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AI research platform
Ask a real audience & get an answers in hours
StatSocial Digital Twins are AI survey respondents built from the observed anonymized behavior of 150M+ U.S. adults, so you can run market research and pre-campaign studies against any audience you can describe, without recruiting a single person.
No recruiting · No scheduling · 3.3 pts MAE vs. published benchmarks
Use Cases
How you can use StatSocial’s AI research platform
Every use case runs on the same engine. StatSocial works as a single AI insights platform for consumer research, audience research, and qualitative work: field a study at scale, then open a focus group when a result deserves a conversation.
Purchase behavior
Understand what drives purchase, switching, and loyalty in a category, grounded in what your consumers actually do rather than what they recall doing.
Audience research
Confirm who your audience is, what they care about, and where their attention already sits before you commit budget to reaching them.
Focus groups
Moderate a live session with your exact audience. Ask, probe, and redirect the discussion as it unfolds, with every quote traceable to the Twin behind it.
Creative and messaging testing
Put concepts, ads, and positioning in front of your market and see which version moves the segments that matter, before the media spend.
Product concepts and market fit
Gauge demand for new products, test pricing sensitivity, and prioritize features before committing resources.
Media and influencer planning
Find the channels, publishers, and creators a specific cohort already follows, then validate the fit before you sign anything.
How it works
How does AI market research work with Digital Twins?
Build the audience
Define your audience by observed behavior, brand affinity, media consumption, or your own customer file. StatSocial generates the matching Digital Twins from the real signals of 150M+ U.S. adults.
Survey your Digital Twins
Our research team designs the survey with you, then fields it to your twins. Quantitative results come back indexed to a general population baseline.
Moderate a focus group
The same twins move into a live session, where you can probe a surprising result, push a segment on its reasoning, or split the room on a disagreement.
See the detailed reasoning
Every session ends with the full transcript and the reasoning behind each position, traceable to the part of the audience that held it.
Test the work before the spend is committed, not after.
Request a demoPlatform Comparison
AI research tools compared: panels, generative AI, and StatSocial
Three ways to get answers from your target audience, and where each one breaks down.
Request a demo| Traditional panels and research firms | DIY LLM and generative AI research | StatSocial | |
|---|---|---|---|
Who answers |
Opt-in respondents sourced by a recruiter | Personas generated from model training data and demographic proxies | Digital Twins built from the observed behavior of 150M+ real U.S. adults |
| Time to results | 3 to 6 weeks | Hours | Hours |
| Hard-to-reach audiences | Low incidence means long screening and quota compromises | Available in name, but averages toward the general population | Defined from real observed behavior, including B2B, fan, creator, and niche audiences |
| Representativeness | Whoever agreed to show up | Unweighted or weighted to census proxies | Every voice weighted to the share of real buyers it represents |
| Accuracy validation | 5 to 6 pts MAE typical for opt-in panels | Rarely published | 3.3 pts MAE across 40+ Pew, Gallup, and Nielsen benchmarks |
| Qualitative follow up | Separate recruit, separate field | A new prompt with no continuity | Live AI focus groups on the same audience, mid study |
| Focus group voice risk | High. One participant can steer the group | Low, but so is real disagreement | Low. Disagreement is preserved and weighted |
| Auditability | Topline tables and a transcript | Model output with no underlying people | Cohort, distribution, and written rationale behind every answer |
| Repeat studies | New recruit, new field | New prompt | Re-query the same audience as questions evolve |
Customer story
How Shepherd uses Digital Twins and Focus Groups
Shepherd wanted to know whether a niche audience would pay for a new product, the kind of audience that barely exists in standard panels. So they put the question to their Twins, then dug into the reasoning behind each answer.
One subscriber base. Three segments. Three different answers.
Segment 01
Core subscribers
Hesitant to payLiked the concept, but cooled off once the conversation turned to price.
Segment 02
Casual users
On the fenceLiked the idea more than expected, just not enough to pay for it yet.
Segment 03
Prospects
Ready to buyThe most willing to pay, especially when the product felt creator-driven.
“
There isn’t a big delta between how they’re responding as a Twin and how we’re seeing them show up in real life.
Dean McBeth, Managing Partner and Co-Founder, Shepherd
Industry recognized. Client recommended.
Frequently asked questions
Everything teams usually ask before they get started.
An AI research tool uses artificial intelligence to generate market research findings without recruiting live respondents. Most work by prompting a language model to role play an audience. StatSocial works differently: studies are fielded to Digital Twins, respondents built from the observed behavior, interests, and demographics of 150M+ real U.S. adults, so results trace back to real anonymized people rather than to a model’s assumptions.
Accuracy varies widely and most tools do not publish it. StatSocial Digital Twins validate at 3.3 points mean absolute error across 40+ studies benchmarked against Pew, Gallup, and Nielsen, compared with 5 to 6 points typical for opt in online panels.
Generative AI market research asks a language model to imagine an audience from its training data. The output sounds plausible but has no underlying people, no weighting, and no way to audit where an answer came from. Digital Twins are built from observed behavior in StatSocial’s PeopleGraph and weighted to the real composition of your market, and every response can be traced to the cohort that held it.
AI audience research uses AI to profile and question a defined audience without fielding a traditional study. With StatSocial you define the audience by observed behavior, brand affinity, media consumption, or your own customer file, then put questions directly to the Digital Twins that match it, including fan communities, creator followings, and niche B2B segments that panels struggle to recruit.
Yes. Any study can move into a live AI focus group with the same Digital Twins. You moderate in real time, ask follow up questions, challenge a response, or pull a single Twin aside for a one on one conversation. Sessions export with full transcripts and quote level attribution.
Any audience you can describe. Because Digital Twins are built from observed behavior across 150M+ U.S. adults, StatSocial reaches segments that are expensive or impossible to recruit through panels: specialist professionals, subscribers to a specific publication, followers of a specific creator, category switchers, and low incidence B2B roles.
No. Synthetic panels and synthetic personas start from assumptions about who an audience might be. Digital Twins start from observed behavior: real, anonymized behavioral profiles resolved in StatSocial’s patented PeopleGraph. That difference is what produces benchmarked accuracy rather than plausible sounding output.
Full verbatim rationale, quantitative scoring indexed to a general population baseline, and the cohort and response distribution behind every answer, so findings can be sized and defended rather than quoted.
Hours, compared with the 3 to 6 weeks a recruited study typically requires. Once the audience and discussion guide are approved, the survey fields and the moderated session can be ran the same day.
Pricing is scoped to the study, audience size and other parameters. To learn more about our pricing, we encourage you to schedule a demo.