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Estimating the Gains (and Losses) of Revenue Management

Author

Listed:
  • DHaultfoeuille, Xavier

    (CREST-ENSAE)

  • Wang, Ao

    (University of Warwick and CAGE)

  • Fevrier, Philippe

    (CREST)

  • Wilner, Lionel

    (CREST)

Abstract

Despite the wide adoption of revenue management in many industries such as airline, railway, and hospitality, there is still scarce empirical evidence on the gains or losses of such strategies compared to uniform pricing or fully flexible strategies. We quantify such gains and losses and identify their underlying sources in the context of French railway transportation. The identification of demand is complicated by censoring and the absence of exogenous price variations. We develop an original identification strategy combining temporal variations in relative prices, consumers’ rationality and weak optimality conditions on the firm’s pricing strategy. Our results suggest similar or better performance of the actual revenue management compared to optimal uniform pricing, but also substantial losses of up to 16.2% compared to the optimal pricing strategy. We also highlight the key role of revenue management in acquiring information when demand is uncertain.

Suggested Citation

  • DHaultfoeuille, Xavier & Wang, Ao & Fevrier, Philippe & Wilner, Lionel, 2022. "Estimating the Gains (and Losses) of Revenue Management," CAGE Online Working Paper Series 621, Competitive Advantage in the Global Economy (CAGE).
    • Handle: RePEc:cge:wacage:621
    as

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    File URL: https://warwick.ac.uk/fac/soc/economics/research/centres/cage/manage/publications/wp621.2022.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Revenue management ; dynamic pricing ; demand estimation ; demand learning ; moment inequalities JEL Codes: C25 ; C61 ; D12 ; R40;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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