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

15 minute break every 45 minutes. 45 minute periods. SOHO Day 32 © Origin World Labs

Day 3 Objectives Hour 1-2  Capacity Control Hour 3-4  Dynamic Pricing  Micro-Optimization SOHO Day 33 © Origin World Labs

OWL’s vision for The Big RM Reset Clerical RM Distribute the Right Rates and Manage Inventory. 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. © Origin World Labs SOHO Day 34

Data Science Elements © Origin World Labs SOHO Day 3 5 Data ScienceAnalytics RMToolsDisciplines PrescriptiveOptimizationExcel, SolverEconomics, Operations Research PredictiveClassification and Analysis Excel and SQLProbability, Statistics DescriptiveData Extraction and Grouping MSQuery, SQLArithmetic

Period Level Dynamic Pricing © Origin World Labs SOHO Day 36

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. © Origin World Labs SOHO Day 37

Standard Deviation Want to know how spread out the data points are. © Origin World Labs SOHO Day 38 STDEV.S(data set) Start with the average to measure how far data spreads out.

Standard Deviation of Rooms Sold by Period and Rate Class © Origin World Labs SOHO Day 39

Frequency Actual vs. Normal © Origin World Labs SOHO Day 310 Normal Actual

Normal Frequency in Excel Given an average and a standard deviation, you can get the probability that any # of rooms will be sold using. © Origin World Labs SOHO Day 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)

Expected Value If the scenario plays out many times. © Origin World Labs SOHO Day 312 Reward x Chance of Reward = Rational, Long term Expected Value (Law of Very Large Numbers) Core Assumption of all Decision Sciences The Blue Pill

Lottery – Tax on people that don’t know math. Powerball odds 1/173,000,000 = % chance of winning. © Origin World Labs SOHO Day 313 Costs $2 to play ($150MM - $2) * % = $.86 - $2 * % = - $2 -$1.14Rational Expectation

Heuristic – Rule of Thumb Easy to calculate and implement. Used for practical applications. Based on experience. Not guaranteed to be optimal. Common Sense. © Origin World Labs SOHO Day 314

Capacity Control Pricing Rule P 1 > P 2 > P 3 © Origin World Labs SOHO Day 315 Class 1 is the highest priced class. Switch to higher class when Expected value is equal or higher.

Capacity Control Algorithms EMSRB Littlewood’s Rule Dynamic Programming © Origin World Labs SOHO Day 316

Micro-Segmented Dynamic Pricing SOHODAY3b.xlsx © Origin World Labs SOHO Day 317 Period Room Type Channel Company Rate PMS Dimensions Accuracy

A Better Demand Curve Remove Outliers © Origin World Labs SOHO Day 318 Avg Gross Rate High Limit Rate Low Limit Rate

Dynamic Pricing Analytic Tables – SOHODAY3.xlsx Frequency Tables Shows the average number of times a rate was sold per day per period. Std Deviation Tables Allows us to calculate the upper and lower limit rates for analyzing the demand curve. © Origin World Labs SOHO Day 319

SQL Statistical Functions 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 © Origin World Labs SOHO Day 320 Column being analyzed goes inside ()