This monthly STAR report provides a summary of hotel performance metrics for Any Hotel in Any City compared to its competitive set and various industry segments. Key metrics such as occupancy, average daily rate (ADR), and revenue per available room (RevPAR) are presented for the current month, year-to-date, and rolling 3-month and 12-month periods. The report also shows supply, demand, and revenue changes. This allows the hotel to benchmark its performance against competitors and industry averages.
The Greek Hospitality newsletter of GBR Consulting provides a snapshot of the performance of Greek hotels based on a sample of more than 180 hotels & resorts in Greece. This hotel data is complemented by data from other sources so as to place the Greek hospitality industry in the perspective of Greek tourism and of the International Hospitality Industry.
This document provides an overview of Smith Travel Research (STR) reports and how to read and use the data they contain. It discusses the history of STR and how they collect hotel data. It explains key STR reports like the competitive set, market share, occupancy index, and definitions of common metrics. It emphasizes using STR data to benchmark performance against competitors and identify opportunities for improved profitability.
The Influence Of Reputation Analytics On Hotel Revenue and Financial PerformanceeCornell
In his landmark study comparing Global Review IndexTM (GRI) scores with ADR, occupancy and RevPAR performance, Cornell’s Chris Anderson proved what intuition told every savvy revenue manager:
A hotel’s online reputation significantly impacts revenue potential.
Chris Anderson, Associate Professor at Cornell University’s School of Hotel Administration and RJ Friedlander, CEO of ReviewPro, shows how hoteliers across all segments of the industry are leveraging online reputation analytics to improve guest satisfaction, revenue and financial performance.
This fast-paced session offers a first-time look at Chris’s most recent research project which explores the relationship between review scores and REIT stock performance. You won’t want to miss listening to Chris discuss the results of this study and the potential game-changing impact the Global Review IndexTM could have on identifying under-/over-valued investments.
Chris Anderson is an associate professor at the Cornell School of Hotel Administration. His main research focus is on revenue management and service pricing. He actively works with industry, across numerous industry types, in the application and development of RM, having worked with a variety of hotels, airlines, rental car and tour companies as well as numerous consumer packaged goods and financial services firms. At the School of Hotel Administration, he teaches courses in revenue management and service operations management.
RJ Friedlander is the founder and CEO of ReviewPro. The company enables hoteliers and restaurateurs to increase guest satisfaction and grow revenue by proactively managing and improving their online reputation. The company’s suite of web-based tools, including the Revenue Optimizer, Advanced Guest Satisfaction Survey solution and Hotel Analysis Reports, provide the analysis, customer intelligence, competitive benchmarking and reporting needed to help hospitality professionals maximize their organization’s performance.
The document discusses how hospitality and tourism industry professionals analyze and view hotel industry data. It provides examples of the types of data analyzed, including performance data, segmentation of data by different hotel groups and time periods, and profitability data. It also gives examples of popular data applications used in the industry, such as for market studies and impact analyses.
The document discusses Review Pro, a company that analyzes online guest reviews and provides metrics like the Global Review Index (GRI) to help hotels track their online reputation and performance. It provides examples of how hotels can use review metrics like the GRI in revenue management decisions to optimize pricing, compare to competitors, and identify opportunities to increase revenue and occupancy. A case study shows how one hotel group used review metrics to inform pricing strategy and improve revenue performance.
This document discusses the importance of smart hoteliers having expertise in data analysis and the role of academia in developing analytical skills. It outlines goals of increasing the quality of hospitality and tourism research and experiential learning. This includes making industry data available to academics and supporting student projects. It also describes certifications available through STR in topics like hotel analytics foundations and conducting feasibility studies to help develop analytical skills for hoteliers.
This document discusses the use of available tourism data for marketing research and policymaking. It defines key terms like marketing and discusses the need for meaningful research before committing resources to new markets. A variety of tourism data sources are described, like entry/departure cards, visitor surveys, and online reviews, which can provide insights into markets, customers, and competitors. Segmentation analyses of key markets like the US and UK are also summarized. The document advocates using demographic and lifestyle data to identify high-value customer groups to target marketing towards.
The Greek Hospitality newsletter of GBR Consulting provides a snapshot of the performance of Greek hotels based on a sample of more than 180 hotels & resorts in Greece. This hotel data is complemented by data from other sources so as to place the Greek hospitality industry in the perspective of Greek tourism and of the International Hospitality Industry.
This document provides an overview of Smith Travel Research (STR) reports and how to read and use the data they contain. It discusses the history of STR and how they collect hotel data. It explains key STR reports like the competitive set, market share, occupancy index, and definitions of common metrics. It emphasizes using STR data to benchmark performance against competitors and identify opportunities for improved profitability.
The Influence Of Reputation Analytics On Hotel Revenue and Financial PerformanceeCornell
In his landmark study comparing Global Review IndexTM (GRI) scores with ADR, occupancy and RevPAR performance, Cornell’s Chris Anderson proved what intuition told every savvy revenue manager:
A hotel’s online reputation significantly impacts revenue potential.
Chris Anderson, Associate Professor at Cornell University’s School of Hotel Administration and RJ Friedlander, CEO of ReviewPro, shows how hoteliers across all segments of the industry are leveraging online reputation analytics to improve guest satisfaction, revenue and financial performance.
This fast-paced session offers a first-time look at Chris’s most recent research project which explores the relationship between review scores and REIT stock performance. You won’t want to miss listening to Chris discuss the results of this study and the potential game-changing impact the Global Review IndexTM could have on identifying under-/over-valued investments.
Chris Anderson is an associate professor at the Cornell School of Hotel Administration. His main research focus is on revenue management and service pricing. He actively works with industry, across numerous industry types, in the application and development of RM, having worked with a variety of hotels, airlines, rental car and tour companies as well as numerous consumer packaged goods and financial services firms. At the School of Hotel Administration, he teaches courses in revenue management and service operations management.
RJ Friedlander is the founder and CEO of ReviewPro. The company enables hoteliers and restaurateurs to increase guest satisfaction and grow revenue by proactively managing and improving their online reputation. The company’s suite of web-based tools, including the Revenue Optimizer, Advanced Guest Satisfaction Survey solution and Hotel Analysis Reports, provide the analysis, customer intelligence, competitive benchmarking and reporting needed to help hospitality professionals maximize their organization’s performance.
The document discusses how hospitality and tourism industry professionals analyze and view hotel industry data. It provides examples of the types of data analyzed, including performance data, segmentation of data by different hotel groups and time periods, and profitability data. It also gives examples of popular data applications used in the industry, such as for market studies and impact analyses.
The document discusses Review Pro, a company that analyzes online guest reviews and provides metrics like the Global Review Index (GRI) to help hotels track their online reputation and performance. It provides examples of how hotels can use review metrics like the GRI in revenue management decisions to optimize pricing, compare to competitors, and identify opportunities to increase revenue and occupancy. A case study shows how one hotel group used review metrics to inform pricing strategy and improve revenue performance.
This document discusses the importance of smart hoteliers having expertise in data analysis and the role of academia in developing analytical skills. It outlines goals of increasing the quality of hospitality and tourism research and experiential learning. This includes making industry data available to academics and supporting student projects. It also describes certifications available through STR in topics like hotel analytics foundations and conducting feasibility studies to help develop analytical skills for hoteliers.
