The goal is to sell the right product, to the right customer, at the right time, for the right price.

was first applied in the airline industry after deregulation in the 1980s. After witnessing the successful implementation by the airline programs, hotels adopted revenue management beginning in the early 1990s.

has always been a data and analytics heavy discipline, and was arguably the first extensive application of advanced analytics in the hotel industry.

This involved managing demand for a specific segment of an itinerary when many different kinds of itineraries, all with different values to the airline, also flowed over it, and optimizing the availability of different fares on these connecting itineraries.

For hotels, creates a similar issue to the hub- and-spoke problem in airlines. Think about a hotel that is popular on weekends.

It is important to consider the peak night along with the surrounding nights

Hotels take reservations in advance, frequently without a guarantee. When this happens, there is a chance of no shows or last-minute cancellations, meaning the hotel is left with empty rooms that could have been sold ahead of time -a potentially significant loss of revenue.

The advent of e-commerce and ____ ushered in an era of price transparency, where consumers have easy access to all the prices in the market.

Vendors have started collecting information on booking pace into key markets. Hotels can have access to aggregated market demand data so that they can understand the market potential versus what they have on books already.

Studies have shown that distribution costs are outpacing revenue growth, and if hoteliers aren't careful, these rising costs will eat away all of their profits. These studies seem to indicate thal hotels should be carefully tracking distribution costs. The challenge is that for some distribution partners, the hotel only ever sees the net rate minus the commission.

There is strong evidence that a hotel's ____, particularly as expressed in reviews, has a relationship with a hotel's pricing power.

Some argue that airline capacity into a market is a marker of how much demand a hotel can expect, and should be accounted for in forecasting. Further, many say that consumers will generally book an airline ticket before they book a hotel, so the number of airline bookings for future dates might be a directional indication of demand. This could be more impactful in certain markets than others.

Casinos have been considering patron value in the revenue management algorithms and decisions for a while now. Hotels are now starting to consider how guest lifetime value should fit into revenue management tactics and strategies.

Some believe that it is a leading indicator of demand into a market, even before any bookings are made. If a hotel notices that search figures are higher or lower than normal during a particular period, it could indicate that demand will be higher or lower as well.

as a blanket term for any type of analytics used to set a price that optimizes hotel revenue.

As revenue management gained success in maximizing revenue from hotel rooms, they naturally started to extend the application beyond hotel rooms to the other revenue-generating assets in the hotel

when did hotels adopted the revenue management?

Revenue management is a specialized pricing discipline applied to industries that have the following characteristics:

It encompass a few functions that leverage similar data for a similar purpose in the hotel and casino business. Departments like finance and development that are responsible for analyzing the performance of units, brands, or regions, and identifying areas of opportunity.

They can be responsible for developing new units or new locations, as well as reporting on the overall performance of the enterprise to stakeholders or shareholders

The performance management functions live and die by core industry metrics like

for the performance analysis function are similar to the emerging data sources that are of interest to other hotel departments

can be used to understand what factors are related to key outcomes and regression

can determine whether differences in performance are statistically significant

in performance analysis is typically performed at a higher level and over a longer range than revenue management forecasting, which is why a revenue management-developed forecast is not generally a substitute for a performance forecast

to review performance data against the characteristics of existing properties to predict performance of a new location.

is very useful in site selection, where the model will predict the performance of a specific brand or type of hotel in that site based on relevant characteristics, or determine the ideal size and type of hotel that would best exploit the potential of the te

There are many constraints in the performance analysis problem, like budgets, agreements with existing franchisees and ownership groups about cannibalization, potential of area-demand generators, and competitive factors.

could help hotels decide where they want to invest with what brands or locations, given the operating constraints

Since performance analysis is so heavily connected with data and reporting. The ideal for performance management is to be able to rapidly produce answers to business questions that impact company strategy or investor relations

At the lower end of sophistication, hospitality and gaming companies are operating in a highly manual performance analysis environment

They use Excel extensively as a data aggregation and reporting tool. It takes a long time to create operating reports

The hotel or casino company has or is in the process of creating a data warehouse and a data management strategy that includes some type of data governance.

have invested in a data visualization tool (sometimes multiple tools), and are using them to automate and speed up the process of creating key operating reports.

Overloaded information technology (IT) departments are still a roadblock in many cases, so any analyses outside of routine reporting tasks can still be manual, and still take time.

already have a data warehouse in place, and in some cases are going through a modernization effort to make the environment more flexible and responsive.

They could be investigating data virtualization and big data strategies like Hadoop. They extensively use data visualization tools, and have a process in place for responsive ad hoc analyses.

