Why Synthetic Focus Groups Are Gaining Traction in Market Research

Synthetic focus groups simulate real human interactions using AI-generated respondents. You basically collect data about your customers, feed it into a synthetic market research tool, and create synthetic users that respond based on that information. Besides textual chats, you can also use them to run surveys, interviews, and user testing studies.

But what’s the point of synthetic respondents? One might argue that you’re just getting answers you could’ve guessed anyway. What’s so novel about this approach if you’re not really discovering any new information?

Sure, it's cheaper than traditional research methods, which can cost a lot and take time, what with recruiting participants and bringing a credible market researcher on board. Plus, you get simulations that never get tired, are available 24/7, and cost a fraction of the money. You can keep asking questions and tweak your study as needed without worrying about fatigue or conflicting schedules.

There are plenty of other pros and cons to synthetic focus groups, and in this post, we’ll cover most of them, along with how they work and some real-world use cases.

All About Synthetic Customers & Market Research

Synthetic market research leverages AI models and machine learning systems to generate realistic “synthetic users” based on actual audience demographics, psychographics, and behavioral inputs. These synthetic personas behave like virtual customers and respond in human-like ways, letting marketers run research studies without relying entirely on real participants.

Now, are they accurate? That’s debatable.

For example, a study from Google DeepMind and Stanford found that GPT-based “synthetic people” matched real participants’ survey answers about 85% of the time, proving that AI can closely mimic real users when trained on quality human data.

The researchers first conducted two-hour qualitative interviews to collect comprehensive life histories from a diverse group of 1,000 people. These interviews were then fed into GPT-4o to create synthetic personas, or “agents.” Next, both the participants and their corresponding AI agents were asked to complete a set of social science tests, like the General Social Survey (GSS) and the Big Five Personality Inventory.

The results showed that the agents replicated participants’ GSS responses with 85% accuracy.

A similar pattern showed up in a real-world newsroom use case. In a Digiday Podcast episode, Tracy Yaverbaun, GM of The Times and Sunday Times, explained how her team has been experimenting with the synthetic research technology. To test it, they built an AI-generated audience panel with their database of 642,000 subscribers.

The Times then used those synthetic respondents to help finalize a name for a new business podcast. And just like in the DeepMind study, they compared the synthetic responses against real users. The synthetic panel's answers were about 92% accurate, only one percentage point below the traditional research benchmark of 93%.

Of course, not all of the responses made sense.

When asked what new experiences they’d like from the Times, the synthetic customers were all in on summaries, AI explainers, and more automation. The actual, loyal customer base, though, was far less excited about introducing AI into the product.

So it’s a bit touch-and-go, depending on how you look at it. Currently, synthetic respondents offer a space for experimentation, a sandbox for brands to play around in, rather than a replacement for real-world research.

What are Synthetic Focus Groups? Pros, Cons, & More

As mentioned before, synthetic focus groups (SFGs) represent AI personas or simulated users that represent different types of users. These users can talk to each other, react to what others say, and discuss ideas together, much like participants in a real focus group.

SFGs don’t just analyze existing data – contrary to traditional AI analytics or predictive modeling – they actively generate new qualitative insights through textual conversation. And the reason they’re gaining traction these days is simple: traditional focus groups aren’t really doable for small businesses and startups.

It’s the same case for big brands. They’re expensive, time-consuming, and logistically complex. You must recruit participants, design a study plan, and either moderate the discussion yourself or hire a professional moderator, which can make the process even more expensive.

Also, synthetic focus groups allow you to test multiple scenarios, messages, and product concepts quickly. Because they’re AI personas, there’s no bandwagon effect, no single “loud voice” dominating the conversation, and far less social pressure that can influence feedback. They also eliminate moderator bias, since the AI moderator follows a predefined discussion structure.

How synthetic focus groups work in practice

Most synthetic research tools work in a pretty similar way when it comes to focus groups. You pick your panel, share your research goal, and then add a discussion guide. From there, an AI moderator runs the session for you, introducing topics, asking follow-up questions, and keeping the conversation on track.

One of the advantages is that you can run multiple focus groups simultaneously, each tailored to a different customer segment or persona. The results are almost instantaneous, complete with transcripts and easy-to-digest insights, including major themes, sentiments, points of agreement or disagreement, and quotes.

The best part is that you can experiment freely. No idea is too risky or too bold to try.