This document discusses the use of available tourism data for marketing research and policymaking. It defines key terms like marketing and discusses the need for meaningful research before committing resources to new markets. A variety of tourism data sources are described, like entry/departure cards, visitor surveys, and online reviews, which can provide insights into markets, customers, and competitors. Segmentation analyses of key markets like the US and UK are also summarized. The document advocates using demographic and lifestyle data to identify high-value customer groups to target marketing towards.
How to increase the revenues of the hotel with Revenue management?Stanislav Ivanov
This document provides an overview of revenue management strategies and techniques that hotels can use to increase revenues. It begins with defining revenue management as optimizing net revenues through offering the right product to the right customers via the right distribution channel at the right time and price. It then covers economic fundamentals, the revenue management process, key metrics, tools like price discrimination and overbooking, channel management, software, and ethical considerations. The goal is to educate on how hotels can systematically use revenue management to maximize profits.
The receptive tour operator (RTO) channel sells over 23 million international hotel room nights annually through wholesalers overseas. RTOs act as intermediaries between US hotels/attractions and international wholesalers, developing products and contracts. They offer a variety of tour packages including escorted groups, fly-drive programs, and MICE tours. While RTOs add value through international distribution and language support, some disadvantages include introducing another layer of costs and requiring rates far in advance. Overall, the RTO channel represents an important part of international tourism sales to the US.
Infor EzRMS is a revenue management solution developed for the hospitality industry that:
- Uses advanced algorithms and business process design to optimize product prices and achieve maximum profits.
- Provides automated demand forecasts and recommendations on selling strategies to sell the right product to the right customer.
- Was designed to help hotels maximize revenue opportunities, occupancy rates, and profit margins through professional revenue management techniques.
Compcruncher Collateral Valuation Reports (CVR). Big data accuracy and reliability. Compare to any valuation in its value class at the price offered. Lenders told us that the CVR is a easy replacement of BPO's, particularly for HELOCs. Replacement for the MSM-Mortgage Servicing Market for their portfolio loans, Default management for any BPOs used in the process (short sales) Alternative in the loan modification programs Quality assurance, Secondary valuation in value dispute resolution and value reconciliation.
Examining a business is important to understand the success or failure. Mathematics and Statistics help uncover all types of ratios, proportions, averages, deviations etc. in all walks of human life. Maths is almost infallible. Statistics, a little less so.
Learning to use maths for a hospitality career begins right in the First year, but the Education Planners wait till the end to expose the students to the real villain in the movie. Profits, onward & upward...
This presentation gives an insight into the hotel industry. This industry has been top rated in terms of search on the internet. Learn a few basics before you start an internet marketing campaign focused majorly around PPC in the hospitality sector.
The basic premise with the Law of Attraction, Law of Vibration, Law of Gratitude, Law of Love, and Law of Allowing, is that when you practice these laws, and stay in harmony with them, you will prosper. You will have abundance. You will have plenty.
There is no denying it. Your thoughts control your actions. Your thoughts dictate what you end up getting from the Universe. If you believe completely that you will receive what you wish for, good things will come your way.
You must accept that which you wish upon. You must be tuned into the universe to get it. You must be in vibration to what you want. You must show the universe you want it by having gratitude for what you have received. You also must show that you are allowing it by being receptive to it and saying “yes” to it when it comes. By doing this, the universe will manifest it to reality and provide you more.
The main point with this book is to draw your attention to the fact that there are universal laws God put in place to help us. He loves us and wants the best for us. By acting in harmony with his will and by obeying his universal laws, you will have plenty. You just have to start the process with a thought, turn that thought in an image, send it to your heart for processing (this turns into emotions and feelings), act on your thoughts, and allow the results to come to you. By doing this you will receive results from your thoughts, whether they are good or bad.
The old saying is, “be careful what you wish for” or “you are your thoughts” holds true here in every respect. Therefore, watch your thoughts if you want the best that life has to offer.
It is hardly true that financial inclusion gaps forced countries around the world to explore the potential of digital financial services and fintech companies allow leapfrogging of traditional brick-and-mortar banking services. As per the study conducted by the World Bank, access to affordable financial services is critical for poverty reduction and economic growth. At the macro level, countries with deeper, more developed financial systems can allocate capital and risks more efficiently and consequently enjoy higher economic growth and larger reductions in poverty and income inequality. At the micro level, financial inclusions—access to and use of basic financial services—can reduce poverty, increase resilience and improve the lives of the poor. Digital financial services bridge the financial inclusion gaps and enhance economic growth. Fueled by the explosive growth of mobile phones, digital financial services (DFS) leverage technology to offer new forms of financial accounts that provide secure options for storing, transferring, and accumulating money. Hence, digital financial services are becoming accessible and affordable to all individuals and businesses through digital financial channels which ultimately boosts financial inclusion.
The contribution of digital financial service in alleviating constraints to financial access is quite immense. The emergence of mobile money, platform eco systems and open application programming interfaces (APIs) uplifted the digital financial service at the global level and impacted the level of financial inclusion. Yet in many emerging economies today, the majority of individuals and small businesses lack access to even basic savings and credit products, which hinders economic growth and perpetuates poverty. Financial exclusion is at the forefront in the list of the challenges which inhibit the growth of many economies around the globe.
Digital financial services enable financial institutions to provide convenient self-service saving and credit products. Traditional saving and lending processes are being replaced by quick and painless digital processes and helps to enhance digital customer centric experience.
Looking the relevance of digital financial service to financial inclusion, the government of Ethiopia is undertaking digital transformation to boost the economy and the necessary regulations have been crafted to create conducive environment. As per the study conducted by national bank of Ethiopia, only 35% of the population is financially included and the remaining 65% of the population is excluded from financial service. Needless to mention, digital financial services would allow financial institutions to outreach financially excluded segments of the population. Increasing digital adoption, digital payment offerings, ease of regulation to attract new entrants, and a growing fintech community are the main drivers of digital transformation in Ethiopia.
Chand Suri is seeking a position in finance or accounting with over 15 years of experience in data analysis, financial reporting, and security master maintenance at various financial institutions including Goldman Sachs, PanAm Mortgage, and Pershing. He has a bachelor's degree in finance and accounting and skills in Microsoft Office, QuickBooks, Bloomberg, and security master databases. His experience includes tasks like analyzing financial statements, researching securities, and ensuring data integrity across various systems.
International Marketing Management PowerPoint Presentation Slides SlideTeam
Every organization needs to adapt to the ever-changing business environment. Sensing this need, we have come up with these content-ready change management PowerPoint presentation slides. These change management PPT templates will help you deal with any kind of an organizational change. Be it with people, goals or processes. The business solutions incorporated here will help you identify the organizational structure, create vision for change, implement strategies, identify resistance and risk, manage cost of change, get feedback and evaluation, and much more. With the help of various change management tools and techniques illustrated in this presentation design, you can achieve the desired business outcomes. This business transition PowerPoint design also covers certain related topics such as change model, transformation strategy, change readiness, change control, project management and business process. By implementing the change control methods mentioned in the presentation, you will be able to have a smooth transition in an organization. So, without waiting much, download our extensively researched change management framework presentation. With our Change Management Presentation slides, understand the need for change and plan to go through it without any hassles.
The document provides information about Marriott International, the largest hotel chain in the world. It discusses Marriott's founding in 1927, current operations in 122 countries, and expansion in the 1980s. It then summarizes Marriott's marketing segmentation strategies for different hotel brands, target markets, price points, services, and facilities.
TRI Hospitality's Revenue Optimization Metamorphosis contains five steps to revolutionize revenue management in your hotel: Demand Optimization, Strategic Pricing, Strategic Channels, Superior Customer Service and Monitor & Report.
