Predictive pricing, demand modeling, and operational intelligence across 100+ global markets.
From a single location in SoHo to hundreds of buildings across six continents in under a decade.
Every location had thousands of desks at different price points. Every pricing decision across every market was being made manually.
Each system replaced a manual process with a predictive one.
We built ML models that ingested location data, local market conditions, member behavior, and seasonal patterns to generate optimal rates for every product type in every building.
The challenge was building a pricing model for a product category that did not exist before.
30% revenue lift · Dynamic pricing across every product type
We built predictive demand models that could forecast occupancy curves for locations that did not exist yet.
The most valuable prediction was not “will this building fill up?” It was “how fast?”
Pre-opening demand forecasting · Location scoring · Product mix optimization
We built churn prediction models that identified at-risk members weeks before they gave notice.
The breakthrough was connecting prediction to action — a pricing adjustment, a space upgrade — automatically.
Early-warning churn detection · Automated retention triggers
We built optimization models that continuously recalculated the ideal product mix for every floor of every building.
Revenue per sq ft optimization · Dynamic product mix
We built the operational intelligence layer that gave leadership real-time visibility into performance across every market.
The hardest problem in global operations is not data. It is context.
Real-time global visibility · Context-aware anomaly detection
“They reinvented where people work. We built the intelligence that made it scale — from one neighborhood to every continent.”
Scaled from $886M through ML-powered operations
From predictive pricing and demand optimization
Running on predictive systems, not spreadsheets
$886M to $3.3B in revenue growth