained: How Marketers Are Using AI to Predict Campaign Performance in 2025

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Why Synthetic Audiences Matter in 2025

It’s harder than ever to plan an ad campaign with confidence.

The tools marketers once relied on, third-party cookies, detailed panel data, broad survey access, are no longer guaranteed. Privacy laws have tightened. First-party data is often incomplete. And audience behavior continues to shift.

At the same time, pressure to get creative right before launch has only grown. Teams need faster insights, better segmentation, and a clearer view of what might work.

That’s where synthetic audiences come in.

Built using public data, behavioral signals, and AI models, synthetic audiences let marketers simulate how different groups might respond to an ad, a headline, or a value proposition. They offer a way to test, iterate, and forecast before spending money on media.

In 2025, synthetic audiences are becoming a core part of campaign planning. Not as a replacement for real people, but as a way to make smarter decisions when time, signal, and budget are limited.

What Are Synthetic Audiences?

Synthetic audiences are modeled groups that represent real people. They aren’t actual individuals, but they behave like them in controlled environments.

These audiences are built using a mix of public data, licensed datasets, and machine learning models. Each one is designed to reflect a specific population, like Gen Z students, mid-career parents, or retirees in rural areas.

Marketers use synthetic audiences to simulate how these groups might react to creative work. You can test a message, a tone, or a design without running a live campaign. It’s like a digital focus group, but faster and more flexible.

Instead of waiting for post-campaign results, you can get early signals on what might resonate and why.

Synthetic audiences don’t replace real testing. But they give you a way to explore ideas, stress-test assumptions, and plan with more context.

Why Marketers Are Turning to Synthetic Audiences Now

Segmentation is getting harder.

Third-party cookies are on their way out. First-party data often leaves gaps. And privacy regulations limit how much you can know about real people.

This shift has left many marketing teams with less signal and more uncertainty. It’s harder to answer basic questions like: Who is this message for? Will it land the way we think it will?

Synthetic audiences help fill that gap.

They let you test ideas before launch, across specific groups. You can model how different segments, like single parents, bilingual teens, or early-career professionals, might respond to your creative work.

You can also explore scenarios that are difficult to test with real people. What happens when a message feels too bold? Or when cultural cues get interpreted in unexpected ways?

With synthetic audiences, you can run these tests early. You can explore edge cases. You can get direction without waiting for results after the fact.

For marketers working with less data and tighter timelines, that’s a useful advantage.

How US Bank Uses Synthetic Audiences

US Bank wanted to understand how different customer groups think about money. Instead of relying only on focus groups or surveys, they turned to synthetic audiences.

Working with a partner called Supernatural AI, the bank built five modeled audience profiles. Each one reflected a real segment they wanted to reach, based on life stage, income, education, and household structure.

The team then used these models to explore how people might respond to specific messages. For example, how do couples decide who manages shared finances? What emotions come up when talking about saving or investing?

Some of these questions would be hard to ask directly. But with synthetic audiences, the bank could simulate realistic responses and test different ideas.

To validate the models, they ran the same creative tests with real audiences. The results aligned more than 90 percent of the time.

This gave the team confidence to move faster. They still applied human judgment to final decisions. But synthetic testing helped surface insights earlier, and opened up areas traditional research might miss.

Read more: AdExchanger – “US Bank Is Using Synthetic Audiences To Generate Real Customer Insights” by Allison Schiff

What You Can Do With Synthetic Audiences

Synthetic audiences are more than a research shortcut. They’re a tool for planning, testing, and improving creative work across the entire campaign lifecycle.

Here are some of the ways marketers are using them today:

• Creative testing: Try different headlines, visuals, tones, and calls to action to see what resonates with specific segments.
  
• Persona development: Build data-informed profiles when first-party data is limited or incomplete.
  
• Media forecasting: Predict how different groups might engage with your creative across channels.
  
• Multicultural messaging: Test how language, tone, and imagery land with different cultural or linguistic groups.
  
• Clean room simulations: Evaluate ideas in privacy-safe environments without relying on personal data.
  

