Revenue & Ancillary Optimization
Optimize pricing, demand forecasting and ancillary revenue with models tuned to traveler behavior and loyalty.
Solution Overview
The Challenge
Fares are set on rules and competitor matching that respond slowly to demand shifts, so seats are sold too cheaply in strong demand and too dearly in weak.
- 1Static pricing rules responding slowly to demand shifts by route and season
- 2Seats underpriced in strong demand and overpriced in weak
- 3Ancillary offers presented identically regardless of traveler type or trip purpose
Our Approach
CloudGate builds demand forecasting and dynamic pricing models on Azure Machine Learning at route, season and booking-curve level, and personalizes ancillary offers by traveler behavior, trip purpose and loyalty status.
- Demand forecasting by route, season and position on the booking curve
- Dynamic pricing recommendations responding to measured demand
- Personalized ancillary offers by traveler behavior, trip purpose and loyalty tier
Typical Outcomes
Typical outcomes for this solution pattern, not the results of a named client engagement.
Technologies Used
More Success Stories
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