The document provides guidance on building a financial model to present to investors. It emphasizes that the financial model is the real business plan as it shows revenues, profits, cash needs, hiring plans, runway, and sensitivity analysis. The model should be built to answer questions about the viability, profitability, scalability, and sensitivity of assumptions for the business. The document outlines the key components of a financial model including income statements, balance sheets, cash flow statements, assumptions, and timelines. It provides examples of building out the revenue model with sales projections, the income statement template, headcount and salary projections, and non-salary expenses.
Bridging marke- credit risk-Modelling the Incremental Risk Charge.pptxGarima Singh Makhija
This document discusses modeling credit migration risk using a generator-based simulation approach. It outlines the key components of an incremental risk charge (IRC) model, including assigning positions to liquidity buckets, simulating rating transitions, pricing positions, and calculating profit and loss. The document discusses important modeling considerations like using through-the-cycle versus point-in-time transition data and calibrating to risk-neutral probabilities. It also provides mathematical background on representing rating transitions as a Markov process and using the generator matrix to describe time-dependent transition probabilities between discrete time periods. The goal is to develop a risk measurement model that is consistent with Basel capital requirements and can evaluate credit migration risk over a one-year horizon at a 99.9%
The document outlines a roadmap to transform a property and casualty insurer's direct marketing strategy from a mass mailing approach focused on call center volume to a more data-driven and targeted strategy. It details challenges around expertise, data access, and key performance indicators. The proposed strategy focuses on building analytics capabilities, testing creative concepts, and optimizing mailings based on customer value. Key metrics like response rates, revenue, and cost per acquisition are improved. The client is able to grow their direct business significantly while lowering marketing costs over a two year period.
How to increase the revenues of the hotel with Revenue management?Stanislav Ivanov
This document provides an overview of revenue management strategies and techniques that hotels can use to increase revenues. It begins with defining revenue management as optimizing net revenues through offering the right product to the right customers via the right distribution channel at the right time and price. It then covers economic fundamentals, the revenue management process, key metrics, tools like price discrimination and overbooking, channel management, software, and ethical considerations. The goal is to educate on how hotels can systematically use revenue management to maximize profits.
The receptive tour operator (RTO) channel sells over 23 million international hotel room nights annually through wholesalers overseas. RTOs act as intermediaries between US hotels/attractions and international wholesalers, developing products and contracts. They offer a variety of tour packages including escorted groups, fly-drive programs, and MICE tours. While RTOs add value through international distribution and language support, some disadvantages include introducing another layer of costs and requiring rates far in advance. Overall, the RTO channel represents an important part of international tourism sales to the US.
Infor EzRMS is a revenue management solution developed for the hospitality industry that:
- Uses advanced algorithms and business process design to optimize product prices and achieve maximum profits.
- Provides automated demand forecasts and recommendations on selling strategies to sell the right product to the right customer.
- Was designed to help hotels maximize revenue opportunities, occupancy rates, and profit margins through professional revenue management techniques.
Compcruncher Collateral Valuation Reports (CVR). Big data accuracy and reliability. Compare to any valuation in its value class at the price offered. Lenders told us that the CVR is a easy replacement of BPO's, particularly for HELOCs. Replacement for the MSM-Mortgage Servicing Market for their portfolio loans, Default management for any BPOs used in the process (short sales) Alternative in the loan modification programs Quality assurance, Secondary valuation in value dispute resolution and value reconciliation.
Examining a business is important to understand the success or failure. Mathematics and Statistics help uncover all types of ratios, proportions, averages, deviations etc. in all walks of human life. Maths is almost infallible. Statistics, a little less so.
Learning to use maths for a hospitality career begins right in the First year, but the Education Planners wait till the end to expose the students to the real villain in the movie. Profits, onward & upward...
This presentation gives an insight into the hotel industry. This industry has been top rated in terms of search on the internet. Learn a few basics before you start an internet marketing campaign focused majorly around PPC in the hospitality sector.
The basic premise with the Law of Attraction, Law of Vibration, Law of Gratitude, Law of Love, and Law of Allowing, is that when you practice these laws, and stay in harmony with them, you will prosper. You will have abundance. You will have plenty.
There is no denying it. Your thoughts control your actions. Your thoughts dictate what you end up getting from the Universe. If you believe completely that you will receive what you wish for, good things will come your way.
You must accept that which you wish upon. You must be tuned into the universe to get it. You must be in vibration to what you want. You must show the universe you want it by having gratitude for what you have received. You also must show that you are allowing it by being receptive to it and saying “yes” to it when it comes. By doing this, the universe will manifest it to reality and provide you more.
The main point with this book is to draw your attention to the fact that there are universal laws God put in place to help us. He loves us and wants the best for us. By acting in harmony with his will and by obeying his universal laws, you will have plenty. You just have to start the process with a thought, turn that thought in an image, send it to your heart for processing (this turns into emotions and feelings), act on your thoughts, and allow the results to come to you. By doing this you will receive results from your thoughts, whether they are good or bad.
The old saying is, “be careful what you wish for” or “you are your thoughts” holds true here in every respect. Therefore, watch your thoughts if you want the best that life has to offer.
It is hardly true that financial inclusion gaps forced countries around the world to explore the potential of digital financial services and fintech companies allow leapfrogging of traditional brick-and-mortar banking services. As per the study conducted by the World Bank, access to affordable financial services is critical for poverty reduction and economic growth. At the macro level, countries with deeper, more developed financial systems can allocate capital and risks more efficiently and consequently enjoy higher economic growth and larger reductions in poverty and income inequality. At the micro level, financial inclusions—access to and use of basic financial services—can reduce poverty, increase resilience and improve the lives of the poor. Digital financial services bridge the financial inclusion gaps and enhance economic growth. Fueled by the explosive growth of mobile phones, digital financial services (DFS) leverage technology to offer new forms of financial accounts that provide secure options for storing, transferring, and accumulating money. Hence, digital financial services are becoming accessible and affordable to all individuals and businesses through digital financial channels which ultimately boosts financial inclusion.
The contribution of digital financial service in alleviating constraints to financial access is quite immense. The emergence of mobile money, platform eco systems and open application programming interfaces (APIs) uplifted the digital financial service at the global level and impacted the level of financial inclusion. Yet in many emerging economies today, the majority of individuals and small businesses lack access to even basic savings and credit products, which hinders economic growth and perpetuates poverty. Financial exclusion is at the forefront in the list of the challenges which inhibit the growth of many economies around the globe.
Digital financial services enable financial institutions to provide convenient self-service saving and credit products. Traditional saving and lending processes are being replaced by quick and painless digital processes and helps to enhance digital customer centric experience.
Looking the relevance of digital financial service to financial inclusion, the government of Ethiopia is undertaking digital transformation to boost the economy and the necessary regulations have been crafted to create conducive environment. As per the study conducted by national bank of Ethiopia, only 35% of the population is financially included and the remaining 65% of the population is excluded from financial service. Needless to mention, digital financial services would allow financial institutions to outreach financially excluded segments of the population. Increasing digital adoption, digital payment offerings, ease of regulation to attract new entrants, and a growing fintech community are the main drivers of digital transformation in Ethiopia.
