A field guide to synthetic data in B2B research

By Jason Talwar, Principal VP, Methods & Innovation

The terminology around synthetic data in B2B research is inconsistent. Vendors use the same words to describe different methods. Practitioners inherit definitions from consumer research that don’t apply cleanly. The quality concepts most relevant to evaluation, like statistical equivalence, model drift, and data provenance, are rarely defined outside academic methodology papers.

This field guide covers those definitions. For the evaluation side of the conversation (the questions to ask vendors and the red flags to watch for), see How to Avoid the B2B Synthetic Data Trap.

The four types of synthetic data

The market uses “synthetic data” to describe four distinct methods with different data requirements, outputs, and risk profiles. Treating them as interchangeable is where bad procurement starts.

Synthetic boosting

Synthetic boosting adds new rows to an existing real survey dataset. The model learns response patterns across all segments (how subgroups relate to each other and to the overall dataset) then generates statistically equivalent responses for underrepresented groups. The underlying mechanism is statistical imputation.

• Requires a minimum of 200 verified real respondents as a foundation
• Produces quantitative outputs only
• Accuracy depends entirely on source data quality—boosting a low-quality dataset produces a larger low-quality dataset

Synthetic modeling

Synthetic modeling generates simulated responses using AI models trained on audience descriptions rather than a verified human dataset. Inputs can include survey data, publicly available market data, or LLM outputs. It deploys faster and costs less than methods requiring real respondents. The tradeoff is specificity. In B2B, where buyers don’t publicly document procurement criteria or vendor evaluations, the model has little real signal to draw from.

• Works best when public data on the target audience is dense
• Hallucination risk is higher
• Outputs should be treated as directional only

Synthetic personas

Synthetic personas simulate an audience segment from scratch based on research data, queryable on demand without running a new study. Unlike synthetic boosting, which extends a specific survey dataset, a synthetic persona is a persistent model of an audience: a segment you can interrogate across a range of questions over time.

• Most useful for hypothesis testing, message iteration, and answering questions between research waves when speed matters more than defensibility
• Quality depends entirely on the data used to build the underlying model

Synthetic twins

Synthetic twins simulate a specific individual from granular, individual-level behavioral data, distinct from synthetic personas, which simulate a segment. The granularity is both the appeal and the constraint. In B2B, individual-level data is sparse, difficult to verify, and hard to keep current. Senior buyers don’t document their procurement behavior publicly. This limits the reliability of individual-level simulation for most enterprise research. Because most B2B decisions are made at the segment level, individual-level outputs are typically anecdotal rather than directionally useful.

Key terms for evaluating synthetic data quality

Training data

The real-world data used to build a synthetic model. Training data is the single most important variable in synthetic research quality—it’s more important than the algorithm, the vendor, or the interface.

Identity verification

The process of confirming a respondent is who they claim to be, typically by cross-referencing corporate email or LinkedIn against verifiable records. Standard research panels don’t require it. In B2B research, where the training data is only as credible as the professionals behind it, verification is the mechanism that makes that credibility documentable.”

The Data Inheritance Problem

The central failure mode in synthetic research: Synthetic outputs inherit the quality of their training data. A model trained on verified, high-quality professional data reproduces those professionals’ behavioral patterns accurately. A model trained on unverified, low-quality, or fraudulent data reproduces those problems faithfully and at scale. The model cannot distinguish between the two.

Incidence rate

The percentage of a broader population who qualify for a specific study. A study targeting finance leaders at enterprise software companies might have an incidence rate of 1–2% in a standard research panel. Consumer studies rarely face this constraint. Low incidence is the structural reason synthetic boosting exists in B2B: qualifying respondents are rare, expensive to recruit, and difficult to reach at sample sizes needed for reliable subgroup analysis. Synthetic boosting addresses this by extending verified data. It does not solve the problem if the underlying data is invalid.

Weighting vs. synthetic boosting

Two methods for addressing underrepresentation in survey data, with different effects on data quality:

• Weighting adjusts how existing responses are counted, redistributing statistical importance across responses already collected. It does not add data and does not reduce margin of error for underrepresented subgroups.
• Synthetic boosting generates new independent data rows from patterns in the existing dataset. Because it adds respondents rather than rebalancing existing ones, it reduces margin of error, producing accuracy equivalent to recruiting those respondents directly.

