Navigating Big Data Analytics - “What if’s” in Dynamic Pricing

Say you work in an airline’s revenue management department.  And for key flights departing on the past three Fridays, high fare bookings are up 25%, 30%, and 45% respectively over the model forecast.  How many more seats will the model hold for full fare bookings next Friday?

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A Guide to Reporting on Airline Dynamic Pricing

Reporting has been a challenge for providers of airline dynamic pricing (“revenue management”) systems from the initial launch of the systems decades ago. Naturally, dynamic pricing systems initially focused on their proprietary models often hidden behind complex algorithms, and business reporting seemed an afterthought. Somewhat slowly, vendors supplemented their systems with reports and alerts, mostly directed toward “flags” that might trigger analyst interventions:

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Pulling Back the Curtain on Airline Dynamic Pricing

Dynamic pricing has gotten extremely sophisticated – and complicated. For airlines, dynamic pricing relies on a series of algorithms to determine the optimal number of seats to be allocated to different fares or fare classes. The algorithms are generally not completely transparent, and there are literally millions of calculations that are updated nightly to insure allocations remain up-to-date. 

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