Revenue and Pricing Strategies in TravelHoteliers acknowledge that no longer it is sufficient to just submit an expected occupancy total for each day with no segmented detail to assist with decision-making.

Published: 16 Jun 2008

Revenue and Pricing Strategies in Travel

Hoteliers acknowledge that no longer it is sufficient to just submit an expected occupancy total for each day with no segmented detail to assist with decision-making.

At the same time, the industry professionals also feel that the revenue management discipline is undeniably constrained in terms of segmentation and other labelling methods due to limitations in human ability to process all of the information.

According to Brian Berry, Regional Director – RM, Starwood Hotels & Resorts, it is important to recognise that while systems have the ability to drill data down to the most minute, precise and detailed level, there is a threshold where that information becomes trivial and statistically insignificant.

"The key is to find the balance where information presented is both detailed and meaningful. The important interface between revenue managers and data is the presentation of the analysis. For example, is the knowledge that a revenue manager is forecasting 5% occupancy in the Government segment in Superior rooms for a 1-night stay on a Tuesday in September meaningful? Or, is it more important to know that the 30 rooms reflect a 50% increase over the same day last year? I would prefer to know the latter," says Berry.

To ensure the forecast quality - and subsequently the restrictions and pricing decisions proposed by the RM system, hoteliers need to monitor and perform sanity checks on a very regular basis.

On the best way to ensure forecast quality is up to the mark, Berry said most RM systems allow for some level of data treatment and manipulation by the revenue manager and provide opportunities for the user to `override' the system's output.

According to Berry, these two functions are typically where revenue managers are either intimidated by the complexity of the system or over-eager to provide human input.

Data management processes generally rely on the user to identify and assign the most appropriate data set to use for a future period or to `adjust' the data to reflect known or anticipated changes in demand conditions.

"The degree to which a system allows for user adjustments varies widely from highly manual to fully automatic and both have their advantages and disadvantages. The primary goal is to ensure that any RM system is using the right information to produce the most accurate forecast, which results in optimal financial results. The ability to override the system's output (e.g. rates and restrictions) is an important feature that allows users to ensure strategies are logical and appropriate. Common sense application of user knowledge is the key to adding validity and sanity to the process," said Berry.

For managing these two key areas, Berry suggests that to ensure that one fully understands how the system works.

"Revenue management systems are really just very expensive calculators and if you truly understand the logic, methodology, database management and optimisation models then you are better equipped to monitor the system and make the appropriate and necessary adjustments. If you are intimidated by the system or misunderstand the system's rational models, your input could be doing more harm than good," said Berry.