Statistical equivalence

The standard measure of synthetic accuracy: how closely synthetic outputs match a verified human control group on the same audience specifications, expressed as a percentage. Statistical equivalence measures fidelity to the training data—not absolute accuracy. A model can achieve high equivalence while reproducing its training data’s errors with equal fidelity. Equivalence is only as meaningful as the data it’s measuring against.

Q: How accurate is synthetic data compared to real survey data?

A: When synthetic boosting is trained on verified professional data, it achieves 95–99.5% statistical equivalence with fresh human samples on the same audience specifications. Standard unverified panel data diverges 20–40% from those same verified human controls at baseline, before any synthetic method is applied. The accuracy figure matters, but what it’s measuring matters more. Equivalence with verified inputs is meaningful. Equivalence with unverified inputs is a precise reproduction of unreliable data.

Control group

A set of independently recruited human respondents, not included in the model’s training data, surveyed on the same audience specifications as the synthetic output being tested. Comparing outputs against the control group is the standard validation mechanism.

Hallucination

When a synthetic model generates outputs with no grounding in real respondent data: responses the underlying professionals would never produce. More likely when the model draws on thin data or is asked to simulate behavior outside its training distribution.

Q: Can synthetic research data hallucinate?

Yes, though this applies to generative synthetic methods—not boosting. Boosting extends real data statistically and can produce outputs that diverge from what real respondents would say, but that’s a measurement error, not fabrication. Hallucination applies where the model generates from scratch. In this context, it means fabricating outputs with no grounding in real respondent data. In market research, this typically appears as implausibly low variance, uniform positivity, or confident answers on topics the underlying professionals would be unlikely to have views on. Benchmarking helps surface these failures. 

Model drift

What happens when a synthetic model becomes outdated relative to real market conditions. A model trained on data from twelve months ago cannot accurately simulate how buyers respond to new products, market shifts, or emerging dynamics that post-date the training data.

Q: How do you keep synthetic research data current?

A: Preventing drift requires a direct link between new primary research and model updates. Each new study feeds back into the model and retrains it on current data. Providers who update by pulling from public web data face a particular problem in B2B: Buyers don’t publicly document how their attitudes and behaviors are changing. A model not regularly retrained on fresh verified professional data reflects how your buyers thought, not how they think now.

Benchmarking

The practice of comparing synthetic outputs against a fresh, independently recruited human control group on the same audience specifications. The mechanism for validating accuracy and identifying hallucinations, systematic bias, and model drift over time.

Q: How often should synthetic outputs be benchmarked?

A: Each new primary study is an opportunity to compare what the model predicted against what real respondents said. A provider who can’t commit to ongoing benchmarking is asking you to accept the outputs on faith.

Supporting glossary

Primary research

Research conducted directly with real, verified human respondents. The foundational data layer synthetic methods extend, augment, or simulate. No synthetic output can be more reliable than the primary research it’s built on.

Panel data

Survey respondents sourced from pre-recruited research panels. Verification standards vary widely across providers. A common training data source for synthetic models, and a common source of the quality problems those models inherit.

Margin of error

A statistical measure of how much survey results might vary from the true population value. Synthetic boosting reduces margin of error by generating new independent data rows, while reweighting does not.

Subgroup variance

The differences in response patterns across groups defined by seniority, company size, industry, or buying role. B2B research is frequently commissioned specifically because subgroup differences are the finding. Synthetic methods most commonly fail to replicate real-world subgroup variance, producing aggregate accuracy while masking the segment-level differences that matter.

Systematic bias

Skewed patterns in synthetic output inherited from the training data and reproduced consistently. Distinct from hallucination: bias distorts real patterns, hallucination invents patterns that don’t exist. Both are identified through comparative benchmarking against fresh human controls.

If you’re evaluating synthetic data vendors or designing a B2B research program, NewtonX works with teams at every stage. Start a research project. 

Related reading: How to Avoid the B2B Synthetic Data Trap