Today, user research is a fundamental part of designing digital experiences. It helps us better understand users’ needs, behaviors, and expectations to create more intuitive and functional products. However, traditional research methods, such as interviews and usability testing, are often costly, time-consuming, and difficult to scale. This is where synthetic user research comes into play, an innovation that uses artificial intelligence and autonomous agents to create virtual user profiles and simulate their interactions.
In this article, we explore how synthetic users are changing the way we conduct CX/UX research, their current limitations, and how Making Science is addressing these challenges with its own advanced synthetic user model.
What Are Synthetic Users?
Synthetic users are profiles generated by AI that simulate the characteristics, needs, and behaviors of real users. These profiles can be created automatically, using data and AI algorithms that mimic the diversity and complexity of a real audience. Tools like Delve AI and Synthetic Users allow anyone to generate detailed personas with information about their background, frustrations, goals, and psychological characteristics. Through these platforms, design teams can quickly create profiles representing a diverse sample of users without the need to recruit real individuals.
Advantages of Research with Synthetic Users
Scalability and Efficiency The main advantage of synthetic users is their ability to simulate multiple profiles in a short period. By eliminating the need to manage and coordinate groups of real users, design teams can quickly obtain relevant data from a wide variety of people. This makes research much more scalable and efficient, especially when fast results are needed.
Cost Reduction Recruiting real users, conducting interviews, and performing usability testing can be costly and logistically challenging. With synthetic users, these costs are significantly reduced, as there is no need to compensate participants or manage the logistical aspects of research sessions.
Simulating Controlled Scenarios With synthetic users, teams can simulate very specific scenarios and observe how virtual users interact with a product or service. This flexibility allows for usability testing with different types of users in controlled contexts, making it easier to identify problems before launching a product to the market.
Limitations of Synthetic Users
While synthetic users offer great advantages, they are not a complete solution. There are some important limitations to consider:
Lack of Real Empathy Although synthetic profiles can be incredibly detailed, they cannot replace genuine interaction with real users. Interviews and usability testing provide an emotional connection that helps designers deeply understand the motivations, friction points, and emotions behind user behavior. Synthetic users, no matter how sophisticated, lack the human complexity necessary to simulate authentic emotional experiences.
Superficially Accurate Simulations Synthetic users can provide valuable feedback, but they often lack the complexity that real users can provide. The insights generated by AI may be overly optimistic or simplistic, making it necessary to complement them with real-world research to obtain a more comprehensive view.
Reliance on Previous Data Synthetic users rely on previous data to create profiles, meaning their accuracy depends on the quality of that data. If this information is limited or outdated, the resulting profiles and insights may not reflect actual user behavior.
How Making Science is Redefining Synthetic Users
Understanding these limitations, at Making Science we have developed our own advanced synthetic user model designed to overcome many of these challenges. Unlike conventional solutions that focus mainly on demographic data, our model integrates multiple layers of information, including:
- Market behavior
- Technological context
- Socioeconomic aspects
- Behavioral trends
- Customer journey insights
Additionally, we enrich our model with sectorial and brand insights—incorporating information about the sector, competitive landscape, and brand-specific elements such as product offerings, pricing strategies, and positioning. This approach allows us to simulate more realistic user experiences that account for industry-specific dynamics and brand perception.
But what truly sets our model apart is its ability to incorporate real data from various sources, such as user interviews, focus groups, and client reports. It can even be enriched with call center data, ensuring a deeper and more authentic understanding of user behavior.
Moreover, our model excels in early-stage validation. It can test redesign proposals at the sketch or wireframe stage, providing actionable insights to determine whether the new approach improves the user experience. This capability accelerates decision-making and enhances design outcomes, helping teams iterate with greater confidence.
Use Cases in CX/UX Research
While synthetic users should not fully replace real-user research, they have an important role in the lifecycle of CX/UX research. Some of the most relevant use cases include:
Exploratory Research: Synthetic users are ideal for the early stages of research, where the goal is to explore potential issues and generate hypotheses. By creating synthetic personas based on demographic and behavioral data, designers can gain an initial understanding of their audience before diving into more costly and complex studies.
Concept and Prototype Testing: Before conducting tests with real users, design teams can use synthetic users to evaluate design ideas, prototypes, and features. This allows for quick validation of concepts and adjustments before rolling out prototypes to real users.
Feature Validation: Synthetic users can be used to validate new features or changes in a product. Through simulations, designers can observe how different user segments interact with the product and whether the new features meet their needs.
AI and the Validation of Interface Affordance One of the most exciting advancements that AI already enables is the validation of the affordance of our interfaces. Simply put, affordance refers to an interface’s ability to indicate how an element should be used intuitively. AI can analyse and predict how users will interact with certain interface elements, helping us improve accessibility, clarity, and usability in our designs. Thanks to simulations with synthetic users, we can observe and adjust interactions before implementing changes in a real product, allowing us to optimise the user experience more efficiently.
The Future of Synthetic Users in CX/UX Research
The field of synthetic user research continues to evolve. At Making Science, our advanced synthetic user model not only integrates complex layers of demographic and behavioral data but also enriches profiles with real user feedback from interviews, focus groups, client reports, call center interactions, and sector-specific insights. This unique approach allows for more nuanced and realistic simulations.
Additionally, our model’s ability to test wireframes and sketches sets it apart, enabling early detection of design challenges and quicker iteration cycles.
However, it is essential to remember that research with real users should always be the foundation of any design strategy. Synthetic users are a powerful tool to complement research, but they should not replace a deep analysis of users’ needs, expectations, and emotions.
TL;DR
Synthetic user research is opening up new opportunities for design and product development teams. While it doesn’t replace real-user work, it provides a scalable and cost-effective alternative to obtain preliminary insights and validate ideas quickly. The key is knowing when and how to use this technology effectively, integrating it into a broader and balanced research approach.
At Making Science, we support our clients in implementing these innovative technologies to improve their user research processes. Our proprietary synthetic user model stands out by integrating both demographic insights and real user data, even from call centers, and enriching the analysis with sector-specific and brand insights. Additionally, we validate redesign concepts at the wireframe stage, optimsing user experiences from the earliest phases. The future of CX/UX research is promising, and we are ready to lead this change.
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