Foresight and innovation in
the global hotel industry

DO'S and DON'TS of Hotel Revenue Management Systems

Professor of Revenue Management, Griffith University
Research Assistant Professor, Hong Kong PolyU
Basak Denizci Guillet darkBasak Denizci Guillet lightIbrahim Mohammed darkIbrahim Mohammed light

Effective revenue management is essential for optimising profitability and maintaining a competitive edge in the hospitality industry. Central to this effort is using Revenue Management Systems (RMS) properly. When configured and utilised correctly, these systems can significantly enhance efficiency and a hotel's revenues. However, maximising the benefits of RMS requires adherence to best practices and avoiding common pitfalls.

This guide outlines the key DO'S and DON'TS of using hotel RMS. It was compiled from 20 in-depth interviews with revenue management experts and the extensive experience of these two professors with RMS.

Following these guidelines will help you leverage your data and RMS to their fullest potential, ensuring that they support your revenue goals and operational efficiency. By understanding these critical aspects, you can maintain a robust and effective revenue management strategy that aligns with your hotel's overall business objectives.


  1. Ensure that the RMS configuration and the implementation are done correctly. These critical steps involve setting up the system to accurately reflect the hotel's inventory, pricing structures, and business rules.
  2. Attend regular client-service meetings with your RMS provider to discuss and explain issues of concern. Regular meetings with your RMS provider are essential for maintaining a strong partnership and ensuring the system is tailored to meet your hotel's needs.
  3. Understand which variables are used for unconstrained demand forecasting. Understanding these variables allows you to better evaluate and interpret the RMS's recommendations and make appropriate adjustments.
  4. Set clear guidelines and criteria for when overrides are permissible. Specify whether the override should be an input override (adjusting forecasting variables) or an output override (adjusting final recommendations). Additionally, decide on the frequency (how many overrides are allowed within a specified time) and the magnitude (percentage of increase or decrease) of the overrides.
  5. Recognize that humans are prone to biases and consciously try to avoid or minimise them. This can be done by ensuring that override decisions are always grounded in objective information and thorough analysis rather than unsupported intuition or assumptions. a.k.a. gut feeling.
  6. Regularly monitor the impact of overrides on revenue and occupancy. Overrides can be necessary in certain situations, but their impact on key performance indicators should be carefully monitored.
  7. Always document the reasons for each override to maintain transparency and accountability. This best practice can lead to more informed and effective revenue management decisions.
  8. Train the revenue management team on how to implement and manage overrides properly. The training should emphasise how the team can perform overrides without disrupting the system’s learning. Therefore, the system must be recognised as part of the team.
  9. Know that in normal times, an 80:20 rule of no overrides to overrides is acceptable. A system reconfiguration may be necessary if the override rate is consistently above 20%.
  10. Understand that overreliance on the system's recommendations solely takes away the art of revenue management. So, it is important to remember that the system does not have the heart to determine guests' feelings and the long-term impact of the decisions on satisfaction and loyalty.


  1. Override the RMS's recommendations without valid reasons. Overriding these recommendations without valid reasons can undermine the system's effectiveness and lead to suboptimal decisions. Valid reasons for overrides might include unforeseen events, data anomalies and strategic decisions.
  2. Override the RMS’s recommendation frequently, as this can reduce its predictive accuracy and effectiveness. RMSs are designed to optimise pricing and inventory decisions based on comprehensive data analysis and algorithms.
  3. Forget to document each override and its rationale to maintain transparency and accountability in revenue management. This practice ensures a clear record of why decisions were made, which can be invaluable for future reference and analysis.
  4. Bypass established procedures and company guidelines for overrides. These procedures ensure that overrides are implemented thoughtfully and based on sound reasoning.
  5. Avoid making bold override decisions, but be mindful not to overestimate your ability to make correct decisions. The system is there to help and not replace or compete with you. Hence, cooperate and collaborate with it.
  6. Accept RMS recommendations hook, line, and sinker. Even when the system’s recommendations align with your expectations, it pays to play the devil’s advocate and ask yourself, what if you and the system are wrong?
  7. Be overly influenced by the outcomes of your previous override decisions to make current decisions unless the conditions are the same and related. Always remember that current and past decisions may look similar but are independent.
  8. Overlook competition, but learn to move beyond anchoring your decisions to only the competitive set and trust the system.
  9. Ignore internal and external stakeholder pressures on human-system interactions - they are real. Do not be fazed by them unless they are data-driven and factual!
  10. Note that no overrides are as concerning as too many overrides. Hence, do not prevent overrides or set rigid rules to stop them. This could lead to dissatisfaction and lack of proactivity.


  • Algorithms: A set of computational (mathematical and statistical) formulae or rules revenue management systems use to make recommendations or decisions.
  • Biases: Inclination for or against a system's recommendation that may not be accurate or sound.
  • Data Anomalies: Incorrect or outdated data that may have affected the RMS's calculations.
  • Forecasting Variables: Factors used as inputs to make informed estimates or predictions of the total demand for hotel rooms. These variables help in understanding the true market demand.
  • Input Override: Adjusting the underlying variables or inputs in the forecasting model, such as modifying demand assumptions or correcting data inaccuracies.
  • Output Override: Altering the final recommendations produced by the system, such as changing the suggested room rates or inventory controls.
  • Revenue Management System: A computer application or software for revenue management.
  • Strategic Decisions: Specific business strategies or goals that the RMS might not fully account for.
  • Unconstrained Demand Forecasting: The process of predicting the total demand for a hotel without considering any limitations or restrictions that might affect the supply.