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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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

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

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.

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

Hotel 123 100 room hotel Does not forecast

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? 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

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

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

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 cindy@kalibrilabs.com Courtesy

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 cindy@kalibrilabs.com v Courtesy

Commissions Rise at 2x the Rate of Revenue Growth 39%+ Retail commissions only Source: HAMA Study 2013-2014 Commission Increase 24% 20% 20% % Increase 20% Total Acquisition Costs Room Revenue Courtesy Sales & Marketing Expense Total Revenue 2009 2010 2011 2012

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

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

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

Basic Terminology STLY Rolling Forecasts Compression Forecast-to-Budget Variance Over-forecast Under-forecast Forecast Incentives Segmentation Rolling Forecasts Variance Compression Rolling Forecast (30-60-90)– 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.

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.

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

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.

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

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

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

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

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

How to Determine Forecast Accuracy? Evaluate your forecast properly.

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

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

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)

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

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)

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

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)

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

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)

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%

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

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

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

Key Takeaways Things to think about per type of property.

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

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