When a team decides not to test an idea because research takes too long or costs too much, the cost of that decision doesn't appear on any budget line. It shows up later as a product feature that missed the mark, a campaign message that didn't resonate, a pricing model that generated unexpected resistance. Research teams that have named this dynamic describe it as a 'priority-only' mindset: validation is reserved for the highest-stakes decisions while dozens of lower-stakes decisions, collectively carrying significant aggregate risk, are made on intuition. Synthetic panels don't eliminate research cost or time—but they compress both enough that the threshold for 'worth testing' drops significantly.
This guide covers what synthetic data is, where it performs well, where it doesn't apply, and how to run your first synthetic study in Qualtrics. It closes with a framework for calibrating trust in synthetic results over time.
What you’ll learn
- What synthetic research is, what it's built to approximate, and where it earns your trust
- How to set up and launch your first synthetic study in Qualtrics
- Learn how to leverage synthetic research across your market research insights program
What synthetic data actually is
Synthetic respondents are trained on large datasets of survey responses to simulate how real population segments answer survey questions. They're built to reflect the attitudinal, behavioral, and demographic patterns in real human data, which means they produce responses that correlate meaningfully with how actual people in similar segments tend to answer the same questions. Qualtrics's synthetic panel capability is built on a fine-tuned foundational LLM model trained to reflect the US general consumer—which defines both its strength and its boundaries. Think of synthetic panel data as a different kind of evidence than human panel data, not an inferior version of it: directionally useful for the right questions, and not applicable for others.
The use cases where synthetic panels perform best
Synthetic panels aren't limited to a single research application—they open up capacity across several modes of use, each addressing a different constraint that traditional research imposes on your program.
1. Rapid innovation: Use synthetic as a high-speed filter to identify your strongest ideas. Assess an unlimited number of rough concepts or features without fielding cost gating your exploration. Take a strategic validation approach: eliminate non-starters with synthetic, surface the winners, then bring your shortlist to human panels for confirmation. You're adding a triangulation layer, not replacing your validation step.
2. Survey optimization: Combat survey fatigue and the reality of human attention spans. Test comprehensive pools of items with synthetic respondents to collect preliminary, contextual data—stress-testing every question to determine whether it justifies space in future human rounds. The result is a tighter, higher-performing instrument by the time it reaches real respondents.
3. Insights-first strategy: Accelerate the time between data collection and business impact. Field to synthetic audiences first, analyze broad trends, and set up your report structures before human data even begins. When human results come in, you're refining conclusions rather than starting from scratch. Synthetic also eliminates the scheduling constraint—when a question comes up mid-cycle or in the middle of a stakeholder conversation, you can field it now rather than parking it until the next window.
4. Risk mitigation: Future-proof long-term data assets and test instrument changes without exposure to customers. Run sensitivity analyses on tracker modifications—brand tracking programs, CX pulses, recurring benchmark studies. Test new scales, swap attributes, or adjust methodology in a synthetic environment before putting live data continuity at risk. Synthetic also lets you explore sensitive topics, early-stage IP, or competitive positioning without exposing that thinking to customers or the market.
5. Augment customer signal: Juxtapose what you already know about your customers with broader market signals. Explore trends not yet surfacing with current customers. Test offerings with new segments. Simulate perspectives from millions rather than just your existing panel. When customer journey data surfaces insights that warrant deeper investigation, synthetic lets you pursue those threads immediately—ranking solutions, understanding competitive benchmarks, and stress-testing hypotheses without waiting for a new fielding cycle.
→ Where synthetic stands on its own: Early exploration, comparing options, message and concept iteration, directional reads, and fast diagnostics. These are contexts where speed and breadth matter more than precision, and where synthetic data gives you a clear, defensible basis for the next decision.
→ Where human data belongs alongside synthetic: When the decision is high-stakes and the cost of a wrong read is significant. When you need specific, recent lived experiences that a model can't simulate. When you need continuity with an existing tracker or trendline where methodological consistency matters. In these contexts, synthetic can still play a role upstream—screening, pre-testing, shaping your approach—but the final evidence base should include human validation.
Running your first synthetic study
Step 1: Prepare your survey for synthetic fielding*
Before submitting to synthetic fielding, work through this checklist: remove JavaScript and custom scripting; convert any Matrix, MaxDiff, or Ranking questions to multiple choice; move any text block content directly into question stems; remove open-text questions; remove custom end-of-survey redirects; and empty your survey trash.
Step 2: Access Synthetic Response Generation
Navigate to Distributions, select Online Sample, and choose Synthetic Response Generation. Start with the US General Population Synthetic template to ensure your synthetic sample reflects a statistically accurate demographic mix.
Step 3: Configure targeting and quotas
From the Targeting tab, adjust quotas to oversample specific groups if your research question calls for it—for example, a higher percentage of high-income respondents or a specific age bracket. The system configures its synthetic personas to match those parameters automatically.
Step 4: Review and launch
Check the Panel Summary to confirm the demographic distribution looks right. Use your established naming convention for the panel. Launch—synthetic responses typically populate within hours rather than days.
Step 5: Filter and analyze using the respondent type tag
Synthetic responses are automatically tagged with Q_RespondentType = 'Synthetic' in your data file. Use this field to create a filtered view that separates synthetic from human data for comparison, or combine them for overall analysis depending on your use case.
*Access to synthetic panels depends on your license. If you don’t see them, contact your Qualtrics Account Executive or Brand Administrator.
Building fluency with a new research method
Synthetic panels don't just approximate what human panels tell you—in many cases they reveal something different. Preference signals unfiltered by social desirability. Patterns that only emerge at a volume human fielding can't support. Early reads on segments you wouldn't have recruited. The two methods are complementary, not redundant. The researchers getting the most value from synthetic have stopped treating it as a mirror and started treating it as a second lens.
Start with the mode that matches your most immediate constraint. More ideas than fielding budget—start with rapid innovation. Surveys that are too long—start with survey optimization. Stakeholders waiting weeks for directional answers—start with an insights-first approach. The entry point matters less than the habit: run studies, evaluate results, build intuition.
When you review synthetic results, you're pattern matching. Do the rank orders make sense? Are preference signals directionally consistent with what you know about your market? Are segment-level splits coherent? That's the standard—results that are sound, useful, and actionable for the decision at hand.
The fluency you build through hands-on use is also what equips you to answer stakeholder questions about when to trust synthetic data. Organizational trust follows researcher confidence.
Next step: Make sure your insights reach decisions before they expire. Faster research creates a new problem: more findings competing for stakeholder attention. Learn how to deliver insight in the formats that actually get read—an executive digest that lands before the meeting, and a highlight reel that puts customer voice in the room before anyone asks for it. →
