1. The Secrets To Hotel Demand Forecasting
Even though revenue management Is a broad topic the focus of the discussion will be price optimization.
The Secrets To Hotel Demand Forecasting
WEDNESDAY, MAY 27th - 9:00AM (PDT) Duetto Educational Series

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3. About: Nathaniel “Nat” Estis Green
Formerly on initial CS team, managed APAC & EMEA sales at Duetto
IHG RM internship got me into revenue management
Senior Global Solutions Engineer
Duetto family member since Dec. 2012

4. Agenda What is Forecasting? Why Forecast?
Types of Forecasting (Const. v Unconst., seasonal impacts, etc.)
What are “rolling” forecasts
Why Forecast?
Pricing (Group v non-group business)
Staffing
Product Inventory
Development work – maintenance schedules
Performance evaluations: incentive structures
Do macro and micro trends impact forecasts?
Lost Business
Air Travel
Review Data
How do you evaluate forecast accuracy?
MSE
MSPE
MAD
MAPE
Budgeting
Daily & Monthly
The infamous “budget season”
Questions
What is Forecasting?
Why Forecast?
Do macro and micro trends impact forecasts?
How do you evaluate forecast accuracy?
Budgeting
Questions

5. 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.”
Perishable inventory optimization
Influences operations (housekeeping), S&M, finance, etc.
rental cars, airlines, hotels, stadiums (tickets)
Inventory /
Capacity
Demand $
Price
Cross, R. (1997) Revenue Management: Hard-Core Tactics for Market Domination. New York, NY: Broadway Books.

6. Ever leave money on the table?
250,000 + People

7. Hotel 123
100 room hotel
Does not forecast

8. Hotel ABC 100 room hotel Has YoY Reservation data Tracks STLY Pricing
Has additional data sources

9. Ever leave money on the table???
250,000 + People

10. Ever leave money on the table?
Hotel sells out in advanced
Hotel did not anticipate such heavy demand and has to scramble regarding staffing & product inventory needs
Nevin’s 3 Major Detriments:
No foresight onto possible revenue, no idea of occupancy
bad from ops standpoint
Bad planning for budget
Lack of Revenue optimization and expense minimization
$250
ADR
Hotel 123 ??
250,000 + People

11. Ever leave money on the table?
Hotel sells out day-of with significantly higher rates
Anticipated staffing & product inventory needs
Can properly yield and plan
$350
ADR
Hotel ABC ??
250,000 + People

12. Industry at a Crossroads
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
1970s
1980s
1990s
2000s
2010s
2013

13. Historically Travelers Booked Directly with Stay Brands
Leading Hotels of the World
Historically Travelers Booked Directly with Stay Brands
Previously travelers booked directly with hotels - the stay brands.
Hotels competed with each other for the consumer and owned the relationship
There has been a bifurcation of travel brands and now there is a new class of brands in travel…
Consumer
Stay Brands
Cindy Estis Green
Courtesy

14. Booking Brands Now Dominate Consumer Point of Entry
Leading Hotels of the World
Booking Brands Now Dominate Consumer Point of Entry
If not forecasting you cannot allocate right resources to S&M and other departments you can lose huge volumes of profit
New class = booking brands
The booking brands have become the dominant point of entry for the consumers
Consumers pass through these brands before they decide where to stay
And these booking brands are companies with massive power and high market caps – like Google, Expedia, Facebook.
Hotels are paying to get traffic from the booking brands & while still paying to compete with the other stay brands on guest experience
Competing with a new class of brands with larger budgets is why costs have risen so much
In fact we looked at how much the costs have gone up…
Booking Brands
Consumer
Stay Brands
Cindy Estis Green v
Courtesy

15. Commissions Rise at 2x the Rate of Revenue Growth
39%+
Retail commissions only
Source: HAMA Study
Commission
Increase
24%
20%
20% %
Increase
20%
Total Acquisition Costs
Room Revenue
Courtesy
Sales & Marketing Expense
Total Revenue
2009
2010
2011
2012

16. Customer Acquisition Comparative Costs as % of Revenue
3-6%
4-6%
15-25%

17. What is Forecasting? Getting started.
Managing booking curves and setting appropriate prices
2. Managing distribution channels
What is Forecasting?
Getting started.

