Businesses are under constant pressure to make faster, smarter marketing decisions. Yet the traditional tools they rely on can be costly, slow, and limited in scope. Conducting surveys can take weeks, and many consumers are either too busy to participate or provide answers that don’t reflect their real behavior.

Enter synthetic audiences. While still an emerging technology, they are being piloted by forward-thinking companies looking for an edge in market research. Synthetic audiences simulate how real customers might respond to campaigns, products, or messages. These tests leverage artificial intelligence (AI) to deliver insights in minutes, instead of weeks.

This doesn’t mean they’re a complete replacement for traditional research, at least not yet. But as adoption grows, synthetic audiences represent a powerful new tool that could transform how businesses understand and engage their markets.

What Are Synthetic Audiences?

Synthetic audiences are AI-generated, data-driven models of real people that simulate how specific customer groups might respond to marketing efforts. As described by Constantine von Hoffman, managing editor of MarTech, synthetic audiences are the digital twins or avatars of customers. Instead of relying on live focus groups or lengthy surveys, companies can test ideas on these avatars.

For example, a software company could use a synthetic audience modeled after IT decision-makers in mid-sized manufacturing firms to test new product messaging. The test might reveal that this audience responds more favorably to messages about systems integration and ROI over those about cutting-edge features. Armed with this insight, the company can fine-tune its campaign before launch.

How Synthetic Audiences are Built

Synthetic audiences are created using a mix of real-world data sources:

• Demographics.

  These factors include age, gender, income level, company size, organizational role, or industry.

• Psychographics.
  

  Values, priorities, and buying motives are important considerations. For example, a CFO prioritizes cost savings, while a CMO focuses on growth and brand equity.

• Behavioral data.

  Synthetic audiences can replicate purchase history, past campaign responses, website activity, or media consumption patterns.

• Predictive modeling.

  Advanced AI algorithms forecast how these profiles would likely react to a new campaign, message, call to action, or product offering.

By using synthetic audiences, businesses don’t need to recruit busy executives into a focus group. Instead, they can build digital twins of key decision-makers that represent their market segments and buyer personas.

For example, a marketing agency is planning to launch a new service. They could create synthetic audiences for different personas, including CEOs, CMOs, marketing directors, and sales directors. Each avatar reflects how those roles typically evaluate vendors:

• A CEO may value long-term ROI and strategic alignment.
• A CMO may prioritize lead generation and brand growth.
• A marketing director might look for execution support and campaign efficiency.
• A sales director may care about conversion rates and pipeline quality.

The team can then test different campaigns to see which resonates most strongly with each group.

Pros of Using Synthetic Audiences

The appeal of synthetic audiences lies in the clear advantages they offer over traditional research. From faster insights to reduced costs, these digital twins give companies the ability to test ideas at scale and with far less friction. Benefits include:

1. Cost Savings

Traditional research methods such as focus groups, surveys, or consumer panels involve recruitment, logistics, facilitator fees, and incentive payments for participants. Synthetic audiences eliminate many of these expenses by simulating consumer reactions digitally. Companies can run multiple tests for a fraction of the price, freeing up resources that can be allocated toward campaign execution or product development.

2. Speed

Traditional research often takes weeks to recruit participants, run sessions, and analyze results. Synthetic audiences deliver insights in minutes or hours because there is no recruitment or scheduling required. Businesses can move from idea to decision almost immediately, keeping pace with competitors and consumer trends.

3. Scalability

Traditional research has practical limits. It’s expensive and logistically challenging to test across dozens of markets or hundreds of variations. Synthetic audiences remove these barriers by allowing marketers to run tests simultaneously across multiple demographics, geographies, and psychographics.

4. Filling Data Gaps

Companies often lack reliable data when entering new markets or targeting niche audiences. Recruiting real participants from these groups can be difficult and costly. Synthetic audiences provide a digital twin, allowing businesses to model likely consumer behavior even when real-world insights are limited. While not perfect, these models help point brands in the right direction and reduce blind spots.

