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Real-Time, Algorithmic Pricing Keeps U.S.'s Largest Bus Transportation Provider Moving

This passenger transportation leader upgrades from static, mileage-based price lists to real-time, dynamic pricing that powers their modern commerce strategy.


Outdated pricing processes created a competitive disadvantage that left money on the table for this bus transportation provider.

This passenger coach service’s pricing processes relied on price lists and manual updates. When the competition published spontaneous sales, they struggled to respond effectively with competitive fares. Pricing teams had to limit their oversight to heavy traffic routes, such as Boston to New York, and apply simplistic mileage-based pricing to the rest of their network. Every delayed price response created an incentive for customers to choose the competition.


A technology-driven approach to pricing enables dynamic responses to every price request based on realtime market and competitive information.

The company turned to PROS to power the intelligence for their digital transformation. To deliver each real-time price, they leverage PROS dynamic pricing science to predict the customer willingness to-pay for each route and then overlay business rules that incorporate real-time competitive pricing, loyalty information, promotions, time of booking, time of travel and other transaction-specific variables.


With dynamic pricing science, this transportation provider can now effectively provide strategic, competitive fares across their entire network. 

With PROS, this iconic passenger transport provider has been able to replace static price lists with real-time, algorithmic responses for every price request. During an average 12-hour period, the PROS solution provides real-time prices for over 26 million price items with sub-second response time. With their pricing process revolutionized by machine learning, they can now deliver the frictionless modern commerce experience customers demand with the responsiveness in pricing they need to be competitive.