Reduce friction when analyzing your workforce

Use predictive synthetic data to gain efficiency and scalability when analyzing global employee bases
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Map quantitative inputs
Identify sub-segments
Assess boost coverage
Implement new workflows

Rely on predictive data to reduce sample sizes

Significantly reduce your sample requirements when collecting data by applying predictive augmentation


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


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


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


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

Boosting employee research to get faster and more granular results

Fairgen's synthetic niche dataset performed better than 2x across segments, questions, and answers — and it was trained on only 7% of the ground truth.
Could Fairgen reliably augment all underrepresented segments?
Yes, Fairgen could reliably augment all 13 niche segments (+2x) using predictive synthetic data.

We validated with a large employee satisfaction survey dataset of 4,270 rows. Fairgen was trained on a fraction of it (1,000 rows) and the rest served as ground truth..

The augmented dataset presented a very low MAE, right below 3%, 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 only at one sub-segment of interest, in this case "employees with over 5 years of employment at the company", all questions maintained small distances to the ground truth (under 3%), much better than 2x.
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 reduce friction when analyzing large, distributed worforces.

Insights team decided to reduce weighting variables across all ongoing employee trackers, trusting Fairgen to support their niche analysis needs.

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.