18. Forecasting Demand controlled by hotel capacity
Demand if capacity is not a factor
Takeaways unconstrained:
Should be first thing you look at on a day-to-day basis; look at constrained dates
If you believe the forecast is real then you need to track pace and raise rates; yield up discounts and room type differential pricing
S&M can leverage promotion dates to fill high need-days – RM shouldn’t be scared to ask for assistance; most markets have low periods where S&M is vital to optimize RM’s performance
Takeaways constrained:
Staffing
How can you have a different constrained and unconstrained forecast?
Unconstrained just shows over 100% demand – pricing should be set to have both hit 100% (or to slow or speed pace to hit sell-out or overbooking level)
Capacity: room available at your property
Compression: If you have 100 room hotel you should still know total requests (300); you should know about compression in the market
Takeaways with Forecasting:
At heart of RM; if you know of a sellout of not your pricing will be much easier to execute: price optimization is getting forecast right
Rolling Forecasts: Define per group, transient, corporate
(In the month for the month, 90 day forecast –every week or two-, full year forecasts – past 5 or 6 months forecast to budget)
Group
Transient
Corporate
Constrained
Forecasts
Unconstrained
Forecasts

19. Basic Terminology STLY Rolling Forecasts Compression
Forecast-to-Budget
Variance
Over-forecast
Under-forecast
Forecast Incentives
Segmentation
Rolling
Forecasts
Variance
Compression
Rolling Forecast ( )– forecast that updates each day or week following previous time periods pace and pick-up. Generally update the 30, 60, and 90 day forecasts.
Compression – higher demand than capacity
Forecast-to-Budget – trying to hit budget numbers. See what ADR or Occ needs to be to hit revenue forecasts. If you’re behind budget and you are forecasting rates or occupancy to hit that budget – can be seen as a scrambling move; generally good to forecast without looking at budget to always keep a realistic perspective.
Variance – comparison of data between time periods
STLY – year over year; look at how you’ve improved on an annual basis
Week-over-week – where you were last week for a given stay date and where you are this week for that same stay date; is there large variance? Why?
To Budget – how far ahead or behind of budget are you?
Forecast Accuracy – Best RMs hit 3% margin of error (90 DBA, 2 60 DBA, 1 30 DBA) for Mean Simple Error
Over – forecast too much occupancy or revenue; over promise under deliver. Overstaff and other expenses could run high while income will be low.
Under – forecast too low for occupancy or revenue
Segmentation
Group
Leisure
Corporate
Booking Window – time period which the first booking comes in on a stay date until the night audit of the stay date. Different segments have different booking windows. If you have a longer booking window you will need to forecast longer out. Vegas is short term and most forecasts are 90 days out – 90% of bookings come 0-90 DBA – and will want to do 30, 60, 90 day forecasts weekly. Resort Properties would want to forecast 6 months weekly – due to international bookings and very early bookings: at least 50% of bookings 90+ DBA.
DBA - Days before arrival
Occupancy
Hard (with OOO) – occupancy % looking at rooms sold divided by total room count (with OOO rooms)
Soft (subtract OOO) – occupancy % looking at room sold divided by rooms available to sell (total room count minus OOO rooms)
Etc.
To-Be-Booked (TBB) – forecasted rooms left to be sold (can be seem total or per segment)
On-The-Books (OTB) – current rooms sold (can be total or per segment)
Out-of-Order (OOO) – rooms out of order
Commit (Group Block) – rooms committed or OTB for group blocks
Forecast-to-
Budget
Occupancy
Forecast Accuracy
Booking Window
Segmentation
Etc.

20. Why Forecast? See the cross-departmental impact.
Managing booking curves and setting appropriate prices
2. Managing distribution channels
Why Forecast?
See the cross-departmental impact.

