Instant visibility into small segments

Use predictive synthetic data to gain the efficiency needed to analyze an increasingly diverse electorate.
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Map quantitative inputs
Identify sub-segments
Assess boost coverage
Implement new workflows

Gain granularity with predictive intelligence

Draw actionable insights from every segment, regardless of sample size.


Use reliable generative AI technology that is trained on your data


Reduce sample sizes needed to run an quantitative survey


Works with any workflow, whether in-house, agency or digital panels


Analyze segments once considered too hard or slow-to-reach
Technical validation

Predicting voter preference ahead of UK's general elections

Fairgen's synthetic niche dataset performed better than 2x across segments, questions, and answers — and it was trained on only 6.6% of the ground truth.
Could Fairgen reliably augment all underrepresented segments?
Yes, Fairgen could reliably augment all 18 niche segments by at least 2x using predictive synthetic data.

In order to conclude that, we've used a public available survey of 18,000 rows investigating political preferences of UK residents. Fairgen was trained on a small fraction of it (1,000 rows), and the sub-segments of interest were analyzed.

The median was very low, below 2%, and the quartiles were close together, showing that Fairgen could remove edge cases. It performed better than a real sample twice as big as the trained one, showing that Fairgen could effectively predict and augment each niche by at least 2x.
What results did Fairgen provide when zooming in on one sub-segment of interest?
When looking at 1 under-represented niche “Green Party electorate”, Fairgen could reliably augment all 161 questions while maintaining low median (right above 2%) and quartiles close together.
What if we zoom in on answers of one specific question?
No problem! We also compared the performance of the augmented dataset on one specific question, of one specific sub-segment.

Here we see that Fairgen could successfully predict, being much closed to the ground truth than the real sample.
What is the impact for political teams?
Political research teams can expect to gain faster granular insights from their quantitative efforts.

It is clear that synthetic data can be used to reduce data collection while still meeting weighting variables, especially when the segments of interest are increasingly niched.

The impact is faster and more granular insights, that can drive quick deicision-making.

Interested in new use cases?

Fairgen Labs is your ideal partner to co-build products that will modernize research practices. Our team of AI experts are currently working with leading research teams globally.