Observed Signal · Jul 31, 2026 · Analysis · Source: https://martech.org/feed/ · Impact: 2/5 · Sentiment: Positive
AI Simulates Buyers but Lacks Personality
The article argues that AI-driven synthetic audiences often miss a key input: buyer personality. Drawing on seven years of research into the personalities of 10,000 buyers across 15 industries, the author introduces the "Two-Thirds Rule": within a given role and industry two personality types account for at least two-thirds of incumbents. The piece explains that common data sources (demographic, firmographic, behavioral, and professional signals) capture what buyers are but not how they decide, because professional signals reflect a "work persona" rather than underlying decision-making patterns. The author recommends three changes to improve synthetic audience accuracy: layer personality into persona generation, model personas at the role-and-industry level, and validate synthetic audiences against personality, not only demographics or firmographics. The article was published on 2026-07-31.
Relevant methodological guidance for marketers and data scientists building synthetic audiences, but not a major platform policy or industry-shifting announcement.
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Key Takeaways & Evidence Grounding
- Author studied personalities of 10,000 buyers across 15 industries over seven years.
- Two-Thirds Rule: within a given role and industry, two personality types account for at least two-thirds of people holding that job.
- Common synthetic-persona inputs (demographic, firmographic, behavioral, and professional signals) often capture a professional/work persona but not underlying decision-making personality.
- Recommended changes: add personality to persona generation, model personas at role-and-industry level, and validate synthetic audiences against personality.
- Article published on 2026-07-31 and appears on MarTech, which is owned by Semrush.
Connected Companies & Entities
4 Entities mapped“Prior to founding Carbon Design, he was the President of the Washington, DC office for Merkle (a Dentsu agency), the world’s largest B2B age...”
“Prior to founding Carbon Design, he was the President of the Washington, DC office for Merkle (a Dentsu agency), the world’s largest B2B age...”
“MarTech is owned by Semrush....”
“Scott has been a member of the Gartner for Marketing Leaders Council...”
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.
B2B Personas Fail Without First‑Party Buyer Intelligence
Michael McGoldrick, global VP of marketing at pharosIQ, and CEO Jeffrey Rokuskie argue that static PowerPoint personas and inferred third‑party intent are insufficient for driving commercial outcomes in B2B marketing. As AI fragments buying journeys and obscures decision-makers, they recommend shifting to contact‑level, first‑party buyer intelligence to identify who inside buying groups is engaging with campaigns. They contend that creative work performs better when informed by observed buyer behaviour rather than broad assumptions or modelled audiences, and that first‑party observations and contact-level engagement provide the confidence needed to improve activation and pipeline generation.
AI Scaling the Wrong Part of Go-to-Market
The article argues that marketers and sales teams have focused AI on increasing volume—more emails, more prospecting—rather than on the preparatory work that enables human-led, relationship-driven selling. Citing industry research (Salesforce, 6sense, Gartner, ZoomInfo), the author says buyers often self-initiate and prefer human validation of AI insights; deals are commonly decided by familiarity and prior relationships. The recommended approach is to use AI to automate research, signal synthesis, data enrichment, prioritization and prediction (the “prep”), then allow humans to handle storytelling, judgment, and sending personalized outreach. The author positions 1:1 ABM and “signal-based experiences” as the highest-value GTM motion that benefits from machines doing the heavy prep while humans preserve trust and nuance.
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