21. 5 Key Reasons to Forecast
Pricing (Group v non-group business)
Staffing
Product Inventory
Development work – maintenance schedules; renovations
Performance evaluations: incentive structures
Ops – number of sell out days
RM – RevPAR increase
S&M – group rooms sold, total rev. to hotel
Pricing
Product
Inventory
Development
Work
Performance Evaluations
Staffing

22. Trends in Forecasting Evaluating macro and micro trends.
Are people looking at true metrics?
What are good forecast accuracy metrics – based on DBA? Is there one?
Trends in Forecasting
Evaluating macro and micro trends.

23. Big Data = Better Data Reviews & Social Media
Web Shopping Regrets & Denials
Competitor Pricing Data
Weather
Traditional Revenue Management
Booking & Reservation Data
Air Traffic
Traditional Revenue Management

24. Big Data = Better Data Reviews & Social Media
Review Data: good to look at for a “thumbs up” or “down” approach. Can impact forecasts if heavy negative buzz in social spheres; can also be the opposite if stronger performance exists. E.g. Bed Bugs, or other serious social buzz could impact forecasts.
Specific points that you may go up or down per week wont have direct impact to property; cant link to specific customer segments.
Competitor pricing: if you are priced wrong in the market you can impact your demand in the market which then impacts your forecast
Reviews & Social Media
Web Shopping Regrets & Denials
Competitor Pricing Data
Weather
Traditional Revenue Management
Booking & Reservation Data
Air Traffic
Traditional Revenue Management

25. Big Data = Better Data Reviews & Social Media
Macro trends help give market-level insight: air travel and weather can be huge indicators for city and regional markets
Web site activity gives huge indication into unconstrained market demand
Reviews & Social Media
Web Shopping Regrets & Denials
Competitor Pricing Data
Weather
Traditional Revenue Management
Booking & Reservation Data
Air Traffic
Traditional Revenue Management

26. Web Site (IBE) & Air Activity
Be proactive, not reactive, with demand trends.
You can look at pace going to your booking engine; Unconstrained demand: cite EDC example to say that you could have seen demand before people purchased to appropriately raise prices
Forward looking information – can’t just get this from Google analytics
Actual bookings are just “tip of the iceberg”: understanding pace and pick-up through lost business gives much better insight into anticipating compressed days
ties to rate code, room type, stay dates, search location (location demand)
Review search date, stay dates, rate code, room type, rate, source, and country
Understand high-demand periods before you sell-out supply

27. Excel v.s Revenue Strategy Systems?
wont hire a full time person to aggregate these data sources but having a system helps aggregate that data
Excel

28. How to Determine Forecast Accuracy?
Evaluate your forecast properly.

29. Four Major Statistics Forecast Accuracy
Mean Simple Percent Error (MSPE)
Simple Error
Forecast Accuracy
Mean Absolute Percent Error (MAPE)
Mean Absolute Deviation (MAD)

30. 100 Room Property Hotel ABC
Let’s go back to the example from the beginning and take Hotel ABC…
100 Room Property
Hotel ABC

31. Four Major Statistics Forecast Accuracy
Example MSE
Mean Simple Percent Error (MSPE)
Simple Error
Forecast Accuracy
Mean Aboslute Percent Error (MAPE)
Mean Absolute Deviation (MAD)

32. Simple Error Example
April Simple Error = Sum (April 6, April 13, April 20, April 27)
April Monday Simple Error = -2+3+(-2)+4= +3
DBA 10
Monday, April 6 -2
Monday, April 13 +3
Monday, April 20
Monday, April 27 +4
April Monday Simple Error
Under-forecast the 6 and 20; over forecast the 13 and 27 = over forecast as shown through simple error

33. Four Major Statistics Forecast Accuracy
Example MSPE
Mean Simple Percent Error (MSPE)
Simple Error
Forecast Accuracy
Mean Absolute Percent Error (MAPE)
Mean Absolute Deviation (MAD)

34. 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%
DBA
10 (Simple Error)
10 (Simple Error %)
Monday, April 6 -2
-2%
Monday, April 13 +3
+3%
Monday, April 20
Monday, April 27 +4
+4%
Monday April Error
Under-forecast the 6 and 20; over forecast the 13 and 27 = over forecast as shown through simple error

