Field augmentation through reliable statistical methods is transforming how we approach dreadful data collection gaps in market research — and the market is in urgent need of it.

We’ve all experienced the pains of low representativity in the past. More often than we’d like, field collection falls short of its targets. At best, this results in delays and higher costs. At worst, insights are compromised with boosters from low-quality panels or via respondent duplication.

Augmented synthetic respondents to the rescue — how AI is changing the game

You have probably read about it elsewhere. Synthetic data is transforming market research at an unparalleled pace. Here at Fairgen, we are proud to lead a movement that uses AI-generated respondents to enhance real data as opposed to replacing it. You can read more in our Complete Guide to Synthetic Data Applied to Research.

By intelligently “completing” existing datasets with predictive synthetic data, this technical breakthrough boosts precision—especially for niche segments that are traditionally underrepresented.

In this post, I’ll walk you through concrete use cases for augmented synthetic respondents across several key market research applications: Usage & Attitude (U&A) studies, brand tracking, brand equity measurement, ads testing, product testing, pack testing, and opinion polling. I’ll also highlight additional examples of segments that can be strategically boosted and why understanding these segments is crucial.

Usage & Attitude (U&A) Studies

U&A studies are critical for CMI teams to reveal consumer behaviors and support smart segmentation decision-making, but often critical groups are too small for reliable analysis. For example, in a national survey of 1,000 respondents, the subgroup of heavy users of a niche digital app might represent only 3–5% of the total.

  • How does it work? U&A studies typically collect data from 1,000+ respondents, allowing purpose-built AI systems to extrapolate patterns and generate boosts for various sub-segments and rare cross-tabulations.
  • What segments are commonly boosted?
    • Heavy and early adopters
    • Tech-savvy consumers
    • Rural users
  • Impact: Enables brands to refine niche go-to-market strategies with a smarter understanding of critical segments such as early adopters of highly innovative products and rural consumers, including cross-tabulations with distinct demographics.
  • Example: A consumer electronics company uses field augmentation to understand tech-savvy early adopters of smart home devices, informing product development and targeted communications.

Brand Tracking

Brand tracking studies are among the most expensive, requiring broad reach and consistent data for reliable results. Due to its high cost, data collection is scrutinized and optimized to avoid oversampling. While overall trends are trusted, key segments often remain underrepresented, causing volatility in performance indicators when filtering the data.

  • How does it work? AI models learn from both past waves and current data, generating additional responses for important segments initially small in number.
  • What segments are commonly boosted?
    • Loyal vs. occasional buyers
    • Brand switchers
    • Niche brand users
    • Young consumers
  • Impact: Brands gain consistent monitoring for groups of all sizes while reducing the need for oversampling and increased collection costs. Insights teams get instant access to reliable trends for rare groups, with longitudinal accuracy across past and present data.
  • Example: A fashion brand uses field augmentation in emerging markets to track brand health among younger, digitally native consumers.

Brand Equity Measurement

Brand equity studies often focus on intangible factors such as loyalty, perceived quality, and emotional connection. When key groups (like high-value customers or brand advocates) are underrepresented, the insights will lack the nuance needed to drive change.

  • How does it work? Models generate synthetic respondents reflecting behaviors and opinions of strategic segments.
  • What segments are commonly boosted?
    • Premium/VIP customers
    • Brand advocates/detractors
    • Environmentally conscious
  • Impact: More data points translate into brand equity scores that more accurately reflect consumer sentiment.
  • Example: A luxury car manufacturer uses field augmentation to understand brand equity among loyal, high-net-worth customers, tailoring exclusive experiences and communications.

Ads Testing

Ad testing surveys require quick, statistically sound feedback. When campaigns target specific demographics, the sample size ends up being too small due to the fast turnarounds needed.

  • How does it work? The model is trained on the full sample, allowing researchers to boost one or a few segments, effectively enlarging the testing pool for a cut of interest.
  • What segments are commonly boosted?
    • Social media-savvy users
    • Lifestyle segments (health-conscious, etc.)
    • Regions
  • Impact: Gives insights teams the ability to cut the data and better understand one or a few segments, given the low numbers in the general population.
  • Example: A beverage company uses field augmentation to test an ad campaign aimed at health-conscious millennials, refining messaging before a nationwide rollout.

Product Testing

Product testing is associated with high collection costs and slower timelines, given the need to recruit, ship, and collect the data, making it very difficult to analyze sub-groups.

  • How does it work? The model learns from responses of both frequent and infrequent users, creating additional synthetic data that mirrors a broader audience.
  • What segments are commonly boosted?
    • Power users
    • Enthusiasts
  • Impact: Arm decision-makers with more precision in understanding one or a few key segments.
  • Example: A tech startup that releases a beta version of its new app to a limited group uses field augmentation to simulate a broader user base, including tech enthusiasts and casual users, revealing detailed insights about usability and feature desirability.

Opinion Polling

Public opinion polls stretch national representative sampling to its limits, as there's typically strong interest in marginal groups.

  • How does it work? The model learns from patterns of the entire national representative population, allowing researchers to boost multiple marginal segments, including complex cross-tabulations.
  • What segments are commonly boosted?
    • Young, Gen Z
    • Underrepresented communities
    • Rural demographics
    • Swing voters, first-time voters
  • Impact: Greater representativity allows for better analysis of critical groups for social and political purposes.
  • Example: During a national election cycle, an opinion poll ends the collection cycle with younger voters and minority communities underrepresented. Field augmentation generates additional responses reflecting their unique perspectives, resulting in a more balanced and accurate understanding of electoral trends crucial for political campaign decisions.

Conclusion

Regardless of the study type, quantitative research now has a new and valid methodology for better understanding niche populations.

Field augmentation using predictive synthetic data is revolutionizing market research by overcoming known limitations of traditional methods. By strategically boosting key segments, researchers gain deeper, more reliable insights into consumer behavior and public opinion.

What do you think? Are you ready to embrace field augmentation to make your market research more efficient, insightful, and inclusive, ensuring every voice is heard?

This is our mission here at Fairgen: to unlock the full potential of insights with AI-augmented research.