Home Revenue Management Revenue Management Revenue Management for the Hospitality Industry Part II

Revenue Management for the Hospitality Industry Part II

Apr 10, 2010  By 


How does it all work?

Market Segment Identification:
The first and foremost step in a hotel RM system is the identification of the various market segments for the hotel room, followed by implementation of a differential pricing scheme. The objective in front of the hotel is the expansion of its market and in motivating the customer to pay more than he/she will usually spend. It is further observed that customers in the business class segment are less sensitive to higher prices as opposed to those in the vacation segment. An RM system helps hotels create additional price-points by building physical and logical fences around the different market segments, as shown in the table below.

 Untitled

 

Demand Forecasting:
The next step in an RM process is forecasting demand and pricing of the different market segments. Pricing and demand are inter-related and need to be coordinated. In the hotel industry, demand for a room is cyclic in nature (day of a week, months of a year) and follows a trend (demand growth due to economic growth). These forecasts are seldom precise but provide the decision-maker with an approximate set of inputs that are used in the planning process. RM models help pinpoint demand by minimizing uncertainty and producing the best possible forecast.

Allocation:
The next important step in a RM process is the allocation of inventory (hotel rooms) among different market segments. The ratio of discounted versus full priced rooms is not fixed during the reservation period; rather, it is “tweaked” appropriately as the date of stay approaches. The opportunity cost of selling a discounted room instead of a full priced one has to be measured in order to make the best decision. Thus, when a customer approaches the hotel for a discounted price, the manager needs to evaluate this scenario with the expected revenue from another customer who might come at a later date, willing to pay a higher price for the same room. The manager would accept the request only if the discounted price now is more than the expected price at which the room might be booked by the second customer. The key word here is “expected”. RM systems use complicated mathematical algorithms to arrive at this decision using techniques such as Littlewoods and Expectation Maximization, referred to as the EM algorithm.

To explain these techniques, let us consider a simple two class scenario. A hotel has two price categories of rooms, say $60 and $100. Since the pricing is different for the two rooms, these rooms are each targeted at a different customer set. Based on the historical preference pattern of customers in each segment, it would be possible to estimate the number of customers who would be willing to buy these rooms at the given price, with a reasonable “variance”. The term variance refers to a tolerance level. For example, an average 50 customers may be willing to pay $100 for some rooms, but it could also mean that the actual number of customers who turn up for the $100 room could be 60 (or even 40) with some probability, or 80 (or 30) with a lesser probability. In statistical terms, this sort of pattern for the different customer segments is said to mimic a normal distribution.

Using the past data and applying statistical know-how, we can actually estimate an “expectation” of revenue by quantifying the probability of a specific demand value and the actual revenue. In the same example, let us assume that this hotel has 100 rooms, which are similar, but priced at the time of booking. If the booking is done fairly closely to the actual date of stay, the customers may need to pay $100, whereas, they might have paid only $60 had they booked in advance. Remember that, on an average, 50 persons are willing to pay $100 for this room. Obviously, many more than 50 (say, 120) are willing to $60 for the same room. We can use the Littlewoods rule to actually estimate the number of rooms that must be protected for those customers who are willing to pay $100. If we protect too many rooms, some rooms may go vacant thereby resulting in a loss of potential revenue of at least $60 per room. On the other hand if we protect too few rooms for $100 customers, we lose the opportunity of $40 per room on that number of rooms. The Littlewoods rule guides us to arrive at an optimal number of rooms that would maximize the expectation of revenues.

Overbooking:

Overbooking is the practice of intentionally selling more rooms than are available in order to offset the effect of cancellations and no-shows. Studies estimate that although a hotel is fully booked, about 5-8% of the rooms are vacant on any given date. Poor overbooking decisions can prove to be very expensive for the hotel. In the short run, it is only a loss of room revenue, but over the long-term, casualties may include decreased customer loyalty, loss of hotel reputation, etc. American Airlines developed an optimization model that maximizes net revenues associated with overbooking decisions for the airline industry.

 To illustrate the overbooking model developed by the American Airlines, let us consider a B757 jet flying from Chicago to Boston. The aircraft has about 180 seats. Based on the past travel pattern, it is observed that an average of 5% (or nine passengers) do not turn up at the time of boarding the flight. If the airlines book all seats for this leg, it is likely to fly with only 171 occupied seats. However it does not mean that it never flies with 172 or more (even 180) seats occupied. There is a lesser chance of the flight flying with 172 passengers, an even lesser chance of it flying with 175 and a miniscule chance of it flying with all 180 passengers. Therefore, if we book 181 passengers instead of 180, we are likely to end up with only 173 passengers (and almost always with lesser than 180 passengers). In an odd event of exactly 181 passengers reporting, the airline would need to bump one passenger. IATA has defined rules to compensate bumped passengers. If we can quantify all costs (including the cost of lost goodwill), the expected revenue would be the revenue from 181 passengers minus the expected cost of compensating the one additional passenger at that odd chance. Since the probability of exactly 181 passengers turning up is so low, the revenue from that additional passenger generally compensates more than the expected cost. For this example, the optimal number of passengers that can be booked would be 186 as illustrated in the figure below.

 This model can be directly applied to the hotel industry as well. The driving force behind the model is the evaluation of the tradeoff between additional revenue accrued by selling an already-reserved room versus the downside from doing so. It has been found that net revenue increases with overbooking until the point where the downside from overbooking a room exceeds customer revenues. Beyond that point, the negative impact of overbooking increases rapidly because fewer and fewer customers appreciate being turned away.

Challenges
It is quite clear that while an RM system can guarantee increased revenues, it can be quite complicated to design and requires high levels of expertise for implementation. Some of the challenges facing hotels in the implementation of a robust and accurate RM system include:

(1) Measuring performance of an RM system is a major issue. Occupancy rates and yield are measures that are affected by external competition. An ideal measurement can be done using an opportunity model that indicates where the hotel stands in comparison to its maximum and
(2) Differential pricing is here to stay – customers seem resigned to the fact that hotels charge different prices for the same room. However, some customers do not like this practice and penalize the hotel by not becoming a patron. Therefore, in a fiercely competitive environment where quality of service is the key to success, RM may not work. In evaluating the efficiency of a RM system, the tradeoff between generating short-term profits and creating long-term customer loyalty and “mindshare” needs to be studied carefully.
(3) From an operational point of view, RM can impact the motivational level of the employees. In many cases, RM takes much of the guess work out of employees, thereby reducing their decision-making responsibilities. Sometimes, employees taking reservations are paid a percentage of the sales they make, motivating them to make group bookings, which in turn may be contradictory with the objectives of an RM system.

 Conclusion
As part of ongoing changes in the industry, companies throughout the entire hospitality spectrum are placing a strong emphasis on implementing major operational changes. Beyond recognizing that meaningful cost reductions must be achieved without compromising safety, capacity and service levels, they are also looking at reducing costs by increasing flexibility and improving asset utilization through an RM strategy. In doing so, they continue to reassess their true core competencies, and are looking to outsource many of these processes, as they look to optimize business efficiencies and increase profitability

Related Post