AI-generated audiences for exploratory research and concept feedback, including visual stimuli.
Product category
Audience simulation
Use cases
Concept testing / Exploratory interviews
Data grounding
Yabble describes an augmented model combining LLMs with trend, social and behavioral information.
Access & pricing
Platform. Confirm current plan with provider.
Research method
Question-led audience creation
Yabble says its augmented data model combines large language models with relevant trend data, social data and behavioral statistics to create an AI-generated audience for a research question.
Audience conversations and research outputs
The supplier documents surveying and interviewing a target audience, obtaining key insights, continuing conversations with personas and exporting a project as a report.
Synthetic versus traditional insight comparison
Yabble describes validation of similarity, quality and depth of insight, including distribution and topic comparisons between generated material and traditional research datasets.
Yabble: Virtual Audiences validation Yabble: ESOMAR AI services questionnaire response
Validation evidence
Provider-reported validation summary
Yabble reports average similarity of 90% between its synthetic and traditional research results in proof-of-concept and validation projects. Its validation document describes similarity checks using distance to closest record, cosine similarity and topic distribution, alongside ARES-based assessments of insight quality and depth.
These figures and methods are reported by Yabble. The public summary does not provide the full set of studies, study populations, question designs or calculation details needed to apply the 90% figure as a general performance result.
Yabble: Virtual Audiences validation Yabble: ESOMAR AI services questionnaire response
Documented scope
Use cases are bounded by the supplier's own guidance
Yabble's published guide recommends particular early-stage uses, including exploration, trend analysis, simple segmentation and simple concept testing. That guidance does not establish suitability for every form of population estimate or high-stakes decision.
Data inputs and recency affect the output
Yabble says the relative use of proprietary material varies by the data provided and the question. It also describes an inherent recency bias, so older customer data may carry less weight than newer information available to the model.
Method and evidence sources
- Yabble: Virtual Audiences · Provider product and methodology overview
- Yabble: Virtual Audiences validation · Provider-reported validation
- Yabble: ESOMAR AI services questionnaire response · Provider methodology disclosure