Chand Suri is seeking a position in finance or accounting with over 15 years of experience in data analysis, financial reporting, and security master maintenance at various financial institutions including Goldman Sachs, PanAm Mortgage, and Pershing. He has a bachelor's degree in finance and accounting and skills in Microsoft Office, QuickBooks, Bloomberg, and security master databases. His experience includes tasks like analyzing financial statements, researching securities, and ensuring data integrity across various systems.
International Marketing Management PowerPoint Presentation Slides SlideTeam
Every organization needs to adapt to the ever-changing business environment. Sensing this need, we have come up with these content-ready change management PowerPoint presentation slides. These change management PPT templates will help you deal with any kind of an organizational change. Be it with people, goals or processes. The business solutions incorporated here will help you identify the organizational structure, create vision for change, implement strategies, identify resistance and risk, manage cost of change, get feedback and evaluation, and much more. With the help of various change management tools and techniques illustrated in this presentation design, you can achieve the desired business outcomes. This business transition PowerPoint design also covers certain related topics such as change model, transformation strategy, change readiness, change control, project management and business process. By implementing the change control methods mentioned in the presentation, you will be able to have a smooth transition in an organization. So, without waiting much, download our extensively researched change management framework presentation. With our Change Management Presentation slides, understand the need for change and plan to go through it without any hassles.
The document provides information about Marriott International, the largest hotel chain in the world. It discusses Marriott's founding in 1927, current operations in 122 countries, and expansion in the 1980s. It then summarizes Marriott's marketing segmentation strategies for different hotel brands, target markets, price points, services, and facilities.
TRI Hospitality's Revenue Optimization Metamorphosis contains five steps to revolutionize revenue management in your hotel: Demand Optimization, Strategic Pricing, Strategic Channels, Superior Customer Service and Monitor & Report.
The document provides guidance on building a financial model to present to investors. It emphasizes that the financial model is the real business plan as it shows revenues, profits, cash needs, hiring plans, runway, and sensitivity analysis. The model should be built to answer questions about the viability, profitability, scalability, and sensitivity of assumptions for the business. The document outlines the key components of a financial model including income statements, balance sheets, cash flow statements, assumptions, and timelines. It provides examples of building out the revenue model with sales projections, the income statement template, headcount and salary projections, and non-salary expenses.
Bridging marke- credit risk-Modelling the Incremental Risk Charge.pptxGarima Singh Makhija
This document discusses modeling credit migration risk using a generator-based simulation approach. It outlines the key components of an incremental risk charge (IRC) model, including assigning positions to liquidity buckets, simulating rating transitions, pricing positions, and calculating profit and loss. The document discusses important modeling considerations like using through-the-cycle versus point-in-time transition data and calibrating to risk-neutral probabilities. It also provides mathematical background on representing rating transitions as a Markov process and using the generator matrix to describe time-dependent transition probabilities between discrete time periods. The goal is to develop a risk measurement model that is consistent with Basel capital requirements and can evaluate credit migration risk over a one-year horizon at a 99.9%
The document outlines a roadmap to transform a property and casualty insurer's direct marketing strategy from a mass mailing approach focused on call center volume to a more data-driven and targeted strategy. It details challenges around expertise, data access, and key performance indicators. The proposed strategy focuses on building analytics capabilities, testing creative concepts, and optimizing mailings based on customer value. Key metrics like response rates, revenue, and cost per acquisition are improved. The client is able to grow their direct business significantly while lowering marketing costs over a two year period.
1. Revenue Management Today:
A Look at What is Working
Steve Hood
Senior Vice President, Research
Smith Travel Research
2. Agenda
• Introduction to STR
• Introduction to the STAR Reports
• Monthly STAR Reports
• Weekly STAR Reports
• US Industry Update
• San Diego Industry Update
4. Who is Smith Travel Research?
• The recognized leader in hotel benchmarking
g g
• Founded in 1985
• W sample over 70% of t t l U S room supply (95% of
We l f total U.S. l f
chain hotels and most significant independent hotels) as
well as almost 45% of total global room supply
• Provide monthly, weekly, and daily STAR reports to over
40,000 hotels (over 600,000 emails per month)
• Impartial, timely, confidential, and accurate
5. Benchmarking 101: my hotel vs. the competition
20% 20%
15%
10% 7% 10%
ange
ange
YOY Cha
YOY Cha
0% 0%
-3%
-10%
10% 10%
-10%
-10%
-20% -20%
Manager A Manager B
A's Competitors B's Competitors
6. Who does STR serve?
• Hotel companies
• Hotels
• Convention & Visitor Bureaus, Tourism & travel
organizations
g
• Developers, Consultants, Appraisers
• Wall Street/Accounting firms, Financial institutions
• Media (including HNN, www.hotelnewsnow.com)
• Hotel vendors (Census database)
• Educators
• Government (including GSA)
7. What data does STR track?
• Hotel Census data – US & WW
• Pipeline data – under construction & in planning
• Rooms Sales data (Supply, Demand, & Revenue)
( pp y )
– Monthly (back to 1987)
– Daily (back to 2000)
• Segmented Sales data – group, transient, contract
• Additional Revenue data – F&B, other, total
• Profit & Loss numbers (Hotel Operating Statistics)
8. What services do we provide?
• Subscription reports
– STAR reports for hotels
– Corporate reports and data files for HQs
– Destination reports for CVBs
• Publications
– US and WW Hotel Reviews and Country Reports
– Market Forecasts
• Ad-Hoc Reports
– Trend reports
– Custom HOST reports
– Market Pipeline reports
• Special Projects
p j
9. STR Geographic Categorization
g p g
• World
• Continents: Americas, Europe, Mideast/Africa, Asia Pacific
• Sub-continents – subsets of continents
(Americas: North Am., South Am., Central Am., & Caribbean)
• Countries – based on ISO/WTO definitions
• Regions – for certain countries such as US, Canada, UKM
(US: New England, Mid Atlantic, Southeast, East North Central, …)
• M k t – for major cities, also non-metro areas
Markets
(US: 162 markets)
• Tracts – subsets of markets
(US: 612 tracts)
• (additional geo. fields: state, county, MSA, lat/long, user-defined)
10. STR Markets and Tracts
• STR markets represent
both metro and non-
metro areas
• Each market contains
at least two sub-
sub
divisions called tracts
(or submarkets outside
North America)
• There are 162 US
markets and 613 tracts.
Over 500 markets
worldwide
11. STR Non-geographic Categorization
• Scale – 7 groups, based on average ADR of entire chain
Luxury, Upper Upscale, Upscale, Midscale w/ & w/o F&B, Economy,
& Independents (a list of all chains by scale is available)
• Class – scale groups with independents included
• Location: Urban Suburban, Airport, Interstate Resort Small Town
Urban, Suburban Airport Interstate, Resort,
• Price – 5 groups, based on ADR relative to specific market
• Size – based on number of rooms
• Types: Extended Stay, Resort, Boutique, Casino, Golf, Ski, Spa,
Convention, Conference, All-suites, Full/Limited Service, Waterpark
• (additional non-geo. fields: Age, User-defined, Corporate Housing,
Condotel, Star Rating for WW)
12. Hotel Affiliation-Related Definitions
• Chain
• P
Parent C
t Company
• Management Company
• Owner
• Asset Management Company
• Membership or Marketing Group
13. STR Competitive Sets
• Which hotels directly compete with my property?
