Three inputs — Influencer consensus, consumer signal, benchmark position — compiled into a single confidence-scored, benchmarked recommendation.
The core of Bubblin is a multi-signal aggregation model. It takes three distinct data streams and fuses them into a single Demand Score for each SKU.
Raw demand scores don't mean much without context. The Benchmark Engine compares each SKU against a catalog of past validations in the same category, price tier, and style cluster.
This tells you not just "is this product liked?" but "is this product liked relative to others that succeeded or failed in production?" — a fundamentally different, and more useful, question.
Every Demand Report is structured for machine consumption, not just human reading. All scores, signals, and metadata are available via the Demand API for downstream processing.
As the dataset grows across pilots and categories, pattern recognition improves. SKUs in a growing category score against a richer benchmark. Predictions get sharper over time.
Over time, validated SKUs, categories, KOL picks, and shopper preferences form a connected graph. Products that score similarly, get picked by similar KOLs, and appeal to similar shoppers cluster together.
This graph becomes the foundation for the Demand Neighborhood — the peer context shown in every Demand Report. It's what makes each new pilot smarter than the last.
Bubblin connects to the tools brands already use. Data flows in to improve prediction context; verdicts flow out to inform production decisions.