Case Study: Fortune 200 Hospitality Company achieves 13-second price refreshes and 50% lower hardware costs with Neo4j

Fortune 200 Hospitality Company Uses Neo4j to Fight Off Airbnb and Booking.com

A Fortune 200 hospitality company with more than 6,000 hotels worldwide faced mounting pressure from data-driven competitors like Airbnb and Booking.com and needed a faster, more flexible pricing strategy for its 1M+ rooms. Its legacy Oracle system couldn’t handle the hundreds of millions of daily rate updates required by the High Performance Pricing engine, causing minutes- or even hours-long delays (notably during the 2014 New York Super Bowl) that blocked timely price changes across properties.

The company piloted and then deployed the Neo4j graph database to power its pricing recommendations engine, cutting average price-refresh time from over 4 minutes to about 13 seconds (99% of publishes within 22 seconds). Faster performance led hotels to increase price changes 300% (from 650K to 1.7M daily), drove measurable business growth, improved adherence tracking to recommended prices, and reduced hardware costs by roughly 50%, while enabling a roadmap toward real-time pricing.