Choi Sunmee and Kimes Sheryl E.
Cornell Hotel and Restaurant Administration Quaterly
On my previous articles I haven’t emphasize the importance and effects of distribution channels in revenue management. Therefore, following article will shed some light on it. Moreover, this article was written 13 years ago, therefore it will be extremely interesting to see what has been changed and whether the authors predictions come true in real life.
Additionally, it is worthy to note that in 2002, Priceline (one of three biggest online travel agents) had a loss of $19 millions. 9 years later, Priceline achieved $1.1 billion profit under the legendary command of Jeffrey Boyd.
In this article authors want to extend the commonly used forecasting and optimization models used in revenue management to “consider the effects of distribution channels and used to computer simulation to test the performance of the extended models.” Some hotels use daily occupancy forecasts to determine rate availability while others develop multiple arrival forecast for combinations of rate, length of stay and room type. On the other hand, inventory allocation methods (like EMR – expected marginal revenue) are used to determine what is the most profitable mix of demand for given capacity. Sophisticated revenue management systems make allocation decisions based on updated demand forecast and overbooking decisions. However, as revenue management systems don’t take into consideration current market decisions, competitors practices or any unusual incidents, Revenue Managers have to adjust the system recommendations each day. After the adjustment, allocation decisions are then communicated to reservation system at property and/or central level, which in turn feeds the GDS routes through a switch system where appropriate. The forecasts mentioned before can be either created at required level of detail (like rate category, length of stay, room type, etc) or at aggregated level such as overall hotel level and then disaggregate for details using historical probability distribution. The first approach have shown reduced forecast error. (Weatherford, Kimes, Scott, 2001)
Traditionally, hotels had three booking channels: hotel direct, central reservation offices and travel agencies. However, since the internet become such widely used medium, two additional channels fastly become most popular: hotel’s web sites and various online travel agencies. Moreover, the variable cost of a booking through different channels can vary form almost nothing to as much as $35 and more. Typically there are up to 4 systems used to process bookings made through those channels: global distribution system (GDS), switch system, central reservation system (CSR) and property management system (PMS). The consistency in rates in all those systems is crucial for successfully managing revenue.
In order to compare the existing models with their extended version, authors simulated hotel with 300 similar rooms, 3 rates and 3 booking channels to simplify calculations. The bookings followed usual for business hotels pattern and there was no overbooking. The system simulated hotel’s reservation booking environment and the functions of revenue management systems. A total of 100 simulation runs were made for each bid-price and forecast method. The summary of this simulation is presented below.
For simulated hotel, optimizing by rate, length of stay and distribution channel didn’t enhance the results used by traditional method. When it comes to forecasting, better result was achieved when using pick up method directly at rate, length of stay and distribution channel than using traditional approach. This result also supports previous study made by Weatherford, Kimes and Scott (2001). However, the most important finding is that “applying revenue-management strategies to distribution channels may not help a hotel that already is optimizing revenues by rate and length of stay“.
Main article: Choi, Sunmee, and Sheryl E. Kimes. ‘Electronic Distribution Channels’ Effect On Hotel Revenue Management’. The Cornell Hotel and Restaurant Administration Quarterly 43.3 (2002): 23-31.
- Weatherford, Lawrence R., Sheryl E. Kimes, and Darren A. Scott. ‘Forecasting For Hotel Revenue Management: Testing Aggregation Against Disaggregation’. The Cornell Hotel and Restaurant Administration Quarterly 42.4 (2001): 53-64.
Author: Mateusz Konopelski