Using Data Science in Pricing
summary
Uklon describes using an ensemble of machine learning models to set ride prices that balance supply and demand in real time across locations. The approach integrates forecasts of order placement, weather, traffic, and the current driver supply to choose prices acceptable to riders and attractive to drivers.
AI-written summary · about
related talks
33mA new way to profit: Predictive analytics and dynamic pricing
30mReal-Time ML services: From ideation to production
13mAI-Driven Task Pricing: Reducing Costs in Micromobility Operations
31mScaling up Lyft’s Growth via Causal ML and Bandits
16mUsing AI to solve complex business problems: optimizing...
27mAI-Powered Pricing: Smarter Decisions, Bigger Profits
49mBuilding Machine Learning Pipeline Using Aerosolve
45mKeynote: Seeking Order amidst Chaos and Uncertainty