When you have 50 early product ideas, you need a fast way to sort them. Whether it’s due to budget or time, you can’t test them all with consumers yet. But cutting the list too soon means a promising idea may never get a shot.
That’s where synthetic respondents give you another option. They can help you make an informed first cut before testing with real consumers.
But the first cut is only the beginning. Then you want answers to questions like: Which benefits drive interest? Why does one concept resonate more than another? What would make this stronger?
These types of questions require direct human feedback, meaning synthetic respondents and human research have different jobs at different points in the innovation process.
In this article, I’ll describe what synthetic respondents are, when to use synthetic vs. human research and how both can help you get stronger ideas to market.
A synthetic respondent is an AI-generated research persona designed to
simulate how specific demographic, attitudinal or behavioral profiles might respond.
Zappi's synthetic respondents, for instance, are grounded in real demographic and behavioral data. They use AI to estimate how consumers may respond to early product ideas, so you can gauge which ones to prioritize.
Synthetic respondents can give fast, directional feedback at scale early in your ideation process. You can quickly sort 30 or 300 ideas with synthetic respondents before moving onto a shorter list to explore with human participants.
It’s important to remember that research with synthetic respondents shouldn’t be the final stop. As you refine the concepts, human research provides the direct consumer evidence teams need.
Synthetic respondents are most useful at the prioritization stage, before you need to understand the deeper nuances of consumer responses. At this point, the goal is to identify where to focus deeper research.
Here are a few instances:
Imagine your innovation team has 150 ideas for new snack products. Testing every one with human respondents would require significant time and budget. The team is more likely to cut the list before any consumer input enters the picture.
Synthetic respondents make it practical to evaluate more ideas at this stage with a better understanding of how the real target audience could respond. That gives a broader range of possibilities a chance to show potential before the team decides where to invest in human research.
You’re not looking for your next winning concept yet. You’re looking for ideas that show promise and may be worth moving to the next stage with humans.
For example, a beverage company might be considering ideas ranging from a high-protein iced coffee to a botanical energy drink to a sparkling prebiotic lemonade. These are meaningfully different concepts, which makes them better suited to early prioritization than small variations of the same idea.
Synthetic respondents can help identify stronger and weaker ideas across the pool, so the team creates a useful shortlist for deeper human research.
Sometimes the alternative to synthetic research isn't human research. It's no research at all.
Early-stage decisions often happen quickly, when the time and cost of commissioning human research may be difficult to justify. Synthetic respondents give teams another option: bring an AI-generated response into the decision instead of relying entirely on internal judgment.
That makes research practical at a point in the innovation process where it might otherwise be skipped.
As promising ideas move from prioritization to concept testing, the questions become more specific — and the decisions more consequential.
Now you need to understand how real consumers respond, why they respond that way and what those reactions mean for the concept you're developing.
That’s where human research plays a bigger role.
Synthetic pre-screening can tell you if an idea looks promising. Human research helps you understand why consumers respond to it the way they do.
Say a high-protein iced coffee shows promise during pre-screening with synthetic respondents. Next, test it with consumers to go deeper. Protein is having a moment. 70% of Americans said they were trying to consume protein in 2025, with motivations ranging from general health and muscle support to weight management and feeling full.
Human research can help the team understand how those broader motivations translate to this particular idea. Are consumers excited about getting more protein, or about getting it conveniently in a drink they already consume every morning? Do they see it as a breakfast substitute, a post-workout option or simply a more functional coffee? Does adding protein make the product feel healthier or raise questions about taste and texture?
Those answers can influence everything from the target consumer and occasion to the benefits the team emphasizes as it develops the concept.
Synthetic pre-screening helps distinguish meaningfully different ideas. But once you start comparing variations on the same idea, the research question becomes more nuanced.
Take the protein coffee example. Now you want to know whether to emphasize sustained energy, feeling fuller longer or all-in-one breakfast convenience.
By now you know protein coffee has potential. Next, you want to hear what consumers like about it.
With your shortlist in hand, the work gets more specific. Who is this really for? Which benefits matter most and how should you position it?
That evidence becomes even more important as the stakes increase. Choosing which ideas deserve another round of research carries relatively little risk. Committing resources to product development and preparing for launch are different decisions. Human research gives teams the deeper evidence they need to refine and validate an idea before making those larger investments.
"We can fit in a round of consumer input at almost any phase now...it can really be about: ‘How do we take this thing & actually make it the best version and get the most out of it?’ That change from evaluation to optimization is really powerful."
— Matt Cahill, Senior Director, Consumer Insights Activation at McDonald's
Synthetic and human research work best as different parts of the same innovation research cycle. It’s not a matter of choosing one or the other. It’s about using the right kind of evidence for the decision you need to make at each stage.
In practice, that creates a progression:
Ultimately, synthetic research can help teams determine what deserves research, while human research helps determine what should move forward and how.
"Zappi gives us the ability to respond to business questions fast, so we can test more up front. When we get better upstream, we increase our chances of ending with an innovation that will be successful in the market."
— Elaine Rodrigo, Chief Insights and Analytics Officer, Reckitt
Zappi’s Synthetic Idea Pre-Screener sits between idea generation and deeper testing with human respondents, allowing teams to evaluate 30 to 300 early ideas at once before deciding which ones to pursue.
Our synthetic respondents are grounded in real demographic and behavioral data to evaluate ideas. The results rank them based on purchase likelihood, flag distinctive ideas and surface positive and negative themes.
For example, a team might start with 100 meaningfully different ideas and use the Pre-Screener to see which rise toward the top of the pool, which appear less promising and which distinctive ideas may deserve a closer look. These signals help teams decide what deserves deeper investment.
From there, selected ideas can move into Zappi’s human research solutions, such as Screen It or Activate It. Human respondents can then help teams understand why an idea resonates, compare more nuanced concepts and refine the ideas they decide to develop.
Remember: Synthetic respondents make the first cut smarter. Human consumers provide the depth and confidence teams need as the questions become more nuanced and the business stakes increase.