How are AI models used to create synthetic users for research?
From data analysis to generative models (LLMs): the process behind how Artificial Intelligence builds customer profiles to accelerate research
You need research insights, but you don’t have weeks to run qualitative interviews. How do you validate a product or campaign idea right now? The answer lies in using AI models to create synthetic users.
These virtual profiles, generated from real data, are not just static descriptions; they function as customer simulators. They use generative AI and LLMs to analyze patterns and even respond to interview-style questions. This article explains the “how”: the technical and practical process behind this research revolution.
What are synthetic users and why use them?
Definition of synthetic users
Synthetic users are fictitious user profiles generated by artificial intelligence that simulate the behaviors, preferences, and characteristics of real user groups. They are based on the analysis of large volumes of real data to reflect demographic and behavioral patterns.
Advantages of using them
- Fast and agile profile generation
- Control and customization of characteristics
- Reduced costs and time compared to in-person research
- Expanded testing scope without the need for real samples
Important limitations
Although useful for early hypotheses, synthetic users do not replace qualitative research with real users, as they may lack emotional depth and real-world context.
How do AI models generate synthetic users?
Data analysis and pattern recognition
AI models are trained on large datasets that include demographic data, online behavior, purchase history, and survey responses to identify correlations and major trends.
Creation with generative models
After training, generative AI tools—such as large language models (LLMs)—receive specific instructions to create detailed profiles, including age, income, habits, and values in a coherent and diverse way.
Simulation of interviews and interactions
Advanced platforms allow interaction with synthetic users through chat, simulating interviews that reflect typical opinions and needs, providing quick insights for exploratory analysis.
Predictive behavior modeling
In addition, AI can simulate how these personas would react to changes in products, campaigns, and pricing, helping teams predict impacts before running real tests.
Practical applications of synthetic users in research
Acceleration of exploratory research
Synthetic users make it possible to gather initial insights about user groups, helping define guidelines for subsequent qualitative research.
Concept testing and validation
They can be used to evaluate early ideas, features, and strategies, anticipating potential issues and refining approaches before larger investments are made.
Support for product development
They act as virtual team members, providing quick feedback on hypotheses and designs throughout the development cycle, speeding up decision-making.
Data-driven marketing
The generated personas help improve audience segmentation, adjusting campaigns and content to maximize engagement and conversions.
Step-by-step guide to creating synthetic users with AI
- Define objectives and target audience
Clearly establish the scope of the research and the desired user profiles to guide the modeling process. - Collect and prepare data
Gather relevant data from multiple sources, ensuring quality and representativeness for training. - Use appropriate AI models
Employ large language models or specialized platforms to generate personas based on defined parameters. - Conduct simulated interviews
Interact with the personas to explore needs and behaviors dynamically. - Validate and refine
Compare the results with real research to improve the profiles and prevent biases.
Myths and truths about synthetic users
Myth: Synthetic users replace real users
False. They complement research but do not replace the depth and empathy gained from real user interactions.
Truth: They are useful in early stages and for quick tests
Correct. They help generate hypotheses and support decision-making when speed is essential.
Myth: Feedback from synthetic users is always accurate
False. Their feedback tends to be positive and superficial, requiring validation.
Truth: They reduce costs in exploratory research
Yes. Creating and iterating with synthetic users requires fewer resources than traditional research.
Common mistakes when using synthetic users
- Relying solely on the generated data without human validation.
- Using synthetic users for final decisions without real-world testing.
- Ignoring qualitative and emotional context when interpreting insights.
- Overlooking the need to constantly update models as users evolve.
Comparative table: Synthetic users vs. Traditional personas
| Aspect | Synthetic users | Traditional Personas | |
| Data source | Large datasets and AI | Direct research with real users | |
| Creation speed | Fast, automated | Slow, manual | |
| Emotional depth | Limited, simulated | High, based on real experiences | |
| Cost | Low to medium | High | |
| Ideal application | Exploration and preliminary testing | Final decisions and detailed validation |
What are generative AI models used in persona creation?
They are algorithms trained to produce coherent text and profiles based on large volumes of data, enabling the generation of detailed and varied hypothetical user profiles.
Which data is most commonly used to train these AI models?
Demographics, online behavior, purchase history, survey responses, and in some cases psychographic data that reflects users’ values and preferences.
How can the quality of synthetic users be ensured?
By complementing the generated data with qualitative validation from real users and periodically updating the models to reflect behavioral changes.
What are the risks of relying solely on synthetic users?
They may lead to decisions based on inaccurate or overly positive feedback and fail to capture emotional and contextual nuances that are crucial for product or campaign success.
Can synthetic users replace field research?
No. They are complementary tools that help accelerate preliminary stages but do not replace the richness of insights gained from real interactions.
How do synthetic users impact product design?
They provide an agile way to evaluate hypotheses, test flows, and validate assumptions before investing in prototypes or product launches.
In which industries is the use of synthetic users most common?
Digital marketing, software development, UX design, online retail, and other sectors that require fast adaptation to consumer behavior.
How does MJV use synthetic users in its projects?
We use this technology to expand initial audience understanding, accelerate hypothesis generation, and support decisions in agile cycles—always combining it with real research for high accuracy.
Which AI tools are used to create synthetic users?
In addition to specialized platforms like Synthetic Users, we use large language models such as GPT-4 and other models designed for analysis and profile generation.
Are synthetic users updated automatically?
They can be, depending on the platform. Continuous updating is important to keep them relevant and accurate as user behavior evolves.
Is it possible to simulate different profiles within the same project?
Yes. AI can create multiple detailed personas representing diverse segments, enriching audience understanding.
Which metrics indicate the effectiveness of synthetic users?
Team satisfaction, development speed, alignment of solutions with the target audience, and correlation with real data from subsequent research.
How to deal with bias in AI-generated personas?
It’s essential to review source data, ensure diversity in data inputs, and continually validate with real feedback to minimize distortions.
Do synthetic users help with marketing personalization?
Yes, because they allow segmentation of content and campaigns based on detailed and updated profiles, improving relevance and engagement.
What is the future of synthetic users in research?
As AI continues to advance, personas will become increasingly realistic, dynamic, and integrated into automated workflows, enhancing their value in decision-making.
Understanding the “how” is only the first step
Knowing which AI models are used to create synthetic users is one thing. Having the right platform to apply that knowledge is another. You don’t need to build your own LLMs from scratch to accelerate your research.
MJV AIRA is our Artificial Intelligence platform built for exactly that. It already includes the generative models and data structure required to create, simulate, and interact with synthetic users quickly—and with validation backed by our expertise. Get to know MJV AiRA here.