Sapien
I lead growth and positioning for a market research platform using simulated audiences.
Research content, buyer guides, and product positioning.
I built Sapien’s SEO and content function: editorial, category pages, ad creative, recorded explainers, and sales material for a simulated-audience research platform.
- My role
- Growth Manager
- Status
- Published content & buyer guides
Key decisionStructure the explanation around the research question a buyer needs to answer.
Articles, category pages, and sales material
My work connects pricing, concept testing, messaging, and consumer-research use cases to the material a buyer needs to evaluate the product. I develop articles and category pages for search, then use the research in explainers, creative briefs, ad concepts, and sales material.
A services comparison needs to distinguish the research question, evidence source, delivery model, and deliverable. A pricing buyer needs tested offers and assumptions; a concept buyer needs comparative responses and a revision brief. Those distinctions give the content its structure.
| Research job | Material to explain |
|---|---|
| Pricing | The audience, offer variants, choice question, and modeled response at each price. |
| Concept testing | Comparable alternatives, segment reactions, and what to revise in the concept. |
| Selecting a research service | Evidence source, delivery process, outputs, and pricing basis. |
Study: Sapien Team. Portfolio contribution: content, research review, and growth.
Two prices, with the audience held fixed
The published AI-tools guide includes a matched comparison of 256GB offers at $1,999 and $1,599. Within each cohort, the same assigned profiles considered both prices. Keeping the audience fixed makes the change in modeled qualification legible.
Qualification rises from 5.52% to 11.27% in the core cohort and from 6.18% to 12.50% in the expansion cohort. The lower offer also gives up $400 per sale. The result alone cannot select the better margin: that needs costs, realized demand, and the rest of the offer.
Reviewing validation studies
I review academic work, validation studies, and competing methods before turning a result into an article or product explanation. The useful details are often the scenario, comparison group, and unit of measurement, rather than a single accuracy headline.
Sapien’s 14-study comparison includes a useful miss: without nearby fast charging, simulated EV choice in the used-car case was 53.1%, against approximately 34.7% reconstructed from the human choice model. The new-versus-used direction reverses. Reporting that scenario explains more than averaging it into a general success rate.
A match to qualitative findings also has a different meaning from matching population prevalence. I keep those distinctions in the explanation so a reader can assess where a method fits their own research question.
Selected buyer guides
The buyer guides compare tools and providers by research job, evidence source, deliverables, and pricing. The services guide explains how to scope a purchase; the category guides help a reader build a shortlist for that scope.
Selected publications from the content function I built, published under the Sapien Team byline:
13 AI market-research tools
Tool comparison and the matched pricing study shown above.
17 market-research companies
Custom studies, retail measurement, market intelligence, and specialist research providers.
15 consumer-insights providers
Audience research, purchase evidence, retail measurement, and how the deliverables differ.
13 qualitative-research companies
Audience, interview method, evidence, and outputs for qualitative study buyers.