– Usually similar hotels in price level
– Usually hotels in close proximity
– Can include or exclude the subject property
• Hotels can have more than one comp set
– Local comp set and a 2nd with a broader geographic area
p g g p
– Weekday vs. weekend or group vs. transient
– Chain has one comp set and management company may have
another
th
– In between two towns
14. STR Competitive Sets Continued
• There are strict rules to ensure confidentiality
– There must be 4 or more reporting hotels
p g
– No more than 40% of rooms in a single chain
– No more than 60% of rooms in a parent company
– Changes to comp sets must be made in pairs to prevent
isolation
• Comp sets chosen by the hotel or by corporate
– Or some combination
– Often managers are bonused on performance relative to
comp set
15. Hotel Metrics 101
• Supply = rooms available
• D
Demand = rooms sold
d ld
• Occupancy = demand / supply (percentage)
• ADR = room revenue / demand ($)
• RevPAR = room revenue / supply (p available
pp y (per
room) ($)
• Percent Change = (this year # - last year #) / last
year # (%)
16. Hotel Metrics 101 Continued
• Index = property # / comp set #
– Number greater than 100 means subject property out
out-
performed the comp set
– Number less than 100 means comp set out-performed the
p p
subject property
– “Penetration” = Occupancy Index
– “Yield” = RevPAR Index
• Rank = property’s position among the comp set
– For example “2 of 7” means subject property had the 2nd
highest value (Occ, ADR, RevPAR) in the comp set
17. Hotel Date & Time Definitions
• Week = Sunday through Saturday
• C
Comparable Day = same d of same week
bl D day f k
last year, most daily percent changes are based
on comparable days (“day to day”) versus “date
( day day ), date
to date” (MTD would be based on date to date)
• Weekday = Sunday through Thursday
• Weekend = Friday and Saturday
18. Date & Time Definitions Continued
• MTD = from first to current day of month
• YTD = from January to current month
• Running #-month = last # months including the
g g
current month, valuable for comparing current
month to longer time period or for graphing (3 or
12 months)
• Running #-day = last # days including the current
day, same advantage as above (28 days)
19. Segmentation and Additional
Revenue Definitions
• Group = rooms sold in block of ten or more and
corresponding revenue
• Transient = rooms sold at rack, corporate, p
p package, or
g
government rates and corresponding revenue
• Contract = rooms sold at rates stipulated by contracts
including airline crews and permanent guests
• Food & Beverage Revenue = revenue from any F&B
establishment
• Other Revenue = all revenue excluding room & F&B
revenue
21. What is the STAR Report?
• The STAR Report analyzes your hotel’s performance:
– Occupancy ADR and RevPAR
Occupancy, ADR,
• Provides comparison to competitors:
– Index numbers (GT 100 = subject winning, LT 100 = comp winning)
– Ranking information (# of #, i.e.: “2 of 7” = 2nd best in comp of 7)
• Provides comparison to various market/industry segments
• Tracks performance for multiple time periods (current
month/week/day, YTD/MTD, running 12 month/running 28 day)
• Tracks year-over-year performance (TY vs. LY percent change)
22. STAR Reports – how and when?
• Excel workbooks (or PDF documents) with multiple tabs
• I l d d
Includes data tables, graphs, and explanations
bl h d l i
• Monthly, weekly, and daily STAR reports are generated
• Delivered to most subscribers via email (or FTP)
– Monthly: on or around the 17th
– Weekly: Tuesday night
– Daily: each day (for WW and select US markets)
• Some reports are delivered to HQ for download by hotel
from corporate website
23. Why do I need the STAR Report?
• Benchmarks your performance
• Allows you to keep an eye on your competitors and your
market
• Lends perspective to good and bad performance
• Aids pricing decisions, help improve profitability
• Allows you to monitor the effect of your business
strategies
• View Supply, Demand, & Revenue changes in your
market
24. Who am I compared to on my STAR
report?
• Competitive Set: a set of 4 or more properties
p p p
• Market: for example Memphis or Nashville
• Market Class: subset of market with similar chain
affiliated and independent hotels (6 groups)
• Tract: geographic subset of market such as Memphis
Airport or Nashville South
• Tract Scale: subset of tract with similar chain or
independent hotels (4 groups)
• WW reports vary slightly
25. Segmentation and Additional
Revenue pages in STAR Reports
• Hotels or companies can subscribe to obtain additional
pages (tabs) in their monthly and weekly STAR reports
• Segmentations pages display Occupancy, ADR, and
RevPAR for Transient, Group, and Contract segments,
also related percent changes, indexes, day of week, and
ranking information
g
• Additional Revenue pages display Revenue Per Rooms
Sold for Room, F&B, Other, and Total Revenue
• A Segmentation Response page displays Segmentation
participation for the comp set
27. Table of Contents
• First tab of the workbook
• Lists all the pages/tabs that are included
• Will list two additional pages for Daily
participants (dark grey)
• Will list eleven additional pages for
Segmentation participants (light grey)
• W ld i l d additional pages f h t l with
Would include dditi l for hotels ith
2nd, 3rd, or 4th comp sets
28. United Kingdom United States
Blue Fin Building 735 East Main Street
110 Southwark Street Hendersonville
London SE1 0TA TN 37075
Phone: +44 (0)20 7922 1930 Phone: +1 (615) 824 8664
Fax: +44 (0)20 7922 1931 Fax: +1 (615) 824 3848
