Synthetic Data & Augmented Sample: A Practical Guide for Modern Research

When “real” data can’t show up for the job, synthetic data steps in as a rigorous, validated partner. Scarce incidence, tight budgets, microscopic timelines, and privacy walls can force research teams into bad choices: skipping a study, under-powering it, raiding another budget, or defaulting to the loudest opinion in the room. The promise of synthetic data isn’t magic. It’s modeling. And the solutions it provides are as real as the constraints that make traditional sample impossible.

At the Synthetic Data & Augmented Sample Showcase, explore how modern modeling unlock the studies you can’t otherwise run, the audiences you can’t otherwise reach, and the insights you can’t otherwise afford to miss.

What Synthetic Data Really Is (And Isn’t)

Synthetic data is generated by models trained on real datasets, and it behaves according to patterns you can validate — not wish for. Researchers already rely on modeling tools such as multiple imputation, hierarchical Bayes partial pooling, small area estimation/MRP, agent-based simulations, and bootstrap resampling. Today’s AI-powered systems build on that foundation, uncovering deeper latent structures and enabling natural language interrogation through LLMs.

Showcase participants will illustrate just how far the field has come, including Synthetic Users, whose approach models the emotional and psychological undercurrents traditional questioning struggles to surface. Their work moves teams beyond what people say into what drives them — revealing the subconscious tensions between fear, trust, desire, and risk that shape behavior. It’s a practical demonstration that synthetic intelligence isn’t just filling data gaps; it’s expanding the boundaries of what insights teams can understand.

Why Demand for Synthetic Data Is Surging

As pressures rise — shrinking timelines, smaller budgets, diminishing response rates, and stricter privacy rules — the limits of traditional sample become painfully clear. Data scarcity forces slowdowns, compromises, and re-prioritization. Synthetic augmentation offers a way out of this bottleneck, allowing teams to move faster and more confidently without sacrificing rigor.

One example: Fairgen will show how synthetic boosting helped Big Village elevate its annual 50,000-respondent U.S.-representative dataset into an equally representative ~100,000 respondents. By doubling statistical power without doubling cost, Big Village unlocked niche, regional, and demographic cuts that previously fell below reporting thresholds. Their session provides a transparent walk-through of workflow, validation, and a case study demonstrating how pre-boosting transforms thin bases into confident calls, making deeper insights both efficient and reliable.

What You Can Unlock with Synthetic & Augmented Sample

• Lift statistical power in low-incidence cells
  Generate non-duplicative synthetic respondents to support rare disease studies, niche B2B targets, or underrepresented groups.
• Maintain privacy while preserving analytical utility
  Create privacy-safe synthetic datasets when PII is restricted or inaccessible.
• Accelerate qual exploration with virtual personas
  Digital twins support rapid concept iteration and exploratory learning when timelines leave no room for traditional qual. This principle will come to life in Verve’s session, where Founder Andrew Cooper and Executive Director Richard Preedy introduce their award-winning “silk-grade” intelligent personas. Verve Vero has moved beyond generic, black-box persona generation to create transparent, validated, auditable models used daily by global brands. Their approach shows what it takes to build, maintain, and operationalize trustworthy personas that allow teams to “bring the customer into every decision”—with consistency, depth, and affordability.

How to Validate Synthetic Data (No Mysticism Required)

• Quality assessment mirrors what researchers already know how to do:
• A/B holdout tests
• Equivalence checks on priority KPIs
• Bias and drift monitoring
• Transparent disclosure of model methods

Participants like Panoplai will show how these checks operate at enterprise scale. Their platform connects real and synthetic data inside one explainable system, unifying data ingestion, survey collection, synthetic enrichment, and interactive reporting under a governed framework. In a recent global candy company study, Panoplai’s modeled responses matched human data with more than 90 percent accuracy. Their session will walk through vertical, horizontal, and net-new synthetic studies to demonstrate exactly how teams can audit, validate, and trust the intelligence produced.

When to Use Synthetic Data—and When Not To

Synthetic augmentation excels when:

• Data scarcity threatens statistical power
• Privacy constraints block access to real data
• You need rapid iteration cycles
• A benchmark is more useful than a full-scale replacement

It is not a substitute for uncovering completely novel behaviors that lack any grounding in training data. In those situations, synthetic data works best as a parallel benchmark, helping teams spot drift, test hypotheses, and pressure-check assumptions.

👉 Join the Next Tech Showcase

Join us for the next Tech Showcase to explore emerging approaches and live demonstrations shaping the future of market research. Register here.