The present invention relates generally to Data Warehouse solutions, and more particularly, to systems and methods for capturing, storing and using detailed transaction and interaction information for the casino and gaming industries. Still more particularly, the present invention is related to a data model to logically model the key business information needs of casinos and gaming venues from an enterprise perspective.
The Enterprise Data Warehouse (EDW) has proved a strategic weapon for most modern organizations. It should be active, dynamic and flexible in order to cope with changing business requirements. It should provide a strategic background to support changing consumer-provider relationships.
The foundation of the enterprise data warehouse is a comprehensive and responsive logical data model addressing challenges in the near future without compromising existing business processes. A logical data model is a graphical representation of the way data is organized in a data warehouse environment. The logical data model specifically defines which individual data elements can be stored and how they relate to one another to provide a model of the business information. The data model ultimately defines which business questions can be answered from the data warehouse and thus determines the business value of the entire decision support system.
A properly designed LDM provides a foundation for more effective sales, marketing and customer management and supports the customer relationship management (CRM) requirements related to identifying, acquiring, retaining and growing valuable customers. A logical data model for the casino and gaming industries reflects the operating principles and policies of these industries and provides the underlying structure for the data imported into the data warehouse, providing. The enterprise data warehouse, organized in accordance with this logical data model, provides support to an enterprise's critical business decision-making needs, and real-time analysis of customers and casino activity.
The present invention is illustrated by way of example, and not by limitation, in the Figures of the accompanying drawings, wherein elements having the same reference numeral designations represent like elements throughout and wherein:
A travel and hospitality industry customer-centric warehouse is established on the Teradata Scalable Data Warehouse 101 as defined by the logical data model (LDM) described below. The logical data model is a consumer-centric data model supporting Revenue Management, Financial Management, Customer Relationship Management, Privacy and Click Stream analysis. It can serve as the base for a full enterprise data warehouse implemented at the client's site. The model has been designed in a modular fashion so non-relevant components can be removed without impacting the consistency of the model. It's an integrated, subject-oriented base of strategic business information that serves as a single source of decision support, providing the travel and provider with the ability to make simple reports or sophisticated information analysis.
As stated earlier, a properly designed logical data model provides a foundation for more effective sales, marketing, and operations management and supports the customer relationship management requirements related to identifying, acquiring, retaining and growing valuable customers.
A logical data model (LDM) is an abstract construct that is physically realized in the database or data warehouse. The data model provides an architecture for the information that will be included in a data warehouse. The database provides the physical realization of that architecture in a form that can be efficiently maintained and used. There may well be some differences between the logical data model and the final database design. The database may include some tables (summary tables, etc.) or columns that have no direct correlation in the logical data model. Elements in the logical model may be grouped differently in the physical database.
A logical data model is organized by Subject Areas, each comprised of numerous Entities, Attributes and Relationships. The data model hierarchy includes one or more Subject Areas. Each Subject Area includes one or more Entities or Tables, each having Attributes and Relationships. Each Attribute describes a fact about an Entity. Relationships between two or more Entities are further defined by Cardinality. The Relationships define which entities are connected to other entities and the cardinality of the relationships. Each of these elements will be described in greater detail below.
A subject area is a subset of objects taken from the universe of data objects for a particular line of business or industry that focus on a particular Business Process. Typically, a subject area is created to help manage large data architectures that may encompass multiple business processes or business subjects. This is the highest-level data concept within a conceptual entity/relationship (E/R) model. Working with subject areas is especially useful when designing and maintaining a large or complex data model. Dividing the enterprise into several distinct subject areas allows different groups within an organization to concentrate on the processes and functions pertinent to their business area.
An Entity represents a person, place, thing, concept, or event (e.g. PARTY, ACCOUNT, PRODUCT, etc.). It represents something for which the business has the means and the desire to collect and store data. An Entity must have distinguishable occurrences, e.g., one must be able to uniquely identify each occurrence of an entity with a primary key (e.g. Party Identifier, Account Identifier, Invoice Number, etc.). An Entity is typically named with a unique singular noun or noun phrase (e.g., PARTY, TRANSPORTED PASSENGER, etc.) that describes one occurrence of the Entity and cannot be used for any other Entity. It should be exclusive of every other Entity in the database. An Entity cannot appear more than once in the conceptual entity/relationship (E/R) model. Each Entity may have relationships to other Entities residing in its own Subject Area or in other Subject Areas.
