What is Synthetic Data?
Synthetic data generation creates entirely new survey responses from AI models trained on real consumer behavior patterns. Unlike traditional surveys that require recruiting and surveying real people, synthetic data lets you generate statistically representative datasets instantly.
Each synthetic respondent is a complete, consistent individual — with demographics, attitudes, and response patterns that reflect real population distributions. The result is a dataset that can be analyzed exactly like traditional survey data, using all the same tools and techniques.
Key Benefits
Synthetic data removes the biggest barriers in traditional survey research: time, cost, and recruitment logistics.
Instant Data Generation
Get complete datasets in minutes, not weeks. No waiting for panel recruitment, field time, or data cleaning.
Unlimited Sample Sizes
Generate any number of respondents you need — from 100 for quick reads to 10,000+ for deep subgroup analysis.
Custom Demographics
Precise control over age, gender, income, education, location, occupation, and more. Set exact quotas for any demographic combination.
Cost Efficiency
A fraction of the cost compared to traditional panel surveys. No recruitment fees, incentive payments, or panel management overhead.
No Recruitment Delays
Start your analysis immediately. No need to wait for hard-to-reach demographics or low-incidence populations to complete surveys.
Perfect For
Synthetic data is especially valuable when traditional survey methods are too slow, too expensive, or logistically impractical.
- New product concept testing — Get fast consumer reads on early-stage ideas before committing to full research budgets
- Early-stage market research — Explore market dynamics and consumer preferences during planning phases
- Quick hypothesis validation — Test assumptions about your target audience before designing larger studies
- Budget-constrained projects — Get professional-quality data when traditional panels exceed your budget
- Sensitive research topics — Study topics where respondent reluctance or social desirability bias affects live surveys
- Academic research projects — Generate large, controlled datasets for methodological studies and classroom instruction
Data Quality
Every synthetic dataset is built on validated AI models and passes through automated quality checks before delivery.
- Statistically representative samples — Generated distributions match validated population parameters
- Consistent response patterns — Each respondent answers coherently across all questions in the survey
- Realistic demographic distributions — Age, income, education, and other variables reflect real population structure
- Validated against real panel data — Models are benchmarked against live panel datasets in ongoing validation studies
- Standard research file formats — Export to CSV, SPSS, and Excel with full variable and value labels
- Full crosstab compatibility — Data works with all standard analysis tools and crosstab software
How It Works
Generating synthetic data follows a straightforward four-step process.
- Define Your Population: Set demographic targets and quotas. Specify the age, gender, income, education, and location distribution for your respondent sample. Use preset census-representative targets or create custom quota structures.
- Upload Your Survey: Use your existing questionnaire or create one directly in our platform. The survey builder supports single choice, multiple choice, grid/matrix, open-ended text, numeric, and formula questions with full skip logic and piping.
- Generate Responses: AI models create realistic individual respondent data. Each respondent is generated as a complete, consistent individual with demographics and survey responses that form a coherent profile.
- Download & Analyze: Get standard CSV, SPSS, and Excel files for immediate analysis. Run automated crosstabs, view charts and visualizations, or export to your preferred analysis tool.
Individual-level consistency: Each synthetic respondent has consistent demographics and realistic response patterns, allowing you to perform the same analysis as with traditional survey data — including subgroup comparisons, cross-tabulations, and statistical significance testing.
Technical Specifications
Key parameters and capabilities for synthetic data generation.
100 to 10,000+ Respondents
Scale from quick directional reads to large-scale studies with deep subgroup analysis.
6+ Targeting Variables
Age, gender, income, education, location, and occupation. Custom variables available for enterprise clients.
CSV, SPSS, Excel
Standard research file formats with full variable labels, value labels, and metadata.
All Standard Types
Single choice, multiple choice, grid/matrix, open-ended text, numeric, and formula questions.
15 – 30 Minutes
Typical generation time for a complete study. Large studies with 10,000+ respondents may take slightly longer.
Automated Validation
Every dataset passes consistency checks, distribution validation, and outlier detection before delivery.