The debate around synthetic research keeps getting framed as a binary: either it will replace human panels entirely, or it is not ready to be taken seriously. Neither is true. Teams generating real value are using synthetic data to make human research better, with synthetic panels enabling faster and more affordable iteration work upfront. Human panels are reserved for deeper questions and provide validation that only human conversation can deliver.
Allstate, Navy Federal Credit Union and Zip are among the organizations now using a blended methodology to cut research timelines from weeks to hours.
Where teams are putting synthetic panels to work
1. Screen more concepts, commit budget to better ideas: Gabb Wireless
Most research teams are not short on ideas. The problem is knowing which ones are worth pursuing. Synthetic panels let teams evaluate high volumes of concepts simultaneously, including historical ideas the market was not ready for, ideas from different teams, and innovations that have succeeded in adjacent markets, without the lead times or fielding costs that typically restrict exploration.
Because synthetic panels do not suffer the survey fatigue that limits variable testing on human panels, teams can run broader screening rounds before committing real-world panel budget. The result is a shorter, targeted shortlist going into human research, with more room to go deep on the ideas that need it.
Gabb, a maker of safe technology products for children, needed a faster and more cost-effective way to understand parents' concerns without sacrificing data quality. Traditional research was slow and struggled to provide a clear view of niche audiences. Gabb ran a synthetic panel study in parallel with a human panel, comparing responses on device ownership, parental control triggers, and attitudes toward screen time. The rank order of what triggers parents to tighten device controls aligned closely across both groups, and the synthetic data accurately mirrored broader public discourse on topics like the anxious generation.
The program delivered reliable, directional signals in a fraction of the time and cost, and confirmed that a blended approach is optimal: synthetic to quickly triage and narrow concepts, human panels to validate the most promising ones before high-stakes decisions.
2. Optimize surveys before fielding and arrive at analysis faster: Booking.com
A poorly designed question is not always visible until it is too late. Stress-testing a survey against a synthetic audience before fielding surfaces double-barreled questions, ambiguous phrasing, and dead branches that would otherwise waste a human round. It also lets teams build the analysis and report structure before human data begins fielding, so when results land, the team is confirming and sharpening a point of view rather than starting from a blank page.
Booking.com uses consumer insights to understand and anticipate emerging trends in travel preferences. When comparing synthetic responses to those of human participants in its annual Travel Trends survey, the synthetic data uncovered a nuance hidden in the depiction of "solo travel," specifically a distinction between traveling alone en route to meet others versus a true solo trip. After noting the gap, the research team re-examined the human responses and found that roughly half had misinterpreted the question. Catching the ambiguity deepened the team's insight into solo traveler habits and preferences, the kind of correction that would have required a costly re-field to surface through human research alone.
3. Protect long-term data assets and reduce risk in high-stakes research
Synthetic data is particularly well suited to two categories of risk that complicate human research. The first is sensitive topics, where synthetic panels build consensus without collecting PII, without requiring personal disclosure, and without exposing confidential concepts to the market before they are ready. The second is long-term data protection, where even small methodology changes to a brand tracker or customer experience benchmark can introduce incongruences that undermine years of trend data.
For brand sentiment work specifically, synthetic enables competitive benchmarking, hypothesis testing around positioning scenarios, and segment-level analysis of how different audiences might respond to brand changes, all before a single human respondent is recruited. Teams can test new scales, swap attributes, or adjust question structure in a synthetic environment first, so live tracker data stays clean.
A leading technology company was concerned that user prompts might lead a generative AI application to create problematic images. Human panel testing was an option, but if the results were indeed problematic, the testing process itself could have caused brand damage. By using Qualtrics synthetic panels instead, the team was able to test a sensitive topic without involving human participants and ensure that outputs fit within their desired parameters, without any risk of unwanted brand exposure.
4. Use continuous synthetic data to enter every study with a head start: Dollar Shave Club
Most research programs are built around projects: a study launches, data is collected, findings are delivered, and the cycle starts again. The gaps between studies are where trends emerge undetected and where the business ends up acting on information that is already months old by the time it reaches a decision.
Synthetic enables a continuous stream of market intelligence that runs alongside project-based research rather than replacing it. Teams can monitor broad shifts in attitudes, behaviors, and preferences on an ongoing basis, building the analysis and report structure before human data even begins fielding. When a new study launches, it is already informed by what has been happening in the market, and when results land, the team is confirming and sharpening a point of view rather than starting from a blank page. The result is a tighter loop between research and business decisions, and a research program that is always moving rather than stopping and starting.
Dollar Shave Club used synthetic panels to explore whether their brand could expand into a consumer segment they had never served. Rather than committing to lengthy traditional research upfront, they tested assumptions about shopping behaviors, brand perceptions, and product preferences in days. Synthetic correctly identified that the target segment shops primarily at accessible retailers and direct-to-consumer channels, mirrored human data on routine-based attitudes, and produced identical thematic segmentation around "experience" versus "necessity" shoppers, compressing a research timeline that would have exceeded a month into a matter of weeks.
5. Answer the next question without a new study
Every completed study generates follow-up questions that nobody thought to ask at the time. Recontacting participants or launching a new study to answer them costs days, weeks, and budget that most teams do not have. Teams can use synthetic panels to recontact the original respondents, simulating how they would have answered the follow-up and anchored to the moment they responded—with no attrition and no risk of opinions shifting between waves.
The practical outcome is that every completed study becomes a reusable asset. When findings land and the business wants to go deeper, teams can answer the next question without going back to field.
Synthetic and human research are stronger together
The common thread is the hybrid research methodology. Synthetic doesn’t replace human panels. It works before, after, and even between human studies so researchers can arrive at their panel sessions with sharper hypotheses and more room to do the work that only human conversation makes possible.
Rather than "should we use synthetic?," insights teams should be asking "how do we produce rigorous research at the speed the business needs?" With synthetic capabilities, quality can be reinforced and insights scaled while teams eliminate the wasted effort that slows research down.
