Marketers and product teams are constantly fighting the problem of the predictability of user behavior. Classic testing methods, such as focus groups or A/B tests, have small data sets and do not always forecast well.
That is why marketers and product managers have been constantly looking for ways to better model user behavior. At some point, it became obvious that traditional methods do not allow you to cover all possible interaction scenarios. Too many variables affect the final decision of the client - context, emotions, habits, even microscopic changes in the interface. Companies needed a method that would allow testing products in real conditions without the need to involve tens of thousands of users for each experiment.
This is how new approaches based on artificial intelligence came into being. Instead of relying on small samples of real users, companies have started applying AI models that are capable of predicting customer reaction, mimicking interfaces, and identifying vulnerabilities in the user experience prior to their occurrence. This has opened up fundamentally new possibilities: marketers have received a tool that does not just analyze historical data, but can generate new insights, predicting customer behavior even before they start interacting with the product.
Synthetic users have become a key tool for growth, especially in B2B SaaS, AI products, and digital marketing. Google, Amazon, and Meta, among other companies, already utilize them to test adverting, interfaces, and user scenarios. For example, Google released AI models for automated testing of alterations to search results in 2023 to predict users' interactions with new parts of the interface.
In this article, we will make you understand what synthetic users are, who uses them, how they help businesses, and why they are becoming a significant component of SaaS and AI products.
What are synthetic users
Synthetic users are simulated versions of actual users of digital goods. They learn from data, interact with interfaces, respond to changes in design, and predict how different audiences will behave under certain conditions. Unlike bots that operate by executing rigidly defined scripts, synthetic users learn from real user data, are sensitive to change, and can assist in testing scenarios difficult to replicate in the real world.
The major advantage of synthetic users is scalability. Companies can create a million synthetic users in hours and mimic their behavior with high fidelity that, in turn, allows them to get analytics without engaging with real customers, saving resources and accelerating hypothesis testing.
Main Characteristics of Synthetic Users
They copy the behavior of real users
AI models mimic user behavior and repeat it in a simulated environment like clicking, scrolling, filling out forms, leaving reviews, and even simulating sophisticated behavior scenarios, such as re-engaging with a product after a few days.
They are not dependent on the human factor
Real users are distracted, lose focus, or react to an environmental context that is difficult to manage. Synthetic users act strictly on provided conditions and thus test outputs are more reproducible and predictable.
Can process large volumes of data
Compared to focus groups, where one can see a few people's behavior, synthetic users can test scenarios on millions of virtual users simultaneously. This is especially important for complicated products with necessity of multi-dimensional testing where statistical significance is critical.
Universality
Synthetic users are used in UX testing, marketing, cybersecurity, advertising, and even product development. They also can be utilized to test the effectiveness of advertising campaigns, predict landing page conversion, or test interfaces before release.
How synthetic users are created
In order to produce a synthetic user, companies use machine learning models trained on massive amount of data and normally follow such specific steps:
1. Data collection
Artificial intelligence models browse through real users' behavior, noticing the way they utilize interfaces, actions they take, and where errors arise.
2. Model training
Based on the collected data, algorithms are created that reproduce user behavior in various scenarios.
3. Running simulations
Synthetic users begin testing the product, simulating the interaction of real users. This allows companies to predict how the audience will react to changes before they are implemented.
4.Analysis of results
Data on the behavior of synthetic users is used to optimize products, improve UX, and increase the effectiveness of marketing strategies.
Many companies create synthetic users using platforms integrated with Google Analytics, Amplitude, or Mixpanel. This allows you to model user behavior based on specific data, such as traffic sources, action history, and audience preferences.
Who uses synthetic users
Synthetic users have already become part of the strategies of the largest tech companies, but their potential goes far beyond IT giants. They are actively used by marketers, product managers, UX researchers and cybersecurity specialists. Companies from a wide range of industries - from SaaS and e-commerce to FinTech and EdTech - use synthetic users to test hypotheses, predict customer behavior and optimize user experience.
Marketers and digital agencies
Digital marketing is increasingly dependent on data. Synthetic users allow you to test ads, predict audience behavior and even automate A/B testing without real users. For example, an AI model can simulate how different audience segments will interact with an ad on Facebook Ads or Google Ads even before the campaign is launched. This reduces the testing budget and increases the effectiveness of marketing decisions.
Product teams in SaaS
Creating a SaaS product that will not only work, but will truly capture the attention of users and retain them, is a task that requires constant experiments, tests, endless iterations of the interface, honing the user flow, optimizing every click, every form, every button, because even minimal changes can affect conversion and the customer life cycle, which means that how quickly and accurately the product team identifies problem areas determines how effectively the product will scale, attract new users and retain those who are already inside the ecosystem, and this is where synthetic users turn from just an interesting AI solution into a real growth tool that allows you to instantly test new features, analyze how exactly changes in the UI will affect retention, and even predict which updates will be successful even before their actual launch, which is already actively used by companies such as HubSpot, where AI models test CRM functionality at the development stage in order to identify bottlenecks in advance and release updates that users will be accepted without unnecessary resistance.
E-commerce and marketplaces
When it comes to online retail, any, even the most insignificant experiment with UX can lead to either an explosive increase in sales or a sharp drop in conversion, because users get used to a certain way of interaction, and any change - be it a new recommendation algorithm, a different order of filtering products, an updated design of the product card or just a change in the color of the "Buy" button - can act as a trigger, forcing users to either immediately react or leave the platform, and this is why companies like Walmart no longer rely only on classic A / B tests, but use synthetic users to predict in advance how buyers will behave when prices, delivery terms, payment methods, catalog structure change, which allows them to test dozens of scenarios in a matter of hours, rather than spending weeks on real collection of user data, risking losing profits.
