Synthetic Respondents Modeled After Real People Are Changing How Market Research Gets Done
Breakthrough Research, a boutique market research and innovation agency, today launched Modeled Communities: synthetic respondent panels built from real consumers that give companies a faster, more flexible way to explore ideas and hypotheses.
Built by Breakthrough’s in-house Innovation team, each Modeled Community helps research and insights teams answer the questions that fall before, during, and after a traditional study — which concepts are worth advancing, which need more work, and which new hypotheses are worth chasing once results are in.
“Our approach has always been building custom research solutions for our clients’ toughest business questions,” said Dan Braker, President of Breakthrough Research. “Modeled Communities apply that same rigor to a faster format. This is a complement to strong, people-informed research — not a replacement for it.”
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Built From Real People
Each Modeled Community starts with real people who already engage with a client’s brand or category. Modeled respondents are built from extensive interviews on their background, motivations, and decision-making, creating a synthetic community that mirrors the attitudes and behaviors of a business’s most important audiences. In Breakthrough’s R&D, modeled respondents produced answers consistent with real human respondents.
Modeled Communities are cost-effective especially for lower-risk applications: screening concepts, pressure-testing messaging, sharpening a questionnaire, reaching harder-to-recruit audiences, etc. Though the company recommends that higher-stakes decisions still be validated with human respondents.
Key Advantages and Use Cases
Modeled Communities offer clients speed, cost, accessibility, and scale advantages over traditional fielding. Common uses include:
- Pre-testing ideas before a full study
- Vetting and iterating on hypotheses quickly
- Reaching harder-to-recruit audiences
- Testing concepts too confidential for live respondents
- Sharpening a questionnaire before fielding
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