In market research, there is a persistent tension between the need for granular insights and the realities of feasibility, timing, and cost. This challenge becomes especially apparent when trying to reach low-incidence audiences, niche geographies, or specific demographic profiles. These are the kinds of segments that often do not appear in large enough numbers to meet traditional sample requirements.
At Big Village, we have begun to explore an emerging approach that’s helping solve for this challenge: synthetic data augmentation.
What is synthetic data (and what it isn’t)
Synthetic data is model-generated data that replicates the statistical characteristics of real respondent-level data. It is built by analyzing known patterns in existing datasets, such as survey responses, and using those patterns to estimate how similar but underrepresented groups are likely to respond.
To be clear, we do not use synthetic data to replace real sample. Instead, we use it to close feasibility gaps, particularly in situations where traditional sampling methods fall short. Think of it as an enhancement layer that allows us to generate more stable insights for hard-to-reach segments.
A real-world example: Local market research at scale
We recently partnered with a major telecommunications brand that needed to conduct research across several U.S. service areas. The scope included 17 Designated Market Areas (DMAs). While a strong representative national base was achievable, gaining readable, statistically stable sample sizes in each individual DMA presented a challenge. In some cases, the available panel simply could not deliver the numbers required for detailed comparisons between markets.
By applying synthetic data modeling, we were able to augment the DMA-level data and create consistent, reliable insights. This synthetic boost allowed for deeper reads at the market level and enabled additional analysis, such as profiling brand users within those geographies. These cuts would not have been possible using traditional methods alone.
Proof of concept: Validating our approach
Before offering synthetic data augmentation as a formal solution, we conducted an internal proof of concept to evaluate its accuracy and reliability. This involved modeling synthetic data for 10 niche audiences and comparing the outputs against a holdout sample of real respondent data.
The validation included demographic cuts by age and gender to assess consistency across subgroups. The results were strong. The synthetic data aligned closely with the real sample, and in several cases, the Mean Absolute Error was lower than that of the holdout group. This provided the confidence needed to move forward and offer synthetic augmentation as a scalable client solution.
Where we see this going
While synthetic data is still relatively new in our space, we are already seeing several promising applications:
- Local market feasibility: Synthetic augmentation is especially useful for regional insights, such as DMA or zip-code level research, which are often costly and difficult to execute using traditional panels.
- Audience expansion for digital activation: Niche subgroups, such as specific interest-based personas or values-driven consumers, can be modeled and expanded for use in our Audience Extension product. Synthetic data helps us establish these segments.
- Fidelity from research to activation: Through our integration across research and media, we can take synthetic-augmented audiences from survey insights to campaign execution. This continuity helps improve segmentation, messaging, and overall media performance.
Why this matters now
Advancements in AI are reshaping the possibilities for market research. At Big Village, we are focused on using these innovations thoughtfully and with a commitment to quality. Our goal is not to replace real respondents or compromise the standards of rigorous research. Rather, we are using synthetic data to responsibly expand the reach and relevance of our insights.
For clients, this opens up new levels of flexibility and scalability. It also enables deeper understanding of hard-to-reach audiences that might otherwise remain out of view.
Want to learn more?
Whether you are facing feasibility issues in a key market, looking to understand a small but strategic customer group, or want to extend your research insights into media planning, synthetic data may offer the right solution.
We would be happy to explore it with you.