www.strglobal.com www.strglobal.com
Monthly STAR Report Sample (US & Canada)
February 2008 STR #: 98765 Date Created: March 28, 2008
Tab
Table of Contents 1
Monthly Performance at a Glance 2
STAR Summary 3
Co pet t e
Competitive Set Report
epo t 4
Response Report 5
Segmentation Summary 6
Segmentation Occupancy Analysis 7
Segmentation ADR Analysis 8
Segmentation RevPAR Analysis 9
Segmentation Index Analysis 10
Segmentation Ranking Analysis
S 11 Available to S
Segmentation participants only
Segmentation Day of Week - Current Month 12
Segmentation Day of Week - Year to Date 13
Additional Revenue ADR Analysis 14
Additional Revenue RevPAR Analysis 15
Segmentation Reponse Report 16
Daily Data for the Month 17
Available to Weekly STAR participants only
Day of Week & Weekday/Weekend 18
Help 19
29. 1 - Monthly Performance at a Glance
y
• Provides a quick one-page overview of
performance relative to your comp set
• Displays Occupancy, ADR, and RevPAR for
your property and your comp set, also I d
t d t l Index
numbers and Percent Changes
• For four points in time: Current Month, Year to
Date, Running 3-month, and Running 12-month
• Also displays basic information for the subject
property and the report settings at the top
30. Monthly Performance at a Glance
Tab 2 - Monthly Performance at a Glance - My Property vs. Competitive Set
Any Hotel
y 123 Any Street
y Any City, Any State 99999
y y, y (555) 555-5555
( )
STR # 98765 ChainID: 999999 MgtCo: None Owner: None
For the Month of: July 2006 Date Created: August 24, 2006 Monthly Competitive Set Data Excludes Subject Property
July 2006
Occupancy (%) ADR ($) RevPAR ($)
My Prop Comp Set Index My Prop Comp Set Index My Prop Comp Set Index
Current Month 89.8 90.5 99.3 234.89 229.88 102.2 210.89 207.93 101.4
Year To Date 80.9 86.5 93.4 248.55 241.02 103.1 201.01 208.60 96.4
Running 3 Month 82.0 91.5 89.6 266.87 255.11 104.6 218.75 233.50 93.7
Running 12 Month 84.9 87.7 96.8 259.98 254.12 102.3 220.81 222.92 99.1
July 2006 vs. 2005 Percent Change (%)
Occupancy ADR RevPAR
My Prop Comp Set Index My Prop Comp Set Index My Prop Comp Set Index
Current Month 2.7 0.7 2.0 10.0 8.8 1.1 12.9 9.5 3.1
Year To Date -8.5 3.2 -11.3 14.3 10.8 3.2 4.6 14.3 -8.5
Running 3 Month -9.8 1.1 -10.7 15.2 11.7 3.1 3.9 12.9 -7.9
Running 12 Month -5.6 2.4 -7.7 15.2 13.8 1.2 8.8 16.5 -6.6
31. 2 – STAR Summary
y
• Provides a detailed comparison of your property
to various industry segments and your comp set
• Displays Occupancy, ADR, and RevPAR, as
well as percent changes f each group
ll t h for h
• For four points in time: Current Month, Year to
Date, Running 3-month, and Running 12-month
• Also shows Supply, Demand, and Revenue
pp y, ,
Percent Changes; Census and Sample property
and room counts; and a Pipeline overview
32. STAR Summary
Tab 3 - STAR Summary - My Property vs. Comp Set and Industry Segments
Any Hotel 123 Any Street Any City, Any State 99999 (555) 555-5555
STR # 98765 ChainID: 999999 MgtCo: None Owner: None
For the Month of: July 2006
y Date Created: August 24, 2006
g , Monthly Competitive Set Data Excludes Subject Property
y p j p y
Occupancy (%) Supply
Current Running 3 Running 12 Month % Run 3 Mon % Run 12 Mon
% Chg Year to Date % Chg % Chg % Chg YTD % Chg
Month Month Month Chg Chg % Chg
Any Hotel 89.8 2.7 80.9 -8.5 82.0 -9.8 84.9 -5.6 0.0 0.0 0.0 0.0
Market: Any Market 84.6 -0.3 81.2 -1.1 85.6 -0.5 82.4 -0.4 -0.3 -0.8 -0.6 -1.1
Market Class: Any Market Upscale 88.1 3.6 84.3 1.5 88.5 1.5 85.1 1.6 0.3 -1.7 -1.2 -1.5
Tract: Any Tract 87.6 0.3 84.4 -0.6 88.4 -0.4 85.4 -0.4 1.1 -0.7 0.8 -1.4
Tract Scale: Upscale Chains 88.8 2.1 85.0 0.8 88.9 0.1 86.0 0.4 1.3 -4.1 0.1 -4.9
Competitive Set: Competitors 90.5 0.7 86.5 3.2 91.5 1.1 87.7 2.4 0.0 0.0 0.0 0.4
Average Daily Rate ($) Demand
Current Running 3 Running 12 Month % Run 3 Mon % Run 12 Mon
% Chg Year to Date % Chg % Chg % Chg YTD % Chg
Month Month Month Chg Chg % Chg
Any Hotel 234.89 10.0 248.55 14.3 266.87 15.2 259.98 15.2 2.7 -8.5 -9.8 -5.6
Market: Any Market 205.50 10.9 215.06 12.6 228.69 13.3 226.23 14.7 -0.6 -1.8 -1.1 -1.5
Market Class: Any Market Upscale 210.02 8.3 221.89 12.2 237.71 12.1 235.76 16.1 3.9 -0.2 0.2 0.1
Tract: Any Tract 222.40 10.1 232.32 12.2 248.61 12.9 246.89 14.8 1.4 -1.3 0.3 -1.8
Tract Scale: Upscale Chains 258.03 8.7 270.02 9.7 285.94 10.6 286.00 12.1 3.5 -3.3 0.2 -4.5
Competitive Set: Competitors 229.88 8.8 241.02 10.8 255.11 11.7 254.12 13.8 0.7 3.2 1.1 2.8
RevPAR ($) Revenue
Current Running 3 Running 12 Month % Run 3 Mon % Run 12 Mon
% Chg Year to Date % Chg % Chg % Chg YTD % Chg
Month Month Month Chg Chg % Chg
Any Hotel 210.89 12.9 201.01 4.6 218.75 3.9 220.81 8.8 12.9 4.6 3.9 8.8
Market: Any Market 173.89 10.5 174.66 11.4 195.79 12.7 186.32 14.2 10.2 10.5 12.1 13.0
Market Class: Any Market Upscale 185.04 12.2 187.10 13.9 210.44 13.7 200.59 18.0 12.5 12.0 12.3 16.2
Tract: Any Tract 194.77 10.4 196.01 11.5 219.76 12.4 210.97 14.4 11.6 10.7 13.3 12.8
Tract Scale: Upscale Chains 229.07 11.0 229.61 10.6 254.14 10.7 245.83 12.6 12.5 6.0 10.8 7.0
Competitive Set: Competitors 207.93 9.5 208.60 14.3 233.50 12.9 222.92 16.5 9.5 14.3 12.9 17.0
Census/Sample - Properties & Rooms Pipeline
Census Sample Sample % Market: Any Market
Properties Rooms Properties Rooms Rooms Under Construction Planning
Market: Any Market 383 78844 204 59398 75.3 Properties Rooms Properties Rooms
Market Class: Any Market Upscale 59 21762 48 20182 92.7 34 3948 61 7302
Tract: Any Tract 77 28152 49 22772 80.9
Tract Scale: Upscale Chains 23 14579 21 13913 95.4 See Help page for pipeline definitions.
