science of hotel optimization rooms revenue workshop

Science of Hotel OptimizationRooms Revenue Workshop

Day 1: Data

Day 2: Analysis

Day 3: Optimization

SOHO Day 1 2

10 minute break every 50 minutes.

50 minute periods.

© Origin World Labs

http://www.forsmarthotels.com/sohodocs

http://www.forsmarthotels.com/sohodocs/

SOHO Day 1 3

Ask every question.

Dozens of Books, Lectures, Courses in 4 hrs.

Contribute examples.

Give me a Pace.

I will go back.


SOHO Day 1 4

Day 1 Objectives

Hour 1 The Analytic RM Hotel Data Science Databases & Data Access

Hour 2 PMS Databases MS Query Tool

Hour 3 Opera Data Tables OLAP Tools (Pivot Table) SQL Queries

Hour 4 SQL Queries Excel Criteria Functions

My Notes


SOHO Day 1 5

What is Analytics?


My Notes

• Not Reporting.

• Misused and Abused.e.g. “Luxury”

Applying mathematics to data to extract insights that

lead to better decisions.

SOHO Day 1 6

The ‘M’ Word


SOHO Day 1 7

The Analytic RM

• The Talent Shortage

• 50% shortage in analytic positions by 2018.

• The Hotel RM experience.

• Analytic maturity.

• 1-2% use RMS.

• Black box algorithms.

• Era of Rate Distribution ROI is over.

• Incremental value of RM.

My Notes


SOHO Day 1 8

Who would you pick to do your taxes?

Bookkeeper


Accountant

SOHO Day 1 9

The Analytic RM


Clerical RM Analytic RM

Rate Distribution Analysis

Manual Reporting Modeling Decisions

Comp Set focused Patterns focused

Rooms Revenue Total Profit

STR Report Strategy ROI

My Notes

SOHO Day 1 10

The Revenue Manager of 2023 Skillset


Analytics Toolbox Choice & DemandData & Granularity Probability Statistics

Drivers Forecasts Optimization Modeling Guest Behavior

Market Biases Rhythm of Business

Communication

SOHO Day 1 11

The Hotel Data Scientist

• Harvard Business Review.

• Many disciplines.

• Broad skill set.

• Not from hospitality.

• Ability to think “nerd” and speak “business”.

• One foot in IT and one foot in the boardroom.

• Extracts profit from data.

• Start in RM.


My Notes

“This Workshop is your launch point.”

SOHO Day 1 12

Data Science Pyramid


Optimization

Analysis & Classification

Data Extraction Day 1

Day 2

Day 3

SOHO Data Science

Descriptive

Predictive

Prescriptive

SOHO Day 1 13

Your new world view

• Data in its native environment.

• At the databases level

• Think “The Matrix”.

• Put your “tech” hat on.


My Notes

SOHO Day 1 14

Databases

• A set of spreadsheet-like tables.• Multiple tables for efficiency.• Works hidden behind transaction

software.

My Notes


SOHO Day 1 15

Relational


Client

Database

Reservation

Reservation Entry Occupancy Graph

Guest Profile Room Types OOOs

SOHO Day 1 16

Simple PMS Schema


Reservation

Reserv ID

Arrival

Departure

Guest ID

Room Type

Guest Profile

Guest ID

Name

Address

Email

Phone

NightlyDate

Rate

Reserv Date

Source

Reserv ID

Unique keys connect each record(row).

You must reconnect these tables to get all the data.

SOHO Day 1 17

Databases: Important Definitions


My Notes

Table: related data grid.

Schema: Organization of tables

Fact : Transactione.g. Rate Paid one night

Dimension : Explains Facte.g. Source, Room Type

Granularity : Level of detaile.g. POS has ticket detail

Columns: Field

Data Dictionary: Explains tables & columns.Warehouse: An organized copy.

SOHO Day 1 18

PMS Database Vendors


PMS DatabaseOpera OracleAgilysis Microsoft

RoomMaster Sybase

Two licenses

SOHO Day 1 19

Opera Tables: Reservations History

RESERVATION_DAILY_ELEMENTSOrganized by Night

RESERVATION_DAILY_ELEMENTS_NAMEOrganized by Reservation


My Notes

Sample companyUpper Creek Resort100 Rooms

SOHO Day 1 20

ODBC

• Open Database Connection.

• “Like rubbing the lamp.”

• Access data via ODBC client.

• Driver available from each database vendor (not PMS) for FREE.

• Usually installed by IT.

• Oracle ODBC client atInstructions at sohodocs.


My Notes

SOHO Day 1 21

Four Data Table Analysis tools

1. Query Wizard – MS Query

2. OLAP – Picot Table

3. SQL

4. Excel – Multi-Criterion Functions


SOHO Day 1 22

MSQUERY

• The most common operation for a business analyst is the query.