Marketing teams can run risky campaign ideas through a synthetic focus group first to see how different users react, which headlines resonate, and what messaging falls flat. At the same time, a UI/UX team might prototype a new checkout flow and use synthetic users to walk through it, finding areas where people get confused or drop off. Meanwhile, product teams could test three different pricing models — subscription, pay-as-you-go, and freemium — by running parallel focus groups and seeing which option feels most appealing to each segment.

Since everything happens fast and without real-world risk, teams can try complex ideas and improve them before involving real customers.

Pros and cons of synthetic focus groups

We’re clearly in an era where it feels like you don’t even need people to conduct market research anymore. But is this the next big thing in MarTech or just another AI gimmick?

The main criticism is that AI models depend on historical data, so they’re basically remixing what’s already known instead of producing something new. But that’s not really a dealbreaker if your goal is to understand your existing customer segments.

There’s also the fact that AI doesn’t feel. It’s not human. It doesn’t get annoyed, tired, or biased in the same way people do, which can actually be a plus because those emotions often influence real interview responses.

On the flip side, it’s hard to fully trust the feedback synthetic users generate. After a certain point, the data can get diluted and may stop being a true representation of real human experience. Remember the “AI content ouroboros,” where AI models are trained on AI-generated content?

So why should you use synthetic focus groups?

For starters, most businesses simply don’t have customers who are excited about answering surveys or joining focus groups. In those cases, the synthetic approach can be a practical alternative.

You don’t have to spend money recruiting or managing human participants. AI participants can operate around the clock, which makes your research insanely scalable. You also get the flexibility to create synthetic users from virtually any demographic, psychographic, or behavioral segment. And it’s fast.

Because synthetic customers are based on generalized customer data, they don’t come with the same compliance, privacy, or consent risks as working with real customer data.

Fun fact: SFGs can be used to fill knowledge gaps and forecast demand. For example, in retail, they can help refine products and promotions to capture new market segments. This can, in turn, improve the ROI for every dollar invested and drive profitable growth.

So, How Do You Create Synthetic Customers?

Human behavior is complex, but not all the time. With accurate customer data, you can predict a lot of things. And synthetic customers are a way to do this. Since they’re virtual avatars built from your existing user data, they can be used to simulate real customer behaviors and preferences.

The easiest way to generate one would be through large language models or LLMs like ChatGPT. All it takes is a prompt containing a brief or detailed description of your target audience and your product.

You can, for all intents and purposes, use these “AI personas” to test product concepts, marketing campaigns, and business strategies. However, be prepared for generic outputs and, at times, stereotyped representations of certain demographic groups.

A better way is to use professional synthetic market research tools available online, like Delve AI’s Synthetic Research Software, built specifically for this purpose.

Hailed by Factors AI as an “implementation of the synthetic persona/digital twin ideas that HBR and academics are exploring—but with marketing outputs attached,” Delve AI sets itself apart from generic generative AI tools in how those synthetic users are built. They are derived from personas grounded in real customer data, including CRM records, web analytics, social audience intelligence, and 40+ public data sources. This also includes Voice of the Customer signals pulled from reviews, ratings, community forums, blogs, news channels, and more.

As a result, your synthetic respondents are anchored in how real people actually think, talk, and behave, not just how an AI model guesses they might.

The way it works is simple: you first generate user personas, then create synthetic panels, and finally use those panels to conduct research studies.

Step 1. Generate user personas

Delve AI’s online persona generator currently offers six types, and in this case, we’ll use the Research Persona tool.

Sign in and go to Research Persona. Then, upload your market research documents, such as interview transcripts, user profiles, surveys, or industry news, basically anything relevant to your audience. No research material? No problem. Just add a solid description of your target audience, click Create Personas, and Delve AI will develop personas based on the data you’ve provided.

The platform uses AI and machine learning to create unique audience profiles. Depending on your database and use case, you’ll get one or multiple personas. Each segment includes persona details, audience distribution, influencers, and journey maps.

Click Persona Details to see user demographics, buying triggers, psychological drivers (like goals, motivations, needs), lifestyle, career status, and core challenges. You’ll also find insights into their communication preferences, social networks, brands, shopping sites, YouTube channels, podcasts, subreddits, and more.

Each of your personas is refreshed monthly with new data, and you can continuously improve their accuracy by adding new research materials.

