webinar - the secrets to hotel demand forecasting
TRANSCRIPT
The Secrets To Hotel Demand Forecasting
WEDNESDAY, MAY 27th - 9:00AM (PDT) Duetto Educational Series
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About: Nathaniel “Nat” Estis Green
Senior Global Solutions EngineerDuetto family member since Dec. 2012
Agenda
▍ What is Forecasting?
▍ Why Forecast?
▍ Do macro and micro trends impact forecasts?
▍ How do you evaluate forecast accuracy?
▍ Budgeting
▍ Questions
4
Revenue Management Introduction
“The application of disciplined analytics that predict consumer behavior at the micro-market level and optimize product availability and price to maximize revenue growth.
The primary aim of Revenue Management is selling the right product to the right customer at the right time for the right price and with the right pack.
The essence of this discipline is in understanding customers' perception of product value and accurately aligning product prices, placement and availability with each customer segment.”
Cross, R. (1997) Revenue Management: Hard-Core Tactics for Market Domination. New York, NY: Broadway Books.
Inventory /Capacity Demand
Price
$
Ever leave money on the table?
250,000 + People
7
100 room hotel
Does not forecast
Hotel 123
8
Hotel ABC
100 room hotel
Has YoY Reservation data
Tracks STLY Pricing
Has additional data sources
Ever leave money on the table?
250,000 + People
??
Ever leave money on the table?
250,000 + People
??$250ADR
Hotel 123
Ever leave money on the table?
250,000 + People
?? $350ADR
Hotel ABC
Industry at a Crossroads
12
1970s 1980s 1990s 2000s 2010s 2013
Separation of ownership, brand, and management
Product segmentation; financial engineering
First online booking; enter Expedia
Online distribution explodes complexity
Crowded value chain
Meta search; enter tech giants & new gatekeepers
Historically Travelers Booked Directly with Stay Brands
13
Consumer Stay Brands
Courtesy
Booking Brands Now Dominate Consumer Point of Entry
14
Consumer
Stay Brands
v
Booking Brands
Courtesy
Commissions Rise at 2x the Rate of Revenue Growth
39%+
2009 2010 2011 2012
%Increase
CommissionIncrease
15
20%20%24%
20%
Total Acquisition Costs
Room Revenue
Sales & Marketing Expense
Total Revenue
Retail commissions onlySource: HAMA Study 2013-2014
Courtesy
Customer Acquisition Comparative Costs as % of Revenue
16
Revenue
Cost %
3-6% 4-6%
15-25%
What is Forecasting?Getting started.
Forecasting
ConstrainedForecasts
UnconstrainedForecasts
Demand controlled by hotel capacity
Demand if capacity is not a factor
Basic Terminology
19
VarianceRolling
Forecasts Compression
Forecast-to-Budget Occupancy Forecast Accuracy
SegmentationBooking Window
Etc.
Why Forecast?See the cross-departmental impact.
21
5 Key Reasons to Forecast
Staffing ProductInventory
DevelopmentWork
PerformanceEvaluations
Pricing
Trends in ForecastingEvaluating macro and micro trends.
Big Data = Better Data
Reviews & Social Media
Competitor Pricing Data
Booking & Reservation Data
Web Shopping Regrets & Denials
Weather
Air Traffic
Traditional Revenue Management
Traditional Revenue Management
Big Data = Better Data
Reviews & Social Media
Competitor Pricing Data
Booking & Reservation Data
Web Shopping Regrets & Denials
Weather
Air Traffic
Traditional Revenue Management
Traditional Revenue Management
Big Data = Better Data
Reviews & Social Media
Competitor Pricing Data
Booking & Reservation Data
Web Shopping Regrets & Denials
Air Traffic
Traditional Revenue Management
Traditional Revenue Management
Weather
Web Site (IBE) & Air Activity
Be proactive, not reactive, with demand trends.
▍Review search date, stay dates, rate code, room type, rate, source, and country
▍Understand high-demand periods before you sell-out supply
Excel v.s Revenue Strategy Systems?
