The Purpose Of This Assignment Is To Practice Creating And I

The Purpose Of This Assignment Is To Practice Creating And Interpretin

The purpose of this Assignment is to practice creating and interpreting an expected payoff table, which will help a hotel determine how many of its rooms to offer at a discount, and how many to offer at full price. By forecasting the expected revenue at different levels, you can help the hotel maximize its revenue by minimizing its discount rate while maximizing its occupancy rate.

Scenario: You have been approached by a hotel owner who would like you to forecast expected payoffs. The hotel has 100 rooms and aims to maximize revenue by selling as many as possible at full price and the rest at a discounted rate through travel brokers such as Travelocity and Expedia. The market rate is $279 per night, but rooms can be sold for as little as $229 without incurring loss. Selling below $229 causes a financial loss. Empty rooms reduce revenue, so the hotel prefers to discount some rooms rather than leave them vacant, although their priority is to sell as many rooms at full price as possible.

Your task is to help the hotel owner find the optimal combination of full and discounted rooms to maximize revenue. You will perform this in Excel by opening the ExpectedPayoff.xlsx file, filling in the pricing assumptions, creating formulas to calculate expected payoffs for each combination of full and discounted rooms, and analyzing the results to determine the best strategy. Additionally, you will calculate the price of perfect information, the opportunity loss table, and the expected opportunity loss, highlighting the optimal cells with color coding. Finally, you will provide a written explanation of how the expected payoff table can assist the hotel owner, and identify ethical considerations involved in using this data.

Paper For Above instruction

The process of optimizing hotel revenue through expected payoff analysis is a critical component of strategic revenue management. This approach leverages probability, forecasting, and decision analysis to determine the most profitable allocation of room inventory between full-price and discounted sales, especially in fluctuating market conditions. The present analysis applies these principles to a hypothetical scenario involving a hotel with 100 rooms, aiming to maximize nightly revenue by balancing full-rate sales with discounted distributions via online travel agents (OTAs).

First, the hotel’s pricing assumptions are established based on the scenario. The maximum market rate is set at $279, which represents the full-price fare. The discounted rate that still covers costs and minimizes losses is $229. These assumptions form the basis for the payoff calculations. The hotel’s goal is to determine the optimal number of rooms to sell at each price point, considering guest demand probabilities and market fluctuations. By manipulating these variables within Excel, the expected payoff for each potential combination of full-price and discounted sales is computed.

Creating the expected payoff table involves defining cells where hotel managers can input various combinations of full and discounted room sales, subject to the constraints that the total does not exceed 100 rooms. Formulas then calculate the revenue for each scenario by multiplying the number of rooms sold at each rate by the respective prices. For example, if 60 rooms are sold at full price and 40 at a discount, the revenue is (60 x $279) + (40 x $229). The formulas ensure no more than 100 rooms are sold in total, effectively preventing calculation errors. These computations enable visualization of how different sales mixes impact revenue, with the optimal scenario highlighted through color coding.

Next, probability estimates for market demand are incorporated based on the given likelihood percentages. These probabilities allow for the calculation of the expected payoff for each sale combination by multiplying revenues with their corresponding likelihoods and summing these products. This statistical approach accounts for market variability, offering the hotel owner a probabilistic understanding of expected revenues under different strategies. Highlighting the maximum expected payoff in each scenario assists decision-makers in identifying the most lucrative options.

The analysis proceeds with the calculation of the price of perfect information, which indicates the maximum benefit if the hotel could perfectly predict demand. This involves comparing the revenue outcomes of perfect demand knowledge (selling all rooms at full price during the peak demand) with the best expected payoff derived from the forecast model. The resulting figure quantifies the value of market intelligence, guiding investments in demand forecasting tools and data analytics.

Opportunity loss is then examined, representing the difference between optimal revenue and actual revenue realized in each scenario. Establishing an opportunity loss table clarifies potential revenue forfeited when the hotel chooses suboptimal room allocations. Calculating the expected opportunity loss, weighted by demand probabilities, offers insights into the risk associated with each strategic choice. The smallest expected opportunity loss indicates the most resilient strategy under demand uncertainties and is highlighted accordingly.

implementing this decision analysis significantly benefits the hotel owner by providing a robust, data-driven foundation for revenue maximization decisions. It highlights the trade-offs between selling at full price versus discounting, balancing occupancy levels against revenue potential. Importantly, it also illuminates the risks involved and the value of perfect information in demand estimation. This strategic tool ensures more consistent profitability, adaptability to market fluctuations, and enhanced competitiveness in a dynamic hospitality landscape.

However, ethical considerations must be acknowledged when employing such models. A positive ethical issue involves transparency with customers, ensuring discounted rates are offered fairly without misleading advertising. Conversely, a negative concern arises if the hotel owner manipulates demand forecasts or pricing strategies unethically to exploit travelers, such as deceptive discounting practices or hidden fees. Ensuring integrity in data use and honest communication with consumers is essential to sustain trust and uphold professional standards.

References

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