Financial technologies and banks
Financial companies operate in an environment where every detail of the interface, every touch of the screen, every micro-action of the user can be critical, because they determine how convenient it is for customers to make transactions, how transparently fees are displayed, how quickly the necessary functions can be found, and also how reliably user data is protected and how effectively the system detects suspicious activity, and this is where synthetic users become not just a tool for UX research, but a real way to predict potential threats, identify fraudulent schemes and even analyze how attackers can try to bypass security mechanisms, and this approach is already used, for example, in Revolut, where AI models test payment systems, simulating hacker attacks and modeling scenarios in which fraudulent transactions can remain unnoticed, which means that synthetic users help find vulnerabilities at the testing stage before they become a real threat.
Gaming industry and streaming services
Game development companies, video streaming platforms and digital content services have long understood that the success of their product depends not only on the quality of the content, but also on how accurately they can predict user behavior, because when it comes to monetization, audience retention and engagement, intuitive solutions no longer work, and this is where synthetic users become a real behavior laboratory, where AI algorithms can simulate hundreds of thousands of interactions, test the difficulty of levels in games, analyze how different groups of users react to in-game purchases, model situations in which players can leave due to too much difficulty or, conversely, lose interest due to too easy gameplay. And even in streaming services like Netflix, where AI has long been analyzing viewer preferences, synthetic users help test interfaces, predict which films and series will generate the greatest interest, and even model the audience's reaction to changes in recommendation algorithms, which allows the platform to understand in advance which changes will bring growth and which can reduce user retention.
How Synthetic Users Help Marketers Improve Conversions
Marketing is a game on the edge of predictability. You know that your target audience exists, you know that it has a need, you know that your product solves this need. But you don’t know the main thing — why some users buy, while others leave, why some clients reach checkout, while others hang on the second screen, why advertising creative that seemed deadly accurate wastes the budget without results.
Synthetic users give marketers a tool that was previously available only to IT teams and analysts with huge research budgets. Now you can not just look at retrospective reports, but model the future. Do not wait until the A/B test fails, but immediately understand which landing page will bring more leads. Do not burn tens of thousands of dollars on advertising that may work, but launch only those ads that have already been tested on thousands of virtual users.
This is how Meta advertising algorithms work. Before your creative goes into the real world, AI is already testing it on synthetic users, assessing which headline, which image, which format will get the greatest response. Do you think that you are simply uploading an ad to Facebook Ads? No. You are entering a game with a system that already knows which option will be more effective. And if you use synthetic users yourself, you are playing ahead of the curve, not just following algorithms.
Email marketing? Same thing. An email subject line can raise or kill an open rate. A CTA button can double the click-through rate or leave an email unattended. Every word in an email chain can be critical. Instead of running A/B tests for weeks, you can take synthetic users, run a thousand virtual scenarios, see where people lose interest, and immediately launch an optimized version.
But the most powerful case is landing pages. Here, any little thing can affect conversion: the order of blocks, the length of the form, the location of the button, even the background color in the mobile version. Synthetic users analyze how thousands of virtual users interact with the site, where they get stuck, which elements make them leave, and which retain them. In e-commerce, this solves the problem of millions.
Using Synthetic Users in SaaS Products
In SaaS products, where every interface change, new feature or even a small edit in the onboarding process can dramatically affect user retention, companies have long understood that testing on real users is a luxury that cannot be afforded. Any error in UX, unnoticeable bug logic or just an inconveniently placed button can cost thousands of refusals, a drop in active users and a loss of LTV. But what if testing could be done on thousands of virtual users who behave like real ones, but do not require a live audience?
That is exactly why synthetic users are increasingly penetrating the development and scaling processes of SaaS products. Take Slack, for example. Before rolling out a new version of the interface, they test how synthetic users will interact with the updates: where frustration will begin, where the path to the target action will slow down, which buttons will be insufficiently noticeable. This doesn’t just save time — it allows you to avoid problems before they turn into real user complaints.
In B2B SaaS, synthetic users solve several key tasks at once: they allow you to speed up product development, test complex interaction scenarios (for example, the operation of team tools in a corporate environment), and analyze how customers with different levels of digital maturity master the product.
Synthetic users are not perfect
Despite all the power of synthetic users, they are far from perfect. Yes, they model behavior, analyze UX, test advertising hypotheses, and even help train AI. But should you rely on them as your only source of data? Not quite.
Synthetic users are initially a simulation, i.e., they are derived from algorithms that have been trained on historical data. This creates a bias problem: if a model is trained on data that already contains errors or distortions, it will only copy them at a new scale. For example, if synthetic users review ad creatives, but are trained on historical click-through behavior, they can produce erroneous predictions.. For example, if synthetic users analyze advertising creatives, but are trained on outdated click-through patterns, they may produce inaccurate forecasts.
Second, they do not have real emotions, intuition, and subjective perceptions that so strongly influence the behavior of real users. A person may make an impulsive purchase because he is in a good mood. They may leave the landing page not because of bad UX, but simply because they were distracted by a call. They may close the registration form because they changed their mind, and not because it is too long. Synthetic personas are still unable to cope with the level of randomness and unpredictability in the real world.
A second problem is one of complexity when it comes to implementing and making sense of data. Businesses need trained analysts for using synthetic users, as these analysts will need to work with AI models, filter out spurious signals, and adequately tweak algorithms for an actual audience. Without proper setup, there is a risk that synthetic users will start producing “perfect” scenarios that will never work in real life.
Nevertheless, despite these limitations, synthetic users continue to evolve. Modern AI models are becoming more complex, are starting to take into account micro-factors of behavior, and even adapt to market changes in real time. Will they completely replace real users? No. But as a tool that complements classical testing methods, saves time, and helps make more accurate decisions, synthetic users have already become indispensable.
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