Observed Signal · Sep 14, 2026 · Opinion / Analysis · Source: The Drum · Impact: 2/5 · Sentiment: Positive
Mark Ritson: Even Synthetic Ritsons Can't Tell Anthropic Everything
Mark Ritson analyzes the customer experience survey he received from Anthropic for its Claude Cowork product. Despite being a shareholder in synthetic research company Evidenza, Ritson argues that Anthropic's use of traditional human surveys validates the need for organic research alongside synthetic data. He compares the survey's quality to industry standards, noting its behavioral focus and use of metrics like Net Promoter Score and product-market fit. He emphasizes that synthetic data, while fast and scalable, cannot capture unique lived experiences, such as the frustration of a lost file. Ritson advocates for a combined approach, using synthetic research for scale and organic research for calibration, highlighting the complementary nature of both methods in market research.
Provides insights into the evolving role of synthetic data in market research, with implications for AI-based consumer insights, but is primarily an opinion piece with no breaking industry news.
Track Anthropic Signals & Market Shifts in Real-Time
Polaris7 autonomous intelligence agents track regulatory filings, primary sources, executive changes, and deal flow 24/7. Create your free Explorer workspace to monitor these entities.
Key Takeaways & Evidence Grounding
- Mark Ritson received and analyzed a customer survey from Anthropic for its Claude Cowork product.
- Ritson is a minority shareholder (2%) in Evidenza, a synthetic research company.
- Evidenza was founded by Peter Weinberg and Jon Lombardo, former LinkedIn B2B Institute executives.
- A Stanford study led by Joon Sung Park showed generative agents replicated human survey answers with 85% reliability in test-retest scenarios.
- Evidenza's enterprise clients include Nestlé, Microsoft, and JP Morgan.
Connected Companies & Entities
7 Entities mapped“Would I be kind enough to spare a few minutes to complete a survey about some software I was using? ... The company that built the thing......”
“...then running LinkedIn’s B2B Institute... they told me they were leaving LinkedIn to found Evidenza....”
“In 2024, they launched and promptly ran a blind replication of an EY brand study....”
“Evidenza lists Nestlé, Microsoft and JP Morgan among the enterprise brands working with its technology....”
“Evidenza lists Nestlé, Microsoft and JP Morgan among the enterprise brands working with its technology....”
“Mars has publicly described using synthetic audiences......”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Synthetic Research Depends on Audience Model Trust
Eric Ayzenberg (Soulmates.ai) argues that the real debate is not AI versus human research but whether the models behind synthetic audiences are trustworthy enough to inform decisions. Citing an evaluation by The Good, he says AI research tools can surface friction, accelerate workflows and provide directional input, but they often fail to reproduce observed behaviour, emotional nuance and the subtle signals needed for high‑stakes decisions. Ayzenberg proposes a three‑step approach: use synthetic audience models for quick directional clarity, use high‑fidelity synthetic models (validated against human data) when behavioural understanding is needed fast, and use traditional recruited field research when observed in‑the‑wild behaviour or regulatory/ investment rigor is required. The piece positions synthetic tools as complementary to human research, not replacements.
Synthetic Research: Promise With a Catch
This MarTech analysis examines the rapid rise of synthetic research—using generative models and synthetic personas to produce market and product insights—and the tradeoffs between speed/cost and scientific rigor. The synthetic data market is projected to grow dramatically, and many insight leaders plan to adopt synthetic approaches for scale and niche sampling. But off-the-shelf LLMs (e.g., ChatGPT, Claude, Gemini) can introduce bias, homogeneity and overly positive responses (the “Pollyanna Principle”), producing outputs that are difficult to validate. The article highlights techniques to improve reliability: fine-tuning models on proprietary survey data, applying a train-synthetic, test-real (TSTR) validation approach, and enforcing governance, transparency and persona provenance checklists. Case studies and experiments (including Stanford/Google DeepMind results and a Dollar Shave Club example) show promise when synthetic methods are benchmarked and verified against real-world data.
Agencies adopt hybrid synthetic audience research
Agencies are increasingly supplementing traditional consumer research with AI-generated synthetic audiences, using a hybrid approach that layers synthetic personas on top of human feedback, syndicated data and social listening. Crowley Webb’s senior VP of data analytics, Andrea Berki-Nnuji, says the agency has used synthetic audiences over the past 18 months but typically vets the output with real humans, estimating fully synthetic datasets reach about 80% of required validity. Vendors and platform selection focus on data provenance, pricing, multi-user capabilities and compliance review. Use cases include concept testing, naming, media messaging and pricing; however, certain contexts such as rare-disease pharma work still require strictly human research.
Track Real-Time Market Signals & Shifts
Set up custom watchlists to receive automated, evidence-grounded executive digests whenever material signals or shifts occur across your tracked landscape.