Agility
The flexibility to explore new angles post-analysis, to reveal additional insights and refine your decisions.
DataBoost AI
Our goal
Complement existing approaches to give sub-segments greater statistical robustness, without replacing the field but amplifying it.
How does it work?
01
The key idea
Transform small sub-samples into robust bases using statistical levers.
02
The mechanisms
Between-column correlations: the model learns the joint structure between study variables
Row correlations: the model is trained to generate coherent rows of data at the individual level
Segment-conditional imputation: the model learns the logic of the complete dataset to enrich the small sub-segment
03
Statistical reasoning
The larger the sample, the closer its estimates are to reality (law of large numbers). The work of Stanford statistician Emmanuel Candès shows that, even with partially correlated samples, a larger sample is almost always more likely to be accurate than a smaller one. DataBoostAI applies this principle: it generates coherent synthetic respondents, thereby increasing the effective size of the sub-segments.
This effective size is measured by the Effective Sample Size (ESS): on average, DataBoostAI multiplies the ESS by 3.
In concrete terms, a sub-segment of 80 respondents can be boosted to the equivalent of 240 reliable respondents, without additional fieldwork.
Got a project in mind?
Feel free to share it with us.
Need more info?
If you prefer, we can discuss it.
Got a project in mind?
Feel free to share it with us.
Need more info?
If you prefer, we can discuss it.
Share your brief
If you don’t have one, we can build it together
A good brief includes your problem, your target audience, the themes you want to explore and other key elements.
Precision and confidence with agility
Robustness
Reliable results, even on very small sub-segments, to guarantee the robustness of your analyses.
Transparency
A validated protocol, measured results and systematic human control for total clarity.
Precision
Reduced margins of error and more stable conclusions for more reliable decision-making.
A real game changer for exploring small brands and boosting our portfolio.
Frank Brezout
Global Consumer & Market Insights Director, L’Oréal Brand
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