Competitive Set: Competitors 6 6865 6 6865 100.0
33. STAR Summary – My Property vs. Comp Set and Industry Segments
RevPAR ($)
Current Running 3 Running 12
% Chg Year to Date % Chg % Chg % Chg
Month Month Month
Any Hotel 210.89 12.9 201.01 4.6 218.75 3.9 220.81 8.8
Market: Any Market 173.89 10.5 174.66 11.4 195.79 12.7 186.32 14.2
Market Class: Any Market Upscale 185.04 12.2 187.10 13.9 210.44 13.7 200.59 18.0
Tract: Any Tract 194.77 10.4 196.01 11.5 219.76 12.4 210.97 14.4
Tract Scale: Upscale Chains 229.07 11.0 229.61 10.6 254.14 10.7 245.83 12.6
Competitive Set: Competitors 207.93 9.5 208.60 14.3 233.50 12.9 222.92 16.5
• Rows display data for your hotel, 4 industry segments, and your comp
set
• Columns display values and percent changes for the current month
month,
YTD, running 3 month, and running 12 month time periods
• Sections for Occupancy, ADR and RevPAR
34. STAR Summary – Supply, Demand, and Revenue Section
Supply
Month % Run 3 Mon % Run 12 Mon
Percent changes for multiple time periods are
YTD % Chg
Chg Chg % Chg
displayed
0.0 0.0 0.0 0.0
-0.3 -0.8 -0.6 -1.1
0.3 -1.7 -1.2 -1.5
1.1
11 -0.7
07 0.8
08 -1.4
14
1.3 -4.1 0.1 -4.9 Supply: The number of rooms in the segment
0.0 0.0 0.0 0.4
multiplied by the number of days in the period
Demand
Month % Run 3 Mon % Run 12 Mon
YTD % Chg
Chg Chg % Chg
2.7 -8.5 -9.8 -5.6
-0.6 -1.8 -1.1 -1.5
Demand: The number of rooms sold (excludes
3.9 -0.2 0.2 0.1
1.4 -1.3 0.3 -1.8 complimentary rooms)
3.5
35 -3 3
3.3 0.2
02 -4 5
4.5
0.7 3.2 1.1 2.8
Revenue
Month %
Chg
YTD % Chg
Run 3 Mon % Run 12 Mon
Chg % Chg
Revenue: Total room revenue generated
12.9 4.6 3.9 8.8
from the sa e ( e a ) o rooms
o e sale (rental) of oo s
10.2 10.5 12.1 13.0
12.5 12.0 12.3 16.2
11.6 10.7 13.3 12.8
12.5 6.0 10.8 7.0
9.5
95 14.3
14 3 12.9
12 9 17.0
17 0
35. STAR Summary – Census and Participation Information
Census/Sample - Properties & Rooms
Census Sample Sample %
Properties Rooms Properties Rooms Rooms
Market: Any Market 383 78844 204 59398 75.3
Market Class: Any Market Upscale 59 21762 48 20182 92.7
Tract: Any Tract 77 28152 49 22772 80.9
80 9
Tract Scale: Upscale Chains 23 14579 21 13913 95.4
Competitive Set: Competitors 6 6865 6 6865 100.0
Participation information is displayed for the industry segments and comp set
Census: Total number of properties / rooms in a segment
Sample: The number of properties / rooms that report data to STR
Sample %: Percentage of rooms in each segment that report data to STR
36. STAR Summary – Pipeline Information
Under construction: Ground
Pipeline
has been broken or the owner is
Market: Any Market finalizing bids for the general
Under Construction Planning contracting
Properties Rooms Properties Rooms
34 3948 61 7302
Planning: Project will g out
g j go
See Help page for pipeline definitions. for bids, construction will start
within 4 months, or an
architect as been selected and
plans are underway
Provides a quick snapshot of pipeline activity in the market
Pipeline data is detailed further in the STR Market Pipeline Report
37. 3 – Competitive Set Report
p p
• Provides a historic comparison of your property to
your comp set over time
• Displays Occupancy, ADR, and RevPAR, as well
as P
Percent Changes, I d numbers, and
t Ch Index b d
Ranking information
• Shows 18 months of monthly data and 3 years of
Year to Date, Running 3-month, and Running 12-
month
• Graphs monthly indexes and RevPAR % Change
38. Competitive Set Report
Tab 4 - Competitive Set Report
Any Hotel 123 Any Street Any City, Any State 99999 (555) 555-5555
STR # 9850 ChainID: 000026566 MgtCo: None Owner: None
For the Month of: July 2006 Date Created: August 24, 2006 Monthly Competitive Set Data Excludes Subject Property
Monthly Indexes RevPAR Percent Change - 2006
117
19
107 14
97 9
4
87
-1
77 Year to Date Running 3 Month Running 12 Month
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
My Property Competitive Set
Occupancy ADR RevPAR 100 %
2005 2006 Year To Date Running 3 Month Running 12 Month
Occupancy (%)
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul 2004 2005 2006 2004 2005 2006 2004 2005 2006
My Property 85.2
85 2 94.1
94 1 91.4
91 4 94.1
94 1 91.0
91 0 87.5
87 5 87.4
87 4 92.3
92 3 89.9
89 9 93.7
93 7 89.8
89 8 74.8
74 8 78.2
78 2 84.3
84 3 82.7
82 7 75.1
75 1 81.0
81 0 89.8
89 8 85.9
85 9 88.4
88 4 80.9
80 9 88.9
88 9 90.8
90 8 82.0
82 0 86.9
86 9 89.9
89 9 84.9
84 9
Competitive Set 75.7 84.5 86.9 90.8 91.1 89.9 89.1 89.7 88.9 91.3 87.7 78.4 77.1 84.8 90.3 92.1 92.1 90.5 83.9 83.9 86.5 89.8 90.6 91.5 83.3 85.7 87.7
Index 112.6 111.3 105.1 103.6 99.9 97.3 98.1 102.8 101.1 102.6 102.3 95.5 101.4 99.4 91.6 81.6 87.9 99.3 102.4 105.4 93.4 98.9 100.3 89.6 104.4 105.0 96.8
Rank 2 of 7 1 of 7 3 of 7 1 of 7 4 of 7 4 of 7 5 of 7 2 of 7 2 of 7 2 of 7 3 of 7 5 of 7 3 of 7 6 of 7 6 of 7 7 of 7 7 of 7 6 of 7 2 of 7 2 of 7 6 of 7 4 of 7 4 of 7 7 of 7 2 of 7 2 of 7 5 of 7
% Chg
My Property 9.6 6.1 -1.9 3.2 6.4 -2.7 -3.4 1.1 -3.1 0.1 -3.0 -0.7 -8.2 -10.4 -9.4 -20.2 -11.0 2.7 4.9 2.9 -8.5 3.3 2.2 -9.8 2.5 3.5 -5.6
Competitive Set -0.4 1.4 0.0 2.9 -0.7 0.3 1.7 -0.8 -2.0 2.8 4.6 15.7 1.9 0.4 3.9 1.4 1.1 0.7 13.0 0.0 3.2 8.4 0.8 1.1 8.8 2.9 2.4
Index 10.0 4.7 -1.9 0.3 7.1 -2.9 -5.1 1.9 -1.2 -2.5 -7.3 -14.2 -10.0 -10.7 -12.9 -21.3 -12.0 2.0 -7.1 2.9 -11.3 -4.8 1.4 -10.7 -5.8 0.5 -7.7
Rank 3 of 7 3 of 7 6 of 7 3 of 7 1 of 7 7 of 7 6 of 7 3 of 7 6 of 7 6 of 7 7 of 7 7 of 7 5 of 7 7 of 7 7 of 7 7 of 7 7 of 7 3 of 7 6 of 7 3 of 7 7 of 7 5 of 7 3 of 7 7 of 7 6 of 7 4 of 7 7 of 7
2005 2006 Year To Date Running 3 Month Running 12 Month
ADR ($)
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul 2004 2005 2006 2004 2005 2006 2004 2005 2006
My Property 192.91 210.16 230.88 235.01 246.28 213.62 203.79 268.37 285.03 305.36 305.85 219.85 214.76 233.08 265.79 280.23 290.72 234.89 192.40 217.45 248.55 201.36 231.75 266.87 198.80 225.66 259.98