27
VS
Excel
How to Determine Forecast Accuracy?Evaluate your forecast properly.
Four Major Statistics
29
Forecast Accuracy
Simple Error Mean Simple Percent Error(MSPE)
Mean Absolute Deviation(MAD)
Mean Absolute Percent Error(MAPE)
30
100 Room Property
Hotel ABC
Four Major Statistics
31
Forecast Accuracy
Simple Error Mean Simple Percent Error(MSPE)
Mean Absolute Deviation(MAD)
Mean Aboslute Percent Error(MAPE)
Simple Error Example
▍ April Simple Error = Sum (April 6, April 13, April 20, April 27)
▍ April Monday Simple Error = -2+3+(-2)+4= +3
32
DBA 10Monday, April 6
-2
Monday, April 13
+3
Monday, April 20
-2
Monday, April 27
+4
April Monday Simple Error
+3
Four Major Statistics
33
Forecast Accuracy
Simple Error Mean Simple Percent Error(MSPE)
Mean Absolute Deviation(MAD)
Mean Absolute Percent Error(MAPE)
Simple Error Percent Example
▍ Simple Error % = Simple Error/ Room Count
▍ Simple Error % = Simple Error/ 100
▍ April Simple Error % = Sum (April 6, April 13, April 20, April 27)
▍ April Monday Simple Error % = -2%+3%+(-2%)+4%= +3%
34
DBA 10 (Simple Error)
10 (Simple Error %)
Monday, April 6
-2 -2%
Monday, April 13
+3 +3%
Monday, April 20
-2 -2%
Monday, April 27
+4 +4%
Monday April Error
+3 +3%
Four Major Statistics
35
Forecast Accuracy
Simple Error Mean Simple Percent Error(MSPE)
Mean Absolute Deviation(MAD)
Mean Absolute Percent Error(MAPE)
Mean Absolute Deviation (MAD)
▍ April MAD= Absolute Sum (April 6, April 13, April 20, April 27)
▍ April Monday MAD= (|-2|+|3|+|-2|+|4|)= 11
36
DBA 10Monday, April 6
|-2| -> 2
Monday, April 13
|+3| -> 3
Monday, April 20
|-2| -> 2
Monday, April 27
|+4| -> 4
Monday April MAD
11
Four Major Statistics
37
Forecast Accuracy
Simple Error Mean Simple Percent Error(MSPE)
Mean Absolute Deviation(MAD)
Mean Absolute Percent Error(MAPE)
Mean Absolute Percent Error (MAPE) Example
▍ MAPE = MAD/ Room Count
▍ April MAPE= Sum (April 6 MAD, April 13 MAD, April 20 MAD,
April 27 MAD)
▍ April Monday MAPE= 2%+3%+2%+4%= 11%
38
DBA 10 (MAD) 10 (MAPE)
Monday, April 6
|-2| -> 2 2%
Monday, April 13
|+3| -> 3 3%
Monday, April 20
|-2| -> 2 2%
Monday, April 27
|+4| -> 4 4%
Monday Accuracy
11 11%
Mean Absolute Percent Error (MAPE) Example
▍ MAPE = MAD/ Room Count
▍ April MAPE= Sum (April 6 MAD, April 13 MAD, April 20 MAD,
April 27 MAD)
▍ April Monday MAPE= 2%+3%+2%+4%= 11%
39
DBA 10 (MAD) 10 (MAPE)
Monday, April 6
|-2| -> 2 2%
Monday, April 13
|+3| -> 3 3%
Monday, April 20
|-2| -> 2 2%
Monday, April 27
|+4| -> 4 4%
Monday Accuracy
11 11%
*Note – there is an 8% difference between the Simple Error % and the MAPE
Best Practices in BudgetingBe efficient, effective, and thorough.
Efficient Budgeting: What’s Best?
41
1 2 3
Daily Monthly Quarterly
42
Key TakeawaysThings to think about per type of property.
Key Takeaways
43
Economy Luxury Resorts
City-Center Airport Convention Casino
Questions?WEDNESDAY, May 27th - 9:00AM (PDT) Duetto Educational Series