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Science of Hotel Optimization Rooms Revenue Workshop

Science of Hotel Optimization Rooms Revenue Workshop. Day 1: Data Day 2: Analysis Day 3: Optimization. 45 minute periods. 15 minute break every 45 minutes. http://www.forsmarthotels.com/sohodocs. Day 3 Objectives. Hour 1-2 Capacity Control Hour 3-4 Dynamic Pricing

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Science of Hotel Optimization Rooms Revenue Workshop

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  1. Science of Hotel OptimizationRooms Revenue Workshop Day 1: Data Day 2: Analysis Day 3: Optimization

  2. 45 minute periods. 15 minute break every 45 minutes. http://www.forsmarthotels.com/sohodocs SOHO Day 3

  3. Day 3 Objectives Hour 1-2 • Capacity Control Hour 3-4 • Dynamic Pricing • Micro-Optimization SOHO Day 3

  4. OWL’s vision for The Big RM Reset Clerical RM Analytical RM To take data, to be able to understand it, to process it, to extract value from it, to visualize it and to communicate it. Distribute the Right Rates and Manage Inventory. SOHO Day 3

  5. Data Science Elements SOHO Day 3

  6. Period Level Dynamic Pricing SOHO Day 3

  7. Capacity Control Optimization - SOHODAY3.xlsx • Limitations set on the number of units offered to a rate class. • Prices are provided by the decision maker, not the algorithm. • Assumes RM has good pricing information. • Still used in airline and hotel RMS systems. • Only need to count rooms sold, regardless of rates charged. SOHO Day 3

  8. Standard Deviation Start with the average to measure how far data spreads out. Want to know how spread out the data points are. STDEV.S(data set) SOHO Day 3

  9. Standard Deviation of Rooms Sold by Period and Rate Class SOHO Day 3

  10. Frequency Actual vs. Normal Normal Actual SOHO Day 3

  11. Normal Frequency in Excel Given an average and a standard deviation, you can get the probability that any # of rooms will be sold using. 1 - NORM.DIST(number of rooms, average, standard deviation, TRUE) Given an average and a standard deviation, you can get the # of rooms that will be sold with a certain probability. NORM.INV(specific probability, average, standard deviation) SOHO Day 3

  12. Expected Value If the scenario plays out many times. Reward x Chance of Reward = Rational, Long term Expected Value (Law of Very Large Numbers) Core Assumption of all Decision Sciences The Blue Pill SOHO Day 3

  13. Lottery – Tax on people that don’t know math. Powerball odds 1/173,000,000 = .000000578% chance of winning. Costs $2 to play ($150MM - $2) * .000000578% = $.86 - $2 * .9999994% = - $2 Rational Expectation -$1.14 SOHO Day 3

  14. Heuristic – Rule of Thumb • Easy to calculate and implement. • Used for practical applications. • Based on experience. • Not guaranteed to be optimal. • Common Sense. SOHO Day 3

  15. Capacity Control Pricing Rule Class 1 is the highest priced class. P1 > P2 > P3 Switch to higher class when Expected value is equal or higher. SOHO Day 3

  16. Capacity Control Algorithms • EMSRB • Littlewood’s Rule • Dynamic Programming SOHO Day 3

  17. Micro-Segmented Dynamic Pricing SOHODAY3b.xlsx Period Room Type Channel PMS Dimensions Company Rate Accuracy SOHO Day 3

  18. A Better Demand Curve Remove Outliers High Limit Rate AvgGross Rate Low Limit Rate SOHO Day 3

  19. Dynamic Pricing Analytic Tables – SOHODAY3.xlsx Frequency Tables Std Deviation Tables Allows us to calculate the upper and lower limit rates for analyzing the demand curve. Shows the average number of times a rate was sold per day per period. SOHO Day 3

  20. SQL Statistical Functions Column being analyzed goes inside () COUNT(): returns the population (or sample, depending on the row source) SUM(): returns the sum of the values in a set AVG():returns the mean STDEV():returns the standard deviation of a sample VAR():returns the variance of a sample SOHO Day 3

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