Science of Hotel OptimizationRooms Revenue Workshop

Day 1: Data

Day 2: Analysis

Day 3: Optimization

SOHO Day 3 2

15 minute break every 45 minutes.

45 minute periods.

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http://www.forsmarthotels.com/sohodocs

SOHO Day 3 3

Day 3 Objectives

Hour 1-2Capacity Control

Hour 3-4Dynamic PricingMicro-Optimization

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SOHO Day 3 4

OWL’s vision for The Big RM Reset

Clerical RMDistribute the Right Rates and Manage Inventory.

Analytical RMTo take data, to be able to understand it, to process it, to extract value from it, to visualize it and to communicate it. 

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SOHO Day 3

Data Science Elements

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Data Science Analytics RM Tools Disciplines

Prescriptive Optimization Excel, Solver Economics, Operations Research

Predictive Classification and Analysis

Excel and SQL Probability, Statistics

Descriptive Data Extraction and Grouping

MSQuery, SQL Arithmetic

SOHO Day 3 6

Period Level Dynamic Pricing

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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.

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SOHO Day 3 8

Standard Deviation

Want to know how spread out the data points are.

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STDEV.S(data set)

Start with the average to measure how far data spreads out.

SOHO Day 3 9

Standard Deviation of Rooms Sold by Period and Rate Class

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SOHO Day 3 10

Frequency Actual vs. Normal

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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.

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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.

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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.

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Costs $2 to play

($150MM - $2) * .000000578% = $.86

- $2 * .9999994% = - $2

-$1.14Rational Expectation

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.

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SOHO Day 3 15

Capacity Control Pricing Rule

P1 > P2 > P3

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Class 1 is the highest priced class.

Switch to higher class when Expected value is

equal or higher.

SOHO Day 3 16

Capacity Control Algorithms

• EMSRB

• Littlewood’s Rule

• Dynamic Programming

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SOHO Day 3 17

Micro-Segmented Dynamic Pricing

SOHODAY3b.xlsx

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Period

Room Type

Channel

Company

Rate

PMS Dimensions

Accuracy

SOHO Day 3 18

A Better Demand Curve

Remove Outliers

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Avg Gross Rate

High LimitRate

LowLimitRate

SOHO Day 3 19

Dynamic Pricing Analytic Tables – SOHODAY3.xlsx

Frequency TablesShows the average number of times a rate was sold per day per period.

Std Deviation TablesAllows us to calculate the upper and lower limit rates for analyzing the demand curve.

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SOHO Day 3 20

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

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Column being analyzed goes inside ()