How Marketplaces Are Building Their Own Forecasting Engines

Episode 48 of The Platform Economy. Lucas and Luna examine how large online marketplaces are moving beyond third-party analytics to build proprietary demand forecasting engines. The episode centers on a specific case: a major ride-hailing platform that developed a system to predict supply-demand imbalances 90 minutes ahead with a claimed 15 percent improvement in driver allocation accuracy. The conversation explores why generic forecasting tools fail for network effects businesses, the data architecture needed to make real-time predictions work, and the strategic implications for smaller platforms that lack the resources to build their own. Topics include the shift from reactive to proactive marketplace management, the integration of weather and event data into supply models, and the risk of over-reliance on machine learning predictions. Recorded June 12, 2026. #MarketplaceForecasting #DemandPrediction #PlatformEconomy #NetworkEffects #MachineLearning #RealTimeData #RideHailing #SupplyDemand #PredictiveAnalytics #DataArchitecture #OperationsResearch #MarketplaceTech #Business #Technology #FexingoBusiness #BusinessPodcast #LucasAndLuna #PlatformStrategy Keep every episode free: buymeacoffee.com/fexingo