An AI startup has raised $300 million to develop technology that simulates how people shop and make buying decisions. These tools are known as synthetic consumers or AI digital twins. They help businesses test products, advertisements and pricing strategies using virtual customers.
Simile, an AI startup valued at a reported $2 billion, is developing systems that predict consumer behavior. According to an October 27 Business Insider report, the company works with businesses in retail, finance and entertainment.
Pharmacy chain CVS has used its technology to study customer responses to store layouts and marketing messages. However, questions remain about the accuracy of AI-generated consumers.
A September study by the Pew Research Center examined nearly 300 questions. It found that AI-generated survey responses differed from real human responses by an average of 12 percentage points. The study raise concerns about whether businesses can rely on AI simulations to predict real consumer behavior.
A $300 Million Startup Wants to Build Digital Versions of Consumers
Simile is building an AI model that aims to predict human behavior. The company wants businesses to use virtual consumers to understand how people might react to different products and prices. They can also use these virtual consumers to test marketing campaigns.
Simile co-founder and CEO Joon Sung Park explained the idea during an appearance on the Sourcery podcast. The system uses information about an individual or group to predict how they might behave in different situations.
Businesses can then interact with these virtual consumers through a web interface and ask questions about their preferences. For example, a retailer could create a virtual group of fitness enthusiasts to find out whether they would buy a new smartwatch priced at $199 or $249.
A supermarket could test whether moving products to another aisle might increase sales. Similarly, a film studio could use the simulations to understand how different audiences might respond to a new movie or advertising campaign.
Park said businesses can choose the type of people they want to study and ask the simulated group as many questions as they need. The goal is to make market research faster and easier. Traditional research often requires companies to recruit participants, conduct interviews, run surveys and analyze the results. These steps can take time and money.
Simile wants businesses to use AI simulations to explore ideas before deciding whether they need to conduct further research with real consumers. According to Park, Simile has raised $300 million and reached a valuation of around $2 billion. However, this funding does not prove that its AI simulations can accurately predict real consumer behavior.
CVS Is Using AI Consumers to Test Shopping Experiences
CVS Health is among the companies reported to be working with Simile. The pharmacy and healthcare company is using AI simulations to study how customers might respond to changes in store layouts, messages and shopping experiences.
Retailers regularly make decisions about product placement, promotions and store design. Traditionally, they rely on surveys, interviews and customer tests to understand what shoppers prefer.
AI simulations offer another way to test these ideas. Retailers can compare different store layouts and see how virtual shoppers might respond before making changes. Simile also works with financial institutions and entertainment companies, according to CEO Joon Sung Park.
Banks could test customer reactions to financial products, while entertainment companies could study audience preferences before launching campaigns. However, virtual shoppers cannot guarantee real sales.
Actual buying decisions depend on factors such as personal budgets, changing preferences and in-store experiences. For now, AI consumer simulations can help businesses test ideas, but they cannot replace real customer feedback or reliably predict every purchase.
How Do Companies Create AI Clones of Real Consumers?
AI consumer models use data about people to create virtual profiles that can answer questions and simulate shopping decisions. The data may include survey answers, age groups, buying habits and other information about consumer behavior.
Simile says it works with organizations such as Gallup to collect data from people who have agreed to take part in research. CEO Joon Sung Park said these partnerships give the company access to information linked to more than 40 million people worldwide.
The company also uses transaction and mobility data to improve its predictions. For example, a grocery retailer could use these models to study how people in a particular age group might respond to a new product. The virtual consumers could then provide simulated responses about pricing, product preferences and advertising.
However, AI consumer models do not always create exact digital copies of real people. Some simulate the behavior of broad groups, while others use data linked to specific individuals.
The accuracy of these predictions depends on the quality of the data, how the models are built and whether their results match real consumer behavior. A simulated response does not guarantee that an actual customer will make the same choice.
Companies Are Turning AI Consumer Clones Into a Marketing Business
Simile is not the only company developing AI tools to study consumer behavior. On October 20, Resonate announced Resonate Digital Twins, a product that uses data linked to real people to simulate consumer responses.
The company says its system covers more than 250 million American adults matched to individual records. Its data resources include over 100 million consumer research responses, more than 30 billion observed behaviors daily and over 15,000 attributes.
Resonate says marketers can ask questions about consumer preferences and turn the results into advertising audiences within five days. The company aims to connect AI-based research directly to advertising.
Businesses could use the system to identify people who may be interested in a product and target those groups with ads. This approach could help companies move from testing marketing ideas to launching campaigns more quickly.
However, the size of Resonate’s database does not mean that every person has agreed to have an AI clone created. Its claims about the accuracy and performance of the system also need independent testing.
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Companies Developing Synthetic Consumer Technology
Several companies are developing synthetic consumer technology for product research, audience analysis and marketing. The table below highlights key companies working on AI-based consumer simulations and their main uses.
| Company | Technology | Usage |
| Simile | AI simulations of human behavior | Product research, store layouts and marketing |
| Resonate | Consumer digital twins | Audience research and advertising targeting |
| Outset | AI-assisted customer research | Customer interviews and digital consumer representations |
| Arima | Synthetic populations | Media planning and campaign testing |
| Vurvey Labs | Synthetic consumer personas | Product development and marketing research |
Advertising agencies and consumer brands are also exploring AI-generated consumers. According to Digiday’s October 12 report, Goodway Group is working with Arima to use synthetic population data for media planning.
The agency wants to test different advertising budgets and strategies before spending money on actual campaigns. Brands such as Magnum, Vans and Kenvue have also tested synthetic personas for product development and market research.