Performance analysis functions are using advanced analytic techniques like statistical modeling forecasting, and predictive analytics. They are building robust models to drive their development strategies. I haven't spoken much about IT in this book so far (this is not an accident, the book is targeted at the business).

The performance management functions live and die by core industry metrics like occupancy, average daily rate (ADR), and revenue per available room (RevPAR

In order to derive bottom-line metrics, they collect detailed information about cost structures of properties

in general are important indicators of potential future performance.

This has intensified competition around the globe. As competition increases, casinos are beginning to diversify their offerings. With increasing focus on developing offerings outside of the casino floor, nonganing revenue is outpacing gaming revenue in some markets, reaching a broader mix of potential patrons.

were among the first to implement rewards programs to track and reward player behavior

These programs provided incentives for gaming patrons to consolidate their play where they could eam more of their preferred benefits, while giving the casino company access to player behavior and value

casino floor operators can use social media activity to understand what is driving players to the casino floor, whether it's specific games they prefer or other activities around the gaming floor

along with reviews and ratings collected both for the casino and their competition, will assist in uncovering valuable information about marketwise preferences and how the casino is stacking up to the competition.

can be used to track traffic patterns around the casino, as well as to influence patrons either through advertising along popular paths or by creating diversions that purposely route patrons past attractions that the casino operators want to feature

used to determine the drivers of patron value

Thought of from the house perspective, represents amount the house will gain from a particular player or a particular game. Remember, all of these calculations are based on probability, so this figure is an estimation. This metric is calculated as average bet hours played x decisions per hour house advantage

This metric is calculated as average bet x time played speed of game house advantage (hold percentage).

This metric is calculated as coin in hold percentage. It might also consider player behavior on the specific slot machine type. For example, if the player always plays the max bet they have a chance at winning the jackpot, or some of the smaller prizes that are available only at max bets. If they don't play max, they won't win, so there's less risk for the casino

Much like other functions within the hotel or casino, for budgeting purposes, casino floor operators forecast casino floor revenue over a longer term time horizon

can help to schedule labor like cocktail servers, dealers, security, and maintenance. Can also help in forming a plan for raising and lowering the minimum bets on the table games

is the basis for calculating patron lifetime value

also described in the marketing chapter, can help to split the patrons into groups with similar behavior or characteristics beyond player value for better targeting.

is also useful for this casino floor design problem, as it can help to assess the impact of changes in this very complex system. Note that many jurisdictions have rules about how often you can change the configuration of the casino floor.

can be used to calculate the optimal deployment and schedule configuration for casino floor staff. It can also be used to figure out an optimal schedule for opening and closing table games and raising and lowering minimum bets

Only so many square feet for games, so many slot machines available, so many seats at table games.

If the seat at that table at that time is empty, it won't generate revenue. If no one is playing the slot machine between 6 p.m. and 7 p.m., it won't generate revenue.

There are peak and off-peak periods at the casino and patrons can be grouped by price sensitivity, theoretical value, and game preference

The cost of adding one more patron to a seat or on a machine is minimal as compared to the overhead for running the property

This problem starts with the right mix of games, although this is regulated in some areas. Calculating the right mix of game types involves understanding demand for each game type, the value of that demand, and the number of tables that can fit in the allocated space. This decision will only be made periodically, as the configuration can be difficult to change out once it is implemented.

The casino can control when they open and close tables. The cost associated with opening the table is primarily staffing, which, compared to revenue potential, could be minimal. It's not nothing, though, so casinos do not really want to open tables when there are no interested player

represents the amount that a player must wager per round in order to play. The casino has the option to raise or lower these bets at any time. Most casinos have moved to digital signage, so the change is a matter of flipping a switch. Many casinos will allow players to continue playing the original minimum if the minimum is raised while the player is still playing

Many table games come with a preconfigured number of players, which is indicated on the game top. However, the play experience can be impacted, positively or negatively, when all the seats are full. Casinos need to decide at what point they want to open more tables in order to spread out the number of players, or if it's better to concentrate the players at a few tables

Since the table games are dealer driven, the casino has some control over the speed of the game. The faster the dealer manages a round, the more rounds per hour and the more revenue that can be generated.

refers to the process of making large amounts of cash that were obtained illegally (like through drug deals, black market transactions, bribery, etc.) appear to come from a legitimate source to avoid government detection

refers to the act of illegally taking money from the casino, or taking money under faise pretenses, which makes it a slightly different problem than money laundering

can be as simple as trying to pass a bad check or cash in fake chips. They can also get infinitely more complicated, the organizing rings to cheat at casino games

can be used to calculate expected revenue from the casino floor or for any individual player.

similar to the term “decisions per hour” in the previous metric. It matters here how fast the game is played—whether it’s hands per hour, rolls per hour, spins per hour—the faster the dealer or machine operates, the faster the patrons lose, and the more money the casino makes.

