Assessing the Impact: Does an Improvement to a Revenue Management System\n Lead to an Improved Revenue?
Bibliographic record
Abstract
Airlines and other industries have been making use of sophisticated Revenue\nManagement Systems to maximize revenue for decades. While improving the\ndifferent components of these systems has been the focus of numerous studies,\nestimating the impact of such improvements on the revenue has been overlooked\nin the literature despite its practical importance. Indeed, quantifying the\nbenefit of a change in a system serves as support for investment decisions.\nThis is a challenging problem as it corresponds to the difference between the\ngenerated value and the value that would have been generated keeping the system\nas before. The latter is not observable. Moreover, the expected impact can be\nsmall in relative value. In this paper, we cast the problem as counterfactual\nprediction of unobserved revenue. The impact on revenue is then the difference\nbetween the observed and the estimated revenue. The originality of this work\nlies in the innovative application of econometric methods proposed for\nmacroeconomic applications to a new problem setting. Broadly applicable, the\napproach benefits from only requiring revenue data observed for\norigin-destination pairs in the network of the airline at each day, before and\nafter a change in the system is applied. We report results using real\nlarge-scale data from Air Canada. We compare a deep neural network\ncounterfactual predictions model with econometric models. They achieve\nrespectively 1% and 1.1% of error on the counterfactual revenue predictions,\nand allow to accurately estimate small impacts (in the order of 2%).\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".