Pricer’s Points: Navigating Big Data Analytics – “What if’s” in Dynamic Pricing | Tom Bacon

Dynamic pricing is highly complex – and sometimes not intuitive.  A significant increase in observed demand for a period may not actually drive a significant increase in forecast demand.  And, many times even a large increase in forecast demand for a higher fare has little or no impact on the seats set aside for that demand.  Certainly, such results are examples of why airline dynamic pricing (“Revenue management”) is often called a “black box.”

Airlines could highlight all of the model assumptions, equations and coefficients.  But even then it is not likely to be clear what the model is doing.  Rather than a listing of “assumptions” or “regression model results”, analysts can benefit from “what if’s”, or more fundamental understanding of the implications of those assumptions, regression models and optimization routines.

To evaluate macro strategies – the use of O&D revenue management vs. leg-based and for the implications of matching competitive fares, for example — the airline industry may consult a simulation tool used by Boeing and MIT researchers called PODS (“Passenger Origin-Destination Simulation”).  This simulation tool has been used to measure the value of RM in general (4-6%) and the extra value of O&D RM (2-3%).  It has also shown that matching a competitor’s fares – ignoring a sophisticated RM model – is revenue negative.  PODS is useful for testing various RM models and enhancements.

But airlines would benefit in their understanding of their systems if they could apply such “what if” to more micro, airline- or market-specific factors.

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