The gaming industry has been expanding and evolving since the late nineties. Traditionally, casinos were located in destination markets like Las Vegas, but recently the number of local markets has been increasing dramatically, as states within the United States, and increasing numbers of countries outside of it, begin to issue gaming licenses.

Compared to a slot machine, a table game in general is capable of generating more revenue in the same period of time because of the size of the bets, the capacity of players per space, and the ability to raise and lower rates

All models are wrong... but some are useful!

In this model, analyst groups are associated with business units and corporate functions. There is no corporate reporting or consolidated structure for analytics

Analysts are close to the business, so they can be very responsive. They are also likely to learn the nuances of the business very quickly

It is difficult to set analytics priorities across the organization. It is also difficult to develop staff, and take advantage of specialized skills sets across the organization. There is almost always a lack of communication between analytics groups in this model.

This type of organization is best fit for a diversified corporation where the multiple businesses have very little in common. Reading between the lines, it's probably not the best option for a hotel or casino company, although many are set up this way currently due to organic or grassroots growth.

In this model, analysts are all part of one corporate organization. They may be assigned to business units or functional areas, but they report to the corporate unit, and the corporate unit sets strategy and priorities

It is easy to invest in specialized resources because they can be deployed for key projects across the organization. Supporting high priority projects is also facilitated in this model, as it is easy to reprioritize and deploy analysts wherever they are needed. Analysts can build a community and gain new skills, it is generally easier to recruit talent, because there is a demonstrated corporate commitment and an established community to join

Distance can be created between analysts and the business problems if the analysts are all located at corporate

It is easiest to deploy this model if there is already an organizational precedent for shared services at corporate. If there isn't an existing awareness of the value of analytics, and the benefits of working with this group, the group may not have enough "work" from the field to sustain operations Further, it may be difficult to work with lines of business who feel as though analytics are being "done to them" instead of controlled by them

in this model, there is one major analytics group in the organization that reports into a business function that is the primary consumer of analytics. This unit may also act as consultants for the rest of the organization. In hotels, the revenue management function may behave this way today. For casinos, it could be the casino marketing group

Analytics are deployed against key business initiatives, and the analysts stay really close to the business, so they can be highly responsive and build local knowledge. However, they are also available to support projects in other areas as they arise.

Opportunities to apply analytics in other functional areas may be missed with this laser focus on one functional area.

For organizations that are just starting on their analytics journey, this model could be good. It provides focused attention on high-valued business initiatives, but there's some flexibility to solve other problems. Additional focused groups could be added as new projects are identified, or the group could be redeployed

Analysts work for a central organization, and business units "hire" analysts to work on their projects. This is a bit different than the centralized version because here the analytical priorities are set by the lines of business as opposed to centrally.

Key analytic resources are positioned to solve problems across the organization. The right fit skill sets can be deployed against key projects. Analysts who are deployed to lines of business can build close relationships with decision makers

If enterprise focus and prioritization activities are weak, analysts may not be deployed on the projects that deliver the most value, but instead become focused on the projects for whatever line of business executives yell the loudest.

The analytics organization must understand the value of the work provided, and be able to set priorities. It will be crucial to market and sell to internal clients to keep building the project pipeline

This model is a collection of decentralized analytics groups that report to business units, but have formal ways to collaborate. This could be a steering committee or a chartered enterprise governance committee, but there is an organization that ties the groups together

This model has an immediate enterprise view, with coordination on priorities, initiatives, resource deployment, and analyst development

Committees may lack clout in the organization. It could be difficult to establish standards, set priorities, and share resources for corporate analytics initiatives

This model works well in large and complex organizations where business units share some, but not all, things in common

Decentralized analyst groups are embedded in lines of business, but are also members of a central coordinating structure that builds a community of analysts at the enterprise level

This structure builds a community of analysts that can share experiences and best practices, and creates opportunities for learning and development

This is a less formal arrangement than the federated model, so this group, while good for best practice sharing, rarely has the power to assess corporate analytics needs, prioritize projects, and manage analyst career paths.

Organizations can adopt this model as a first step on the path toward a centralized, consulting. or federated model. It is useful for organizations that want to promote community learning and development, but don't yet have a mandate for any corporate level initiatives

Our industry is at a crucial inflection point where analytical talent must keep pace with technology (and vice versa) to be successful

the enabling technologies for analytical automation are introduced department by department. Users become accustomed to incorporating system results into their decision making. As the analytical culture grows in these departments, the enterprise begins to prepare for the organizational and cultural changes required for more holistic and synchronized decision making