Competitive Set 199.79 210.74 225.94 233.05 241.04 211.33 205.11 275.40 273.81 294.54 310.55 211.98 214.75 235.64 249.42 263.19 272.36 229.88 189.64 217.61 241.02 196.98 228.41 255.11 195.32 223.24 254.12
Index 96.6 99.7 102.2 100.8 102.2 101.1 99.4 97.4 104.1 103.7 98.5 103.7 100.0 98.9 106.6 106.5 106.7 102.2 101.5 99.9 103.1 102.2 101.5 104.6 101.8 101.1 102.3
Rank 4 of 7 3 of 7 2 of 7 2 of 7 2 of 7 2 of 7 3 of 7 4 of 7 2 of 7 3 of 7 2 of 7 3 of 7 3 of 7 3 of 7 1 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7
% Chg
My Property 7.8 7.6 19.5 10.4 22.9 12.1 11.4 19.2 17.8 19.1 11.2 19.0 11.3 10.9 15.1 19.2 18.0 10.0 7.0 13.0 14.3 9.9 15.1 15.2 3.9 13.5 15.2
Competitive Set 14.8 11.0 18.2 13.4 18.1 16.4 12.2 27.2 16.9 18.8 14.3 11.0 7.5 11.8 10.4 12.9 13.0 8.8 8.5 14.7 10.8 12.4 16.0 11.7 3.5 14.3 13.8
Index -6.1 -3.1 1.1 -2.6 4.1 -3.7 -0.7 -6.3 0.8 0.2 -2.7 7.2 3.6 -0.8 4.3 5.6 4.5 1.1 -1.3 -1.5 3.2 -2.3 -0.7 3.1 0.4 -0.7 1.2
Rank 7 of 7 6 of 7 2 of 7 7 of 7 2 of 7 6 of 7 5 of 7 6 of 7 3 of 7 3 of 7 6 of 7 2 of 7 3 of 7 5 of 7 2 of 7 2 of 7 2 of 7 4 of 7 5 of 7 6 of 7 3 of 7 5 of 7 5 of 7 2 of 7 5 of 7 5 of 7 3 of 7
2005 2006 Year To Date Running 3 Month Running 12 Month
RevPAR ($)
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul 2004 2005 2006 2004 2005 2006 2004 2005 2006
My Property 164.38 197.68 210.94 221.07 224.01 186.83 178.09 247.59 256.27 286.01 274.51 164.53 167.96 196.43 219.91 210.46 235.42 210.89 165.25 192.16 201.01 178.95 210.49 218.75 172.84 202.97 220.81
Competitive Set 151.21 178.06 196.34 211.53 219.51 189.89 182.70 247.16 243.55 269.01 272.41 166.12 165.68 199.80 225.29 242.33 250.80 207.93 159.02 182.47 208.60 176.92 206.84 233.50 162.61 191.31 222.92
Index 108.7 111.0 107.4 104.5 102.0 98.4 97.5 100.2 105.2 106.3 100.8 99.0 101.4 98.3 97.6 86.9 93.9 101.4 103.9 105.3 96.4 101.1 101.8 93.7 106.3 106.1 99.1
Rank 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 2 of 7 3 of 7 4 of 7 2 of 7 2 of 7 3 of 7 4 of 7 3 of 7 4 of 7 5 of 7 7 of 7 4 of 7 2 of 7 2 of 7 2 of 7 3 of 7 2 of 7 2 of 7 5 of 7 2 of 7 2 of 7 3 of 7
% Chg
My Property 18.1 14.1 17.3 13.9 30.8 9.1 7.6 20.5 14.1 19.2 7.9 18.2 2.2 -0.6 4.2 -4.8 5.1 12.9 12.3 16.3 4.6 13.5 17.6 3.9 6.5 17.4 8.8
Competitive Set 14.3 12.5 18.2 16.6 17.3 16.8 14.1 26.2 14.6 22.1 19.6 28.4 9.6 12.2 14.7 14.6 14.3 9.5 22.5 14.7 14.3 21.9 16.9 12.9 12.6 17.7 16.5
Index 3.3 1.5 -0.8 -2.4 11.5 -6.6 -5.8 -4.5 -0.4 -2.3 -9.8 -7.9 -6.7 -11.4 -9.1 -16.9 -8.0 3.1 -8.3 1.3 -8.5 -6.9 0.6 -7.9 -5.4 -0.2 -6.6
Rank
R k 3 of 7
f 5 of 7
f 4 of 7
f 6 of 7
f 1 of 7
f 7 of 7
f 7 of 7
f 7 of 7
f 5 of 7
f 5 of 7
f 7 of 7
f 6 of 7
f 6 of 7
f 7 of 7
f 7 of 7
f 7 of 7
f 7 of 7
f 3 of 7
f 6 of 7
f 5 of 7
f 7 of 7
f 6 of 7
f 3 of 7
f 7 of 7
f 6 of 7
f 4 of 7
f 7 of 7
f
39. Competitive Set Report – Graph of Indexes
Dotted Line Represents an Index of 100
Monthly Indexes
117
112
107
102
97
92
87
82
77
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
Occupancy ADR RevPAR 100 %
18 month graph of Occupancy, ADR, and RevPAR indexes by month
40. Competitive Set Report – Data Tables
18 months of occupancy, ADR and R PAR f your hotel versus th
th f d RevPAR for h t l the
competitive set
3 years of YTD, running 3-month and running 12-month data
Includes % change calculations, index numbers, and ranking information
Index = Property performance / competitive set performance X 100
41. 4 – STAR Response Report
p p
• Provides monthly calendars for this year and last
year,
year including special events
• Lists your hotel and every property in your comp
set with b i i f
t ith basic information and d t participation
ti d data ti i ti
detail for the last three years
• Empty circles indicate monthly only; filled in
circles indicate monthly and daily; no circle
means no data
• Check to be sure all properties are participating
42. STAR Response Report
Tab 5 - Response Report
Any Hotel 123 Any Street Any City, Any State 99999 (555) 555-5555
STR # 98765 ChainID: 999999 MgtCo: None Owner: None
For the Month of: July 2006 Date Created: August 24, 2006
This Year July 2006 (This Year) July 2005 (Last Year)
Jul 4th - Independence Day Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat
1 1 2
2 3 4 5 6 7 8 3 4 5 6 7 8 9
9 10 11 12 13 14 15 10 11 12 13 14 15 16
16 17 18 19 20 21 22 17 18 19 20 21 22 23
23 24 25 26 27 28 29 24 25 26 27 28 29 30
30 31 31
Last Year
Jul 4th - Independence Day
2004 2005 2006
May
May
Aug
Sep
Aug
Sep
Nov
Dec
Nov
Dec
Mar
Mar
Feb
Feb
Jan
Jun
Jan
Jun
Apr
Apr
Oct
Oct
Jul
Jul
STR# Name City, State Zip Phone Rooms Open Date
98765 Any Hotel Any City, Any State 99999 (555) 555-5555 100 190001 ●●●●●●●●●●●●●●●●●●●●●●●●
99876 Hotel A Any City, Any State 99999 (555) 555-5555 200 190002 ●●●●●●●●●●●●●●●●●●●●●●●●
99987 Hotel B Any City, Any State 99999 (555) 555-5555 300 190003 ●●●●●●●●●●●●●●●●●●●●●●●●
99998 Hotel C Any City, Any State 99999 (555) 555-5555 400 190004 ●●●●●●●●●●●●●●●●●●●●●●●●
99999 Hotel D Any City, Any State 99999 (555) 555-5555 500 190005 ●●●●●●●●●●●●●●●●●●●●●●●●
98876 Hotel E Any City, Any State 99999 (555) 555-5555 600 190006 ●●●●●●●●●●●●●●●●●●●●●●●●
98887 Hotel F Any City, Any State 99999 (555) 555-5555 700 190007 ●●●●●●●●●●●●●●●●●●●●●●●●
2800
Data received:
○ = Monthly Only
● = Monthly & Daily
Displays info and p
p y participation for hotels in the comp set, also months and events
p p ,
43. 5 – Daily Data for the Month
y
• Provides a recap of all the daily data for the
current month
• Helps you reconcile the daily data to the monthly
results
lt
• Graphs daily index values during the month
• Should be similar to the daily data from your
weekly reports although there may be minor
y p g y
changes due to adjustments
46. 6 – Day of Week and Weekday
y y
Weekend Report
• D ill d
Drills down i t d il d t b each d of week
into daily data by h day f k
and subtotals by Weekday and Weekend
• Provides Occupancy, ADR, and RevPAR for
property and comp set, plus Index numbers and
Percent Changes
• Allows you to compare DOW and WD/WE
performance for Current Month, YTD, Running 3
and Running 12-month