Each of these use cases helps marketers reduce guesswork. You can explore what’s likely to work before you spend on media, and adapt creative to match the expectations of the audience you want to reach.

Synthetic audiences give you time back, and a clearer path forward.

Benefits of Synthetic Audience Modeling

Synthetic audiences help you move faster without losing focus.

They let you test ideas early, explore edge cases, and model responses across a wide range of segments. That’s useful when timelines are short and data is limited.

Here are the main benefits:

• Speed: Get feedback in hours, not weeks.
  
• Scale: Simulate broad or niche segments without needing to recruit real participants.
  
• Flexibility: Test different tones, visuals, and value propositions before you commit.
  
• Safety: Explore sensitive questions or high-risk scenarios without exposing real users.
  
• Efficiency: Cut down on long research cycles and focus your efforts where they matter most.
  

Synthetic models won’t replace lived experience. But they give you a fast and structured way to see how ideas might land, and avoid surprises after launch.

What to Watch Out For

Synthetic audiences are powerful, but they’re not perfect.

They simulate behavior based on data and assumptions. That means the quality of the output depends on the quality of the input.

Here are a few things to keep in mind:

• They’re directional, not definitive: Use them to explore ideas and narrow your options, not to predict exact outcomes.
  
• Bias can creep in: Every model is built on assumptions. Be mindful of how those assumptions might shape the results.
  
• Validation still matters: Wherever possible, compare synthetic responses with real-world performance. Look for patterns, not just answers.
  
• They’re not a replacement for human judgment: Use them to inform decisions, not to automate them entirely.
  

Synthetic audiences are most valuable when paired with thoughtful strategy. They help you ask better questions and get to better answers, but only if you stay critical and curious.

The Future of Segmentation: Look Ahead, Not Back

Traditional segmentation has always looked backward. It grouped people by age, income, or location, then tried to guess what might work next.

Synthetic audiences shift that approach. They let you look ahead.

You can explore how a new idea might land before it reaches the market. You can test creative in controlled conditions. You can build and refine personas without waiting for real-time campaign data to come in.

This doesn’t replace experience. It adds to it.

In a world where attention is short, budgets are tight, and signals are fading, the ability to simulate outcomes before launch is no longer a luxury. It’s becoming a necessary part of the creative and media planning process.

Synthetic audiences won’t solve every problem. But they give teams a head start, and a way to make decisions with more clarity, even when the path forward isn’t clear.

Smarter Planning Starts with Simulation

Good marketing comes down to understanding people. That hasn’t changed.

What’s changing is how we get there.

Synthetic audiences offer a practical way to test ideas early, refine creative choices, and reduce uncertainty before a campaign ever goes live. They won’t replace real-world feedback, but they give you a head start, especially when time, budget, or data are in short supply.

As audience signals become harder to capture, tools that help you simulate and plan will only grow more important.

If your team is navigating tighter timelines or wants to stress-test ideas before launch, synthetic audiences are worth exploring.

They’re not a shortcut. They’re a way to think more clearly before you move.

FAQ: Synthetic Audiences in Marketing

What is a synthetic audience?

A synthetic audience is a modeled group that represents real people. It’s built using public data, third-party research, and AI models to simulate how specific populations might respond to ads, messages, or product offers.

Are synthetic audiences real people?

No. They aren’t individuals. They’re simulations that reflect behavioral patterns and preferences of real-world segments. Think of them as stand-ins for testing ideas before launch.

How accurate are synthetic audiences?

They’re directional tools, not exact predictors. When validated against real audience responses, they often align closely, sometimes over 90 percent. Still, it’s important to compare synthetic results with live data when possible.

Can synthetic audiences replace real testing?

No. They’re not a replacement, but a supplement. Use them to explore ideas early, refine creative, and reduce risk before going to market.

What kinds of teams use synthetic audiences?

Brand marketers, creative strategists, media planners, and research teams all use synthetic audiences to test messaging, shape personas, and forecast outcomes across different segments.