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Minds

August 21, 2026·Comparison·Minds Team

# **Synthetic Audience Data Sources Compared**

Synthetic audiences differ less by the word persona than by the evidence used to construct them. Buyers should separate observed, licensed, customer-provided, public, and inferred inputs before comparing any output.

[Build a grounded audience](https://getminds.ai/?register=true)

Synthetic audience quality begins before the first prompt. The central question is not “which AI model powers the persona?” but “what evidence defines this population, and what right does the vendor have to use it?” A useful comparison separates five input classes: directly collected human data, licensed behavioral data, customer-provided evidence, public evidence, and model inference.

No class is automatically superior. Direct interviews can be rich but narrow. Transaction records reveal behavior but not motive. Customer research is specific but may be stale. Public statistics are auditable but often coarse. Inference fills gaps but creates the greatest provenance risk. The right synthetic audience combines sources that fit the decision and makes every assumption visible.

## Data foundation comparison

| Platform | Publicly described foundation | Primary advantage | Diligence question |
| --- | --- | --- | --- |
| Minds | Customer, partner, attached, and suitable public research; reusable persona knowledge; explicit assumptions | Inspectable construction around the buyer's research context | Which distributions are observed, targeted, reconstructed, or assumed? |
| Simile | Real people, proprietary human-behavior data, behavioral and transactional data, and customer data | Human grounding plus continuous calibration | Which results use the base population versus a customer-trained model? |
| Aaru | Public data, licensed transactions, point-of-interest visits, search demand, media use, and customer data | Broad observed-behavior coverage at population scale | Which licensed signals cover this geography and decision? |
| Electric Twin | Audience twins informed by panel and organizational data, with orchestration and validation | Reusable corporate audience access | Which panel vintage, holdout, and model version produced this twin? |
| Artificial Societies | Persona construction from public or supplied context plus social-network and influence modeling | Relationships become part of the simulation | Which traits and graph edges are observed versus inferred? |

The table reports public positioning reviewed on 21 August 2026. It does not imply exclusivity, and an unlisted source should be treated as not publicly documented until the vendor confirms it.

## Directly collected human data

Direct interviews, surveys, and passive human observations can give a model a concrete behavioral anchor. Simile makes this the center of its public moat: it says every population starts with real people and is validated repeatedly against human evidence. Buyers should examine consent, reuse rights, withdrawal rights, demographic coverage, and whether a result is calibrated to the specific customer or drawn from a general population model.

Direct collection does not remove sampling error. A beautifully interviewed cohort can still miss the market relevant to a decision. Ask for coverage and subgroup performance, not only total respondent counts.

## Licensed behavioral and transaction data

Aaru's public methodology emphasizes transactions, location, search, media, statistics, and other behavioral inputs. These signals can answer a different question from survey opinion: what populations do, not only what they say they would do.

The diligence burden is dataset-specific. Coverage may vary by geography, category, period, and consumer type. A source can be large while remaining weak for a niche B2B market or a new behavior. Buyers should request the date range, population coverage, licensing scope, matching method, and validation for the exact intervention being simulated.

## Customer and first-party research

Customer data can be the most decision-relevant input because it reflects the actual product, audience, and market. It can also create leakage if the benchmark outcome is included in the material used to construct or answer the study.

Minds supports reusable audiences built from explicit briefs, uploaded or linked material, existing persona knowledge, and suitable research. In the audience workflow, completed respondent data can supply observed distributions, while screeners and unfielded questionnaires define candidate options, exclusions, and target quotas. Where a source names a variable but gives no share, a proposed distribution can be labeled as an assumption instead of attributed to the file.

For high-trust work, freeze the source bundle, record hashes or versions, and keep benchmark outcomes outside the runtime. See the [Minds applied validation](https://getminds.ai/research/synthetic-gen-z-food-survey-validation-2026) for an example of outcome isolation.

## Public data and institutional segmentation

Public statistics are valuable because an independent reviewer can inspect them. Their limits should be equally visible: they may be old, aggregated at the wrong level, or unrelated to the behavior being predicted.

Minds also supports customer-specific or sector-specific populations and institutionally grounded segment systems where enabled. Its public [SINUS-Institut partnership](https://getminds.ai/newsroom/minds-partners-with-sinus) provides one such foundation. Research groups such as [INTEGRAL](https://www.integral.co.at/en/expertise) also show why established segmentation, product-testing, pricing, and online-research practice matters. A named framework is not a universal accuracy guarantee; its value is a more defensible vocabulary and source lineage for the market it covers.

## Inferred traits and reconstructed distributions

Every platform must fill gaps. The important distinction is whether that operation is visible. An inferred political preference, reconstructed joint distribution, generated persona trait, or network edge should not be presented as observed fact.

When only marginal distributions are available, Minds uses an explicitly independence-based joint reconstruction rather than inventing correlations. That is still an assumption. A reviewer should be able to see it, change it, and test sensitivity. The same standard should apply to every vendor.

## Buyer checklist

1. List every source used to construct the population.
2. Label each source as collected, licensed, customer-provided, public, or inferred.
3. Record geography, population, date, rights, and refresh cadence.
4. Separate observed distributions, target quotas, and assumptions.
5. Hold outcome data outside the generation runtime.
6. Test subgroup coverage and sensitivity to missing sources.
7. Record the model, prompt, population, and source versions.
8. Decide which findings require real-human or behavioral confirmation.

## Related evidence

Continue with [validation and accuracy compared](https://getminds.ai/comparison/synthetic-audience-validation-and-accuracy), [independent personas vs networked societies](https://getminds.ai/comparison/independent-personas-vs-networked-societies), the [Minds methodology](https://getminds.ai/research/methodology), the [validation checklist](https://getminds.ai/research/synthetic-audiences-validation-checklist), and [Electric Twin alternatives](https://getminds.ai/blog/electric-twin-alternatives).

## **Frequently asked questions**

### **What data is used to create synthetic audiences?**

Depending on the platform, synthetic audiences may use recruited interviews, panel responses, licensed behavioral and transaction records, customer research, public statistics, media or web behavior, and model-inferred traits. These inputs should be labeled separately.

### **Is proprietary data always better for synthetic research?**

No. Proprietary data can improve relevance and defensibility when it fits the decision, but freshness, legal rights, coverage, bias, and validation matter more than the label proprietary. A transparent public statistic may be stronger than an opaque proprietary proxy.

### **How does Minds ground a synthetic audience?**

Minds can build reusable audiences from explicit briefs, existing Minds, attached files or links, approved research, partner material, and suitable public sources. Observed distributions, target quotas, and assumptions are kept distinct in the supported workflow.