NextMinder: A Smarter Way to Understand Consumers (Deep Dive)
From proof to practice: how Synthetic Audiences help leaders decide faster without lowering the bar.
Executive Brief (1 minute)
Leaders shouldn't have to choose between fast and right. A recent study by a global product‑testing leader shows that, under clear conditions (a 'clear‑winner' gap of ~8 points on a 0–100 scale), a small human seed boosted with synthetic respondents can reach the same business decision as a much larger human‑only study: cutting time and cost without lowering the bar. Standards bodies (NIST) and regulators (FCA) now provide clear guardrails for utility, fidelity, and privacy, making synthetic approaches auditable and responsible. This paper explains the evidence, the guardrails, and how NextMinder operationalizes both in 90 days.
A quick story to start
It's Monday morning. A global brand team has three product ideas on the table and a launch window closing fast. The old playbook says: run a big, multi‑country survey, wait weeks, then decide. But the market won't wait: competitors won't either.
This is the moment where Synthetic Audiences change the equation: Can we get to the same decision we'd reach with a much larger study, only faster and at lower cost?
What we mean by "Synthetic Audiences" (plain language)
Short version: digital, statistically built "look‑alikes" of real consumers.
How they're built (three simple steps):
1) Start with humans: a small but representative group of real people (your "seed").
2) Learn patterns: a model studies how their answers relate (e.g., people who like X often also like Y).
3) Scale up safely: the model creates additional, synthetic respondents that follow those same patterns.
Why do this? To test ideas across segments and markets in days, not months: without losing decision quality.
Plain words: we're not inventing opinions; we're scaling patterns from a solid human sample and checking against real outcomes.
Decision‑level evidence: what the study found (in plain English)
The big question: If we run a small human test and boost it with synthetic respondents, do we reach the same business decision as a much larger, human‑only test?
Key finding – the "clear‑winner" rule:
When the top option is clearly better (≈8 points or more on a 0–100 scale), 50 humans + synthetic boost reproduced the same go/no‑go decision and a very similar order of winners as a 200‑human test. Statisticians describe the rank agreement as ~0.8 correlation (plain words: the rankings largely line up).
Guardrail: the human seed must be representative.
When the small group didn't reflect the larger sample (wrong mix of ages/users/markets), 20–30% of tests didn't match. When quotas were aligned, the decision matched using 50 humans + 150 synthetic vs. 200 humans.
Plain words: get the small group right, or the boost won't help.
Where the speed comes from: fewer prototypes and recruits to ship or schedule. Public communications from the study cite up to ~50% faster product testing in some contexts.
Governance & trust (put first, in simple terms)
Leaders need a reliable way to trust synthetic results. Two credible anchors help:
1) NIST SDNist (U.S. standards body): a toolkit that creates a quality report scoring utility (does it work for the task?), fidelity (does it resemble the source data?), and privacy (is it safe?). Plain words: a standard dashboard you can show your Risk/Legal team.
2) UK FCA (regulator): guidance to validate privacy + utility + fidelity as the three pillars when adopting synthetic data. Plain words: don't just ask "is it private?", also ask "does it work?" and "does it reflect reality?"
Beyond product tests: independent research signal
Peer‑reviewed science (PLOS ONE, 2024) shows models trained on well‑built synthetic tabular data can perform almost identically to those trained on real data, with accuracy differences often as small as 0.001–0.006 on a 0–1 scale (AUC). Plain words: the model behaved almost the same whether it learned from real or synthetic data.
How NextMinder puts the insight into practice
1) Seed first, then scale: build a representative human seed by market/segment and document quotas.
2) Prove the decision, not just the math: paired tests: Small+Synthetic vs. All‑human (or historical large‑N). Check same go/no‑go, rank agreement, error bars.
3) Make it auditable: produce NIST‑style quality reports (utility, fidelity, privacy) for pilots and refresh over time.
4) Use a simple threshold policy: if there's a clear winner (≈8‑point gap), Small+Synthetic is allowed; if it's a tight race, increase human N or run a targeted top‑up.
5) Keep it honest over time: schedule drift checks (quarterly or after big campaigns) and re‑validate if audience/messages shift.
What results should executives expect? (ranges you can defend)
• Time‑to‑insight: after initial setup, many product/message tests move weeks → days; public comms cite up to ~50% faster cycles in product testing contexts.
• Decision quality: in clear‑winner scenarios (≈8‑point gap), Small+Synthetic matched the same business decision and showed strong agreement on the order of winners (~0.8). Plain words: same call, faster.
• Cost profile: fewer shipped prototypes and smaller human samples often mean material savings (public ranges of ~20–60% in some contexts). Plain words: more tests for the same budget.
Boundary: when differences are tiny, don't force it. Add human sample or use qualitative work to understand the "why." Synthetic boosts humans; it doesn't replace them.
Mini‑glossary (right where you need it)
Representative seed: your small human group looks like the audience you care about (right mix of users/markets). If this is off, the boosted result will be off.
Correlation (~0.8): a score of how well two rankings line up. 0.8 means "largely similar order," not identical, good enough for the same decision when the winner is clearly ahead.
8‑point gap (0–100 scale): rule of thumb: when the winner beats the laggard by ~8 points or more, Small+Synthetic can mirror big‑N decisions.
Utility / Fidelity / Privacy: does it work for the task, does it resemble the source, and is it safe for people's data? (What NIST/FCA ask you to check.)
A final scene
Back to that Monday meeting. The team runs a Small+Synthetic test using a well‑built seed. The results point to the same winner the larger study would have picked, only they see it this Friday, not six weeks from now. They adjust the creative, rerun a quick check, and go to market with confidence. That's what we mean by a smarter way to understand consumers: fast, responsible, and decision‑grade.
Sources (select, to be linked on site)
• Ipsos (2025): The Power of Product Testing with Synthetic Data (Humanizing AI, Part 2).
• Ipsos (2025): Speed up Product Testing by up to 50% with Ipsos Synthetic Data (news/knowledge page).
• Pereira et al., PLOS ONE (2024): Assessment of differentially private synthetic data.
• NIST SDNist v2
• UK FCA (2024): Report: Using Synthetic Data in Financial Services.
Let's turn insight into action.
Want to see how Synthetic Audiences can transform your testing or targeting process?
Get a tailored walkthrough of how it works, no strings attached.