5. Risk Reduction

The first step to launching a new product is identifying potential risks. Synthetic audiences allow businesses to do this before committing significant resources. This approach reduces the likelihood of high-profile flops or wasted marketing spend.

Cons of Using Synthetic Audiences

As promising as synthetic audiences are, they come with limitations. Accuracy, bias, and over-reliance on AI models raise important questions about when and how they should be used.

1. Accuracy Concerns

According to Nielsen Norman Group (NN/g), one of the limitations of synthetic users is that they tend to be much more positive than real humans. For example, AI chatbots tend to want to please, known as sycophancy or insincere flattery. Synthetic audiences are predictive models. While they are built on real-world data, they cannot fully replicate human emotion, cultural nuance, or spontaneous behavior. Businesses that rely solely on synthetic insights risk missing subtleties that only real people can provide.

For instance, when NN/g asked if the synthetic respondent had taken the online courses, the avatar said it always completed all online courses as planned. Yet the human participants in their study tell researchers that they often didn’t complete online courses that they began.

2. Bias Risks

Models are only as good as the data they are built on. In an interview with Market Research Institute International, Will Cimarosa, senior vice president of market research at Suzy, warned that synthetic audiences are only as reliable as the data they are trained on. “There are so many subconscious drivers going on there, like how is that kind of dynamic dealt with… what is the quality of the training set?” he said. If training data is biased, for example, skewed toward Western consumer behavior, the synthetic audience will reflect that bias. This can produce inaccurate or misleading insights for diverse global markets.

3. Over-Reliance

Synthetic audiences are best used as a first layer of testing. They should not replace traditional research entirely. Businesses that rely on them exclusively risk missing valuable insights that capture motivations, emotions, and cultural context.

4. Trust Concerns

Interested parties may hesitate to make high-stakes business decisions based on insights from avatars rather than real audiences. Building confidence in synthetic research requires transparency about how audiences are modeled and, ideally, validation through real-world testing.

Getting Started With Synthetic Audiences

Synthetic audiences aren’t a silver bullet, but they are fast becoming a must-have tool in the modern marketer’s toolkit. The smartest move for businesses is to start experimenting. Run small tests, validate results against traditional research, and use those insights to refine strategies.

To begin integrating synthetic audiences into your marketing workflow, consider these steps. Each one provides a path to test the technology without overhauling your entire strategy.

• Find the right technology provider.

  Evaluate vendors based on transparency, data sources, and model accuracy. Look for providers that can explain how their synthetic populations are created and validated, and how they ensure clear reporting on bias detection and data provenance. Emerging vendors like AdSkate, Quantiphi, and others offer services that help companies build, test, and interpret synthetic audiences. Working with experienced providers can shorten the learning curve and ensure ethical, transparent use of the technology.

• Define a specific question or hypothesis.

  Start small. For example, test how different price points or messages might influence decision-makers in a target industry. The clearer the question, the easier it is to measure the value of synthetic feedback.

• Select a pilot campaign or use case.

  Choose a campaign where audience understanding is critical, such as a product launch, rebranding effort, or new market entry. Synthetic audiences can quickly simulate reactions and highlight areas for adjustment before spending heavily on advertising.

• Compare synthetic insights with real-world data.

  Use your CRM or analytics tools to benchmark AI-generated audience responses against actual engagement and conversion data. Over time, you’ll learn where synthetic insights align and where human nuance still prevails.

• Create an internal governance framework.

  As with any AI-driven initiative, synthetic audience testing should include guidelines for data privacy, bias evaluation, and model transparency. This approach safeguards trust and ensures insights are actionable rather than speculative.

As AdSkate puts it, “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.”

The Competitive Edge of Early Adoption

Synthetic audiences mark a significant shift in how marketers test, predict, and refine campaigns. As AI capabilities advance, these digital proxies for real customers will move from experimental tools to essential components of modern marketing. Companies that start exploring now will set the benchmarks for how data, creativity, and automation intersect to shape smarter decision-making.

Early adopters will gain the advantage of speed, precision, and foresight. By blending synthetic insights with human expertise, marketers can anticipate audience reactions, optimize campaigns before launch, and stay ahead of shifting customer expectations.