• MSQuery is the simplest way to query a database.

• Built into Excel.

• Database agnostic. Vendor Neutral.

• Creates a Real-time link to data.

My Notes


Data > From Other Sources > From Microsoft Query

SOHO Day 1 23

MSQUERY


My Notes

Connect

Update

Refresh

SOHO Day 1 24

Query Wizard


Select Table Select Columns

Filter ReturnSort

SOHO Day 1 25

MS Query power tools

• Criteria > Add Criteria

• Table > Add Tables

• Drag and Drop Join


My Notes

SOHO Day 1 26

Excel Table

• Behaves like a database table.• For related data.• Header Row• Filters• Sorting• Calculated Columns• Formatting• Insert/Delete• Structured references

Instead of C2[@[RESV_DAILY_EL_SEQ]]


My Notes

SOHO Day 1 27

Pivot Table

• OLAP Tool

• Fast way to investigate data.

• Multi-dimensional

• Multi-perspectives

• Drill-down

• Slicing

more at sohodocs


My Notes

Insert > Pivot Table

SOHO Day 1 28

Pivot Table

What you are measuring• Values

The Dimensions• Columns• Rows

Slice of the data• Filters

Tips• Start with the end in mind• Creative Thinking


My Notes

SOHO Day 1 29

SQL: Standard Query Language

• Common Language for Database

• A very specific way to ask a database a question.

• Universal ANSI standard.

• We will use the query part.

• DML Data manipulation language.

• “The coding” is sensitive.


My Notes

SOHO Day 1 30

Types of SQL

Developed to connect data source to a programming language.Slight variations in code.

PMS Database SQLOpera Oracle PL/SQLAgilysis Microsoft T-SQL

RoomMaster Sybase T-SQL


My Notes

SOHO Day 1 31

SELECT

• To select the columns to return.

• What is to be returned.

• “*” represents all


My Notes

SELECT column_name,column_name

SOHO Day 1 32

FROM

• Defines the Table(s) to be used

• Always required.

[Database].[Table]


My Notes

SELECT column_name,column_nameFROM table_name;

SELECT RESV_NAME_IDFROM `C:\SOHODAY1.xlsx`.`Element$`

SOHO Day 1 33

WHERE

• Your filters• Use operators

– =, >, >=, <, <=– Between– In (list)

• Multiple Conditions joined by “and”


My Notes

SELECT column_name,column_nameFROM table_nameWHERE column_name operator value;

SELECT *FROM `C:\SOHODAY1.xlsx`.`Element$` WHERE (RESERVATION_DATE>{ts '2012-01-01 00:00:00'})

SOHO Day 1 34

JOINS

SELECT *FROM `C:\SOHODAY1.xlsx`.Èlement_Name$` `C:\SOHODAY1.xlsx`.Èlements$` WHERE Èlements$`.RESV_DAILY_EL_SEQ = Èlement_Name$`.RESV_DAILY_EL_SEQ


My Notes

SOHO Day 1 35

GROUP BY

• Group Functions• Common Arithmetic

Count, Sum, Max, Min, Avg


My Notes

SELECT RESERVATION_DATE, SUM(RATE_AMOUNT), SUM(QUANTITY)

FROM `C:\SOHODAY1.xlsx`.`Element$` WHERE (RESERVATION_DATE>{ts '2012-01-01 00:00:00'})GROUP BY RESERVATION_DATE

SOHO Day 1 36

ALIASES

• Renames a column in query

• Careful not to use database or SQL words


My Notes

SELECT RESERVATION_DATE STAY, SUM(RATE_AMOUNT) REVENUE, SUM(QUANTITY) NIGHTS

FROM `C:\SOHODAY1.xlsx`.`Element$` WHERE (RESERVATION_DATE>{ts '2012-01-01 00:00:00'})GROUP BY RESERVATION_DATE

SOHO Day 1 37

Excel Vlookup

• Go get data according to a match

• Searches first column.

• VLOOKUP( DATA TO MATCH, DATA GRID, COLUMN TO SEARCH, TYPE OF MATCH)


My Notes

SOHO Day 1 38

Excel Multiple Criteria Functions

• COUNTIFS– COUNTIFS(Column, Criteria,Column2, Criteria2,…)

• SUMIFS– SUMIFS(Column to Sum,Column,Criteria1,…)

• AVERAGEIFS

Criteria filter – “operator”&e.g. “>=“&25


My Notes

SOHO Day 1 39

What we covered?

The New RM

Data

Databases

Tables

Columns

Dimensions

MSQuery

Pivot Table

SQL

Excel

My Notes


SOHO Day 1 40

Next Friday: Analysis

ProbabilityVarianceExpected ValueDemand CurvesForecasting


science of hotel optimization rooms revenue workshop

Documents

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analysis day

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analytic rmsoho day

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