Step 2: Create simulated user panels

The next step is to generate synthetic users or panels for your research study. To begin, go to the Synthetic Research and purchase the number of users you need. Then, create a panel with this group and give it a name, like Marketing Insights Group.

You’ll then be taken to a screen where you can choose which persona or persona product to use for creating synthetic users. For this example, we’ve picked a segment from Research Persona named Edward Collins.

Once selected, the software generates synthetic users based on this persona segment. Each synthetic customer comes with a Start Chat option, allowing you to interact with them directly.

Step 3: Run surveys and interviews

To run a survey or an interview, go to Studies and select Create Study on the dashboard. You’ll be prompted to:

• Choose your synthetic user panel
• Enter your study name (e.g., Product Market Fit Survey)
• Select the number of users (e.g., 100)
• Upload a CSV file containing your survey questions (eg, multiple-choice, rating scale, open-ended, and ranking questions)

The platform runs your survey across the panel and automatically turns the results into charts and themes.

Click the Data button to see how many people responded to each question and who they were. You can also check out the full survey/interview transcripts and sort them by question or by respondent.

Want to go beyond surface-level answers? Use the chat feature to follow up and ask users why they made certain choices. You can also explore other marketing or product questions. For example, in the sample below, we asked a user what influenced their decision to complete a purchase online.

Because everything is structured and tied back to real datasets, our platform can run more controlled simulations than a generic chatbot prompt ever could. You’ll get fewer stereotypes, more consistency, and results that are much closer to something you can actually act on.

Step 4: Conduct focus group discussions

The workflow for conducting focus group discussions is quite similar to the previous step. You simply have to select the focus group option, click on “Create a new focus group,” and fill in the details required. Enter the study name, choose the number of participants (max 15), specify the session duration, and write down the discussion goals and topics. Finally, select the user panel – eg, Marketing Insights Group – you want to use for the focus group.

The next step is to add your discussion guide. You can either enter the discussion sections and descriptions manually or use one automatically generated by Delve AI. If you have any files (JPG, PNG, MP3, or MP4) that you would like to use in the discussion, you can upload them to the platform as well. When everything is in place, review all the details and hit “Run study.”

As you can see, the platform will analyze all the simulated user responses to identify important themes and codes. You will also be able to view the complete transcript of the focus group discussion, along with a summarized report highlighting the key findings.

Getting Started With Synthetic Market Research

You may be thinking: Will synthetic market research do away with traditional, human-based research studies? Right now, that’s highly unlikely.

Synthetic users are great for early-stage testing and predictive modeling, but emotions and subconscious decision-making aren’t something they’ve mastered yet. Because everything is text-based, you can’t really pick up on tone, body language, or the subtle expressions that usually accompany how people talk or react.

AI respondents can also miss cultural and social context, like social trends or slang, since they’re trained on past data. So, while synthetic users are incredibly useful, they’re best seen as a complement to human research, not a replacement.

There are also several things to keep in mind before and during your use of synthetic users:

• Don’t cut corners on data. The quality of synthetic respondents depends entirely on the data used to build them. They’re only as good as the inputs you provide.
• Watch out for bias. AI can introduce bias into your study. For example, it often has a tendency to be overly positive and focus on the best possible outcome.
• Test multiple tools. Try different synthetic market research platforms before committing to one. Ask yourself: Are the personas realistic? Does the tool support the use cases I want to test?
• Start small. Begin with low-risk, simple tasks that don’t have a major impact on your business. This helps you evaluate credibility and accuracy without putting too much at stake.
• Validate with human research. Test the results against a traditional test group made up of real human participants to check for accuracy and reliability.

Final Thoughts

For all its follies, artificial intelligence is only going to get better and more sophisticated in the future; after all, just look at how far we’ve come since 2022. In perhaps a year or two, synthetic users will be free of most biases and will improve decision-making.

They can already speed up the pace of insight. With traditional market research, by the time results arrive, the market may have already moved on. Synthetic users dramatically reduce the time to insight, allowing teams to test new concepts, ideas, and incentives 24/7. They help move hypotheses into action without long wait times.

Synthetic respondents also go beyond analyzing past data and can tell you what customers are likely to do in the future. They can show how different customer groups think and behave at scale.

That said, synthetic focus groups cannot replace human participants. They do not operate with empathy or think with emotions, at least not yet. So, while they can prove to be useful, they should be used to complement, not replace, human inputs.