Revenue Management without Demand Forecasting: A Data-Driven Approach for Bid Price Generation
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ID: 283437
2023
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Abstract
Traditional revenue management relies on long and stable historical data and
predictable demand patterns. However, meeting those requirements is not always
possible. Many industries face demand volatility on an ongoing basis, an
example would be air cargo which has much shorter booking horizon with highly
variable batch arrivals. Even for passenger airlines where revenue management
(RM) is well-established, reacting to external shocks is a well-known challenge
that requires user monitoring and manual intervention. Moreover, traditional RM
comes with strict data requirements including historical bookings and pricing
even in the absence of any bookings, spanning multiple years. For companies
that have not established a practice in RM, that type of extensive data is
usually not available. We present a data-driven approach to RM which eliminates
the need for demand forecasting and optimization techniques. We develop a
methodology to generate bid prices using historical booking data only. Our
approach is an ex-post greedy heuristic to estimate proxies for marginal
opportunity costs as a function of remaining capacity and time-to-departure
solely based on historical booking data. We utilize a neural network algorithm
to project bid price estimations into the future. We conduct an extensive
simulation study where we measure performance of our methodology compared to
that of an optimally generated bid price using dynamic programming (DP). We
also extend our simulations to measure performance of both data-driven and DP
generated bid prices under the presence of demand misspecification. Our results
show that our data-driven methodology stays near a theoretical optimum (<1%
revenue gap) for a wide-range of settings, whereas DP deviates more
significantly from the optimal as the magnitude of misspecification is
increased. This highlights the robustness of our data-driven approach.
| Reference Key |
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|---|---|
| Authors | Ezgi C. Eren; Zhaoyang Zhang; Jonas Rauch; Ravi Kumar; Royce Kallesen |
| Journal | arXiv |
| Year | 2023 |
| DOI |
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| URL | |
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