The Secrets To Hotel Demand Forecasting WEDNESDAY MAY

The Secrets To Hotel Demand Forecasting WEDNESDAY, MAY 27 th - 9: 00 AM (PDT) Duetto Educational Series

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About: Nathaniel “Nat” Estis Green Senior Global Solutions Engineer Duetto 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. Inventory / Capacity 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. Demand $ Price

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

Hotel 123 100 room hotel Does not forecast 7

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

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

Ever leave money on the table? Hotel 123 $250 ADR ? ? 250, 000 + People

Ever leave money on the table? ? ? Hotel ABC $350 ADR 250, 000 + People

Industry at a Crossroads Separation of ownership, brand, and management 1970 s First online Product segmentation; booking; enter Expedia financial engineering 1980 s 1990 s Online distribution explodes complexity 2000 s Crowded value chain 2010 s Meta search; enter tech giants & new gatekeepers 2013 12

Historically Travelers Booked Directly with Stay Brands Consumer Stay Brands Courtesy 13

Booking Brands Now Dominate Consumer Point of Entry Consumer Stay Brands v Booking Brands Courtesy 14

Commissions Rise at 2 x the Rate of Revenue Growth 39%+ Retail commissions only Source: HAMA Study 2013 -2014 Commission Increase % 4 2 20% 20% % Increase Total Acquisition Costs Room Revenue Courtesy 2009 2010 2011 Sales & Marketing Expense Total Revenue 2012 15

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

What is Forecasting? Getting started.

Forecasting Demand controlled by hotel capacity Constrained Forecasts Demand if capacity is not a factor Unconstrained Forecasts

Basic Terminology Variance Rolling Forecasts Compression Forecast-to. Budget Occupancy Forecast Accuracy Segmentation Booking Window Etc. 19

Why Forecast? See the cross-departmental impact.

5 Key Reasons to Forecast Pricing Staffing Product Inventory Development Work Performance Evaluations 21

Trends in Forecasting Evaluating macro and micro trends.

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

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

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

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 sellout supply

Excel v. s Revenue Strategy Systems? VS Excel 27

How to Determine Forecast Accuracy? Evaluate your forecast properly.

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

100 Room Property Hotel ABC 30

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

Simple Error Example DBA Monday, April 6 Monday, April 13 Monday, April 20 Monday, April 27 April Monday Simple Error 10 -2 ▍ April Simple Error = Sum (April 6, April 13, April 20, April 27) ▍ April Monday Simple Error = -2+3+(-2)+4= +3 +3 -2 +4 +3 32

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

Simple Error Percent Example DBA 10 (Simple Error) Monday, -2 April 6 Monday, +3 April 13 Monday, -2 April 20 Monday, +4 April 27 Monday +3 April Error 10 (Simple Error %) -2% ▍ Simple Error % = Simple Error/ Room Count ▍ Simple Error % = Simple Error/ 100 -2% ▍ April Simple Error % = Sum (April 6, April 13, April 20, April 27) +4% ▍ April Monday Simple Error % = -2%+3%+(-2%)+4%= +3% +3% 34

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

Mean Absolute Deviation (MAD) DBA Monday, April 6 Monday, April 13 Monday, April 20 Monday, April 27 Monday April MAD 10 |-2| -> 2 |+3| -> 3 ▍ April MAD= Absolute Sum (April 6, April 13, April 20, April 27) ▍ April Monday MAD= (|-2|+|3|+|-2|+|4|)= 11 |-2| -> 2 |+4| -> 4 11 36

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

Mean Absolute Percent Error (MAPE) Example DBA 10 (MAD) Monday, April 6 Monday, April 13 Monday, April 20 Monday, April 27 Monday Accuracy |-2| -> 2 10 (MAPE) 2% |+3| -> 3 3% |-2| -> 2 2% |+4| -> 4 4% 11 11% ▍ ▍ ▍ 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

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

Best Practices in Budgeting Be efficient, effective, and thorough.

Efficient Budgeting: What’s Best? 1 Daily 2 Monthly 3 Quarterly 41

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

Key Takeaways Economy City-Center Luxury Airport Resorts Convention Casino 43

Questions? WEDNESDAY, May 27 th - 9: 00 AM (PDT) Duetto Educational Series