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Intelligent Aggressiveness: Using Forecast Multipliers, Hybrid Forecasting, Fare Adjustment, and Unconstraining Methods to Increase Revenue *
Author(s) -
Weatherford Larry R.
Publication year - 2017
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12228
Subject(s) - allowance (engineering) , revenue , lever , competitor analysis , computer science , operations research , microeconomics , econometrics , economics , business , marketing , operations management , finance , mathematics , engineering , mechanical engineering
Many studies have begun the exploration of airlines using intelligent aggressiveness (IA) in unidimensional directions (e.g., forecast multipliers alone). This article uses the sophisticated passenger origin–destination simulator (PODS) to examine the revenue impact of four different IA levers—forecast multipliers, unconstraining, hybrid forecasting (HF) and fare adjustment (FA). We also explore the impacts in two different origin–destination networks. Due to the competitive nature of PODS (two or four airlines competing) and its allowance for customer choice, we are able to assess all the implications, including the impact of spill, upgrades and recapture. We find that with a single IA lever, independent of the network and demand level, in a more‐restricted fare environment, the optimal lever is almost always HF with moderate‐to‐aggressive estimates of willingness‐to‐pay, with revenue gains of 0.4–4.3% in a large global network, and gains of 1.7–4.2% in a domestic network, depending on demand level and optimization method used. We also test two additional, less‐restricted fare environments and find that revenue improvements have a wider range (0.8–6.3%) with a single lever in the larger network. Finally, we explore the impacts of allowing the competitors to use basic IA and the airline of interest to use multiple IA levers.