AI Consumer Predictions Can Be Wrong, Pew Research Finds
Independent research has raised concerns about how accurately AI can predict human responses. On September 30, Pew Research Center published a study testing whether AI-generated respondents could give answers similar to those of real people.
Researchers used AI models to simulate participants from Pew’s American Trends Panel. They gave the models information about real participants and asked them to answer questions from surveys that people had already completed. This showed clear differences between AI-generated and human responses.
Across nearly 300 survey questions, AI answers differed from real survey results by an average of about 12 percentage points. The gaps were larger for some demographic and political groups.
| Population | Average absolute error |
| All U.S. adults | 12.4 percentage points |
| Republican and Republican-leaning adults | 16.1 percentage points |
| Democratic and Democratic-leaning adults | 13.6 percentage points |
| Black adults | 15.1 percentage points |
| Hispanic adults | 13.9 percenatge points |
| White adults | 12.5 percentage points |
Source: Pew Research Center
Pew found that nearly half of the survey questions had at least one answer option that no AI respondent selected. This suggests that AI models may fail to capture the range of opinions held by real people.
Researchers concluded that the tested methods could not replace traditional public opinion polls. The study did not test Simile or Resonate, so its findings do not prove that all AI consumer models are inaccurate. However, businesses should verify AI predictions with real customer feedback to avoid poor product decisions and ineffective advertising campaigns.
Some Studies Report Better Results From Synthetic Consumers
Despite Pew Research’s study, other research suggests that AI consumer models can perform well on specific tasks. In a May 2026 analysis, Boston Consulting Group (BCG) explored how businesses could use AI-generated consumer groups to test products and pricing.
One experiment predicted actual consumer choices with 92% accuracy after the model was refined. BCG said synthetic research could help companies test more product ideas and speed up development. However, it also found that these models were less effective at predicting reactions to completely new products.
AI models learn from existing data, so they may predict preferences for familiar products more accurately than reactions to something unfamiliar. For example, a model might compare preferences for two popular beverage flavors but struggle to predict demand for a new type of drink.
BCG also warned that synthetic consumer panels should not fully replace traditional market research. Pew and BCG studied different tasks, which helps explain their different findings. Pew tested AI responses to public opinion surveys, while BCG examined specific consumer choices.
Could Companies Predict Your Purchases Before You Make Them?
Synthetic consumer technology aims to help businesses predict what people might buy in the future. Retailers already use shopping history, browsing activity and customer preferences to recommend products. AI simulations take this a step further by testing how consumers might respond to new situations.
For example, a clothing brand could compare prices for a new collection. A supermarket could test a new store layout, while an advertising agency could compare campaign ideas before launching ads.
This approach could help businesses test thousands of ideas without conducting a separate survey for each one. However, predicting a group’s preferences is different from knowing what an individual will buy. Personal budgets, changing needs and social influences can all affect purchasing decisions.
Even models trained on large amounts of data cannot account for every factor. There is no established evidence that these systems can consistently predict an individual’s future purchases with certainty.
AI Consumer Clones Raise New Privacy Questions
AI consumer models raise concerns about how companies collect and use personal data. These systems may use shopping habits, location patterns, age groups and personal preferences. Some rely on research from people who agree to participate, while others combine surveys with commercial data and observed behavior.
The risks depend on how the information is collected, stored and used. Simulating a broad group is different from building a detailed profile linked to a specific person. Resonate says its system connects predictions to audience records that marketers can target with ads.
This raises questions about whether consumers know how their data is used to make AI-based predictions. Companies could also use this information to estimate personal details that people have never shared directly.
Even if these estimates are wrong, they could affect the ads, offers and recommendations people receive. Privacy concerns are also growing around AI shopping assistants.
In September, Reuters reported that major banks had warned about fraud, scams and data privacy risks linked to AI agents that shop for customers. Although the technologies work differently, both could change how AI influences buying decisions and the relationship between businesses and consumers.
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Will AI Replace Traditional Market Research?
AI consumer models can help businesses test ideas faster and at a lower cost. Companies can ask virtual customers questions and compare different products, prices and marketing strategies without running a new survey each time.
However, relying entirely on AI comes with risks. Real consumers can change their minds, express unexpected opinions and respond to new trends that AI models may miss. These systems can also reflect biases in their training data.
Pew Research found that AI-generated responses did not always match real public opinion. BCG’s research, meanwhile, suggests that synthetic panels can help with certain consumer decisions when their results are tested.
A better approach may be to combine AI simulations with traditional market research. Businesses can use virtual consumers to test initial ideas and then ask real customers for feedback before making major decisions.
This shift could also change the role of market researchers. They may spend more time checking AI predictions, finding errors and comparing results with real consumer behavior. Whether this technology will create new jobs or reduce demand for some research roles remains unclear.
The Future of Shopping May Begin With Your Digital Twin
Synthetic consumers are changing how businesses use AI. Instead of using it only to create ads, answer questions or recommend products, companies are now using AI to predict how people might respond to different products and offers.
Simile’s $300 million funding and reported $2 billion valuation show investor interest in this technology. Resonate’s digital twins also show how AI-generated consumer profiles could help companies plan advertising campaigns.
Retailers, healthcare companies and marketing agencies are already exploring these tools. However, their long-term success will depend on how accurately they predict real consumer behavior.
Companies must also address concerns about consent, privacy and the use of personal data. These issues become more important when AI predictions influence major business decisions. For consumers, the change may happen without them noticing.
An advertisement, product recommendation or store layout could be tested on an AI model representing people with similar interests and habits. AI may help businesses predict what customers are likely to buy. But understanding why people make those choices remains a bigger challenge.
