Streamline brand insights into niche markets

Use predictive synthetic data to gain insights from every niche of interest, with a fraction of the quantitative data you already collect.
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Map quantitative inputs
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Identify target niches
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Assess boost coverage
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Implement new workflows

Understanding niche segments at scale

Forget about high margins of error when looking at small samples

Smart

Use reliable predictive intelligence that is trained and monitored on your data

Economic

Reduce sample sizes needed to run your ongoing consumer trackers and surveys

Flexible

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

Powerful

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

How a dating app could reduce data collection in half when looking at sub-segments

Fairgen's synthetic niche dataset performed better than 2x across segments, questions, and answers — and it was trained on only 24% of the ground truth.
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Could Fairgen reliably augment all underrepresented segments?
Yes, Fairgen could reliably augment 20 niche segments (+2x) using predictive synthetic data.

In order to conclude that, the client provided a large dataset as the ground truth. Fairgen was then trained on a fraction of it (1,000 rows), and the niche segments were analyzed.

When looking at graph below, the MAE of Fairgen's synthetic dataset was very low, at 2.13%. Additionally, 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.
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What results did Fairgen provide when zooming in on one sub-segment of interest?
When looking only at one sub-segment of interest, in this case users "seeking a committed romantic relationships", all questions maintained small distances to the ground truth, slightly better than 2x.
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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 business impact?
It was clear that synthetic panels, as long as trained on real data, can be a permanent solution to cut costs and accelerate time-to-insights.

It resulted in a 40% reduction in traditional data collection, saving a six-figure sum from the research budget.

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.
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