35. Four Major Statistics Forecast Accuracy
Example of MAD
Mean Simple Percent Error (MSPE)
Simple Error
Forecast Accuracy
Mean Absolute Percent Error (MAPE)
Mean Absolute Deviation (MAD)

36. Mean Absolute Deviation (MAD)
April MAD= Absolute Sum (April 6, April 13, April 20, April 27)
April Monday MAD= (|-2|+|3|+|-2|+|4|)= 11
DBA 10
Monday, April 6
|-2| -> 2
Monday, April 13
|+3| -> 3
Monday, April 20
Monday, April 27
|+4| -> 4
Monday April MAD 11
Absolute values of each number then find the average; this is the more accurate means of evaluating forecast accuracy

37. Four Major Statistics Forecast Accuracy
Example MAPE
Mean Simple Percent Error (MSPE)
Simple Error
Forecast Accuracy
Mean Absolute Percent Error (MAPE)
Mean Absolute Deviation (MAD)

38. 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%
DBA
10 (MAD)
10 (MAPE)
Monday, April 6
|-2| -> 2 2%
Monday, April 13
|+3| -> 3 3%
Monday, April 20
Monday, April 27
|+4| -> 4 4%
Monday Accuracy 11
11%

39. 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%
DBA
10 (MAD)
10 (MAPE)
Monday, April 6
|-2| -> 2 2%
Monday, April 13
|+3| -> 3 3%
Monday, April 20
Monday, April 27
|+4| -> 4 4%
Monday Accuracy 11
11%
*Note – there is an 8% difference between the Simple Error % and the MAPE

40. Best Practices in Budgeting
Managing booking curves and setting appropriate prices
2. Managing distribution channels
Best Practices in Budgeting
Be efficient, effective, and thorough.

41. Efficient Budgeting: What’s Best?
Type of Budgeting:
Daily: granular detail into micro performance of your property; used in various circumstances though mostly with smaller properties (under 75 rooms)
Monthly and Quarterly: how are you doing each month and quarter to budget. What are expenses and expected returns: what is the health of your business?
Budget Season:
What is it?
Better RMs with pulse on city (is there a new terminal at the airport being built? What’s going on in the city? – Should practically be on the “city planning committee” to best optimize budgeting.
Why it’s important:
Any executive will care about budgeting and planning around the annual budget. Hold employees to budget and performance expectations for the property/hotel company.
Need to understand trends: why are you doing above or below budget? 1 2 3
Daily
Monthly
Quarterly

42. Key Takeaways
Things to think about per type of property.

43. Key Takeaways Economy Luxury Resorts Casino City-Center Airport
For all: Understand your market placement and how pricing impacts demand around that. If you are pricing yourself out of your comp set you will impact your forecast. Happy medium between raising rates and improving occ. to hit the forecasted revenues.
Economy hotel – simple forecasting due to minimal segmentation. Fewer data sets required due to simplified distribution landscape.
Luxury & Resort – detailed forecasting looking at various variances including macro data sets. Know your booking window and also market happenings. Understand seasonality if it persists (looking at Quarterly YoY performance)
City Center & Convention – like luxury though need to be very well versed in market happenings – especially conference and events. Macro trends play a huge impact into understanding city-wide market performance.
Airport – very last minute short booking window. Minimal forecasting other than anticipating yieldable demand (mostly walk-in) around heavy corporate business. Macro trends (air travel and weather) can help immensely with forecasting
Casino – need to understand balance between property RM, S&M, and casino marketing. Need to be on the same page with forecasts so there can be an aligned casino revenue strategy across the boards.
Economy
Luxury
Resorts
Casino
City-Center
Airport
Convention

44. WEDNESDAY, May 27th - 9:00AM (PDT) Duetto Educational Series
Even though revenue management Is a broad topic the focus of the discussion will be price optimization.
Questions?
WEDNESDAY, May 27th - 9:00AM (PDT) Duetto Educational Series