Trend forecasting is broken. Join us to learn how to fix it.
For a decade, brands have chased the same lagging indicators, like sales data, search volumes, retail analytics, social virality — and ended up looking identical.
Big data tells you what everyone else already knows. By definition, it’s a sameness engine. And the obvious AI shortcut makes it worse: large language models are trained on the rear-view mirror and statistically revert to the mean, which is the opposite of where real trends live.
This session lays out a different approach, built on the scientific method: detect with big data, validate with thick data, test with synthetic data.
This session lays out a different approach, built on the scientific method: detect with big data, validate with thick data, test with synthetic data.
But these changes must be underpinned by a shift in wider mentality; one in which trends should be treated as hypotheses to test, not statistical events to report.
Register here to join Francesco D'Orazio's (
@abc3d) session:
thesilab.com/resource/the-sa…