Response from Anthropic’s Claude:
Looking at the trajectory of advertising technology, several synthetic audience types will be particularly valuable by 2030:
Privacy-First Synthetic Personas
- Privacy-compliant audience expansion becomes possible without third-party data, addressing the growing regulatory landscape. These audiences will simulate consumer behaviors while maintaining complete anonymization, becoming essential as third-party cookies disappear and privacy regulations tighten globally.
Behavioral Prediction Models
- Synthetic users are simulated versions of actual users of digital goods. They learn from data, interact with interfaces, respond to changes in design, and predict how different audiences will behave under certain conditions. These will evolve beyond simple demographic modeling to predict complex consumer journeys and decision-making patterns.
Niche Micro-Segment Audiences
- You can model how different segments, like single parents, bilingual teens, or early-career professionals, might respond to your creative work. As personalization becomes more sophisticated, synthetic audiences representing highly specific lifestyle intersections will become crucial for targeting emerging consumer tribes.
Real-Time Creative Testing Audiences
- Creative testing can be revolutionized through AI-generated synthetic respondents that produce concept-preference scores within five percentage points of real-panel results while cutting research time and cost by roughly 80%. These rapid-feedback synthetic panels will enable immediate creative optimization and A/B testing at scale.
Cross-Cultural Synthetic Cohorts
- With global advertising becoming more nuanced, synthetic audiences representing cultural intersections and diaspora communities will help brands navigate complex multicultural markets without relying on potentially biased real-world data collection.
The key shift will be from demographic-based synthetic audiences to behavior and intent-based models that can predict not just who might buy, but when, why, and through which channels they’re most likely to convert.
Sources include: M1-Project, Single Grain and Nielsen IQ