Synthetic Personas

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With Synthetic Personas, teams can interact with simulations of their audiences to test hypotheses, iterate on ideas, or answer pressing business questions. Trained on a rich foundation of market research, Synthetic Personas predict how real-world audiences respond to messages, concepts, and other business decisions.

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Join the organizations who have already found success

Predictions that reflect your specific audiences, not a generic approximation

Synthetic Personas are grounded in real-world buyer research: an organization’s own past research plus NewtonX’s B2B professional survey data. Together, they produce simulations that capture both the specific nuances of an organization’s audiences and the broader landscape those audiences operate in.

Context from your organization

Past surveys, transcripts, and segmentation studies are used to train proprietary personas specific to an organization. The model learns from what those audiences have actually expressed, giving it the institutional context that generic Synthetic Personas do not have.

Context from NewtonX

Each Synthetic Persona is further informed by NewtonX’s identity-verified B2B professional survey data: behavioral, attitudinal, and professional signals spanning hundreds of industries, functions, and geographies. This data improves the context for the model by providing a view of what is happening in the broader market landscape beyond any single organization.

Leveraging Synthetic Personas

Available whenever a full study is not

Synthetic Personas can be queried at any time, without waiting for recruiting, scheduling, and fieldwork. Teams can pose qualitative questions and receive interview-style responses, or ask quantitative questions and receive survey-style outputs—available on demand for quick answers.

Ask follow-up questions from a recently closed study without going back to field
Iterate on messaging until a version is ready to go to market
Narrow a long list of concepts to a shortlist worth testing in a real-world study
Stress-test survey structure and flow before going live

Synthetic Personas are well-suited for everyday decisions that must be made without a full research study, whether because of time constraints, bandwidth limitations, or lower decision stakes. For business-critical decisions where findings need to be statistically defensible, primary research remains the right choice.

Why Synthetic Personas produce better predictions

Predictions grounded in your specific audiences, not generic descriptions

Other synthetic persona tools query a shared pool of AI-generated respondents built on broad, publicly available data and a basic description of a target audience—a configuration that any of their clients can replicate.

Synthetic Personas are different. They are trained on research that is exclusive to an organization: past surveys, interview transcripts, and segmentation studies that capture what those specific audiences have actually expressed. The result is a simulation of an organization’s real buyers, not a generic population.

Benefits of Synthetic Personas

Proprietary to your organization

Because Synthetic Personas are built on an organization’s own research data, the insights they surface are available only to that organization. The personas are locked to the client—not accessible to other organizations, and not replicable by a competitor targeting the same audience.

Backed by identity-verified B2B professional data

NewtonX data is sourced from professionals whose backgrounds have been verified—meaning the behavioral, attitudinal, and professional signals informing each persona come from people with genuine expertise and firsthand experience in the topics they are asked about. Because the training data reflects how real B2B professionals actually think and behave, Synthetic Personas have accurate, trustworthy context to draw on when generating predictions, rather than pulling from publicly available online data with inconsistent quality.

Value that compounds over time

New studies an organization runs can be used to retrain its Synthetic Personas, keeping the model current and making it progressively more accurate. Primary research and Synthetic Personas reinforce each other. Each new study makes the personas sharper, and sharper personas make future research more focused.