An Attribute is a data fact about an Entity or Relationship. It is a logical (not physical) construct. It is data in its atomic form. In other words, it is the lowest level of information that still has business meaning without further decomposition. An example would be FIRST NAME, or LAST NAME. An example of an invalid attribute would be PERSON NAME if it includes both the first and last names, as this could be further decomposed into the separate, definable (first name, last name) data facts.
A Relationship is an association that links occurrences of one or more Entities. A Relationship must connect at least one Entity. If only one Entity is connected, the Relationship is said to be Recursive. A Relationship is described by a noun or passive verb or verb phase that describes the action taken in the Relationship. A Relationship represent a static state of being between the occurrences of the Entities it connects. Relationships are not intended to represent processes or data flows. They cannot be linked to another Relationships. They may optionally represent future, present, and/or past relatedness. The time frame must be explicitly defined in the data definition. Relationships may contain attributes. In a normalized model, a Relationship containing Attributes will result in the creation of an Entity.
In order for a data model to be considered accurate, it must contain both the maximum and minimum number of Entity occurrences expected. This is controlled by rules of cardinality, which describes a relationship between two Entities based on how many occurrences of one Entity type may exist relative to the occurrence of the other Entity. Typically, it is a ratio, commonly depicted as a one-to-one (1:1); one-to-many (1:N); and many-to-many (M:N) relationship.
The maximum cardinality may be an infinite number or a fixed number but never zero. The minimum cardinality may be zero, or some other positive number, but it must be less than or equal to the maximum cardinality for the same relationship.
The logical data model for the E-Business will now be described in more detail. The logical data model uses IDEF1X modeling conventions, as shown in Table 1.
Relationship and cardinality conventions are shown in Table 2.
The Travel and Hospitality Data Model (LDM) is a large data model composed of a large number of tables. To effectively view and understand the data model, the data tables have been logically organized into smaller groups called subject areas. Each subject area is comprised of a set of tables that contain information relevant to a particular entity. In addition, the subject areas address particular business questions.
The Travel and Hospitality Data Model Logical Data Model is presented in a conceptual view in
The Conceptual View is derived directly from the Travel and Hospitality Data Model Logical Data Model by selecting the most important entities from every subject area, being sure that at least one entity from each subject area was selected and distilling the relationships among the selected entities, while still maintaining the general nature of the way the entities relate to each other. During this process, intervening entities were abstracted into relationships. Many-to-many relationships were used where appropriate. Several entities shown in the Conceptual View represent a subject area or combination of entities within a subject area. The result is a simple, easy to understand diagram that conveys the general content of the underlying logical data model.
For ease of use and understanding, the Travel and Hospitality Logical Data Model has been divided into numerous subject areas identified in Appendix A.
Casino operators have a real need to be able to monitor their environments. This includes the capability to know which customers are currently in-house and what gaming they are engaged in. All of the equipment such as slot machines and table games must provide a detailed view of current activity and provide for historical trends and statistics. VIP customers must be immediately recognized and catered to according to their value to the Enterprise and their gaming maturity. Faulty or failing equipment must be identified and quickly repaired. Customer Relationship Management capabilities must provide for customer compensation awards and usage and for loyalty rating and points. Sweepstakes management permits customers to participate using a variety of methods. Casinos have a pressing need to monitor and analyze a myriad of details that stem from the gaming activities which include customers' game and table preferences, wagering strategies, wins, jackpots and losses, sweepstake entries and wins, buy-ins, pay-outs, turnover, theoretical statistics against which to measure customer and casino performance and many more such types of key business information. Following are some key issues which Casinos must understand to bring the best value to the business.
Casinos must understand the true profitability of each patron from a multi-dimensional view. That means understanding the gaming behavior of different patrons so a patron can be compared to profitable segments and a determination made as to how to raise their profitability to the casino. This involves understanding their gaming activity at the casino, the cost to the casino in terms of comps and other marketing costs to get the patron to visit the casino and finally understanding their non-gaming spend while on the property. This area enables casino operators to better understand the attributes of their highest value patrons and ensure that they receive the highest level of service and attention. This allows the casino operators to optimize labor skills and allow scheduling around peak high value patron activity periods. By understanding the gaming and non-gaming profiles of their best customers, the casinos can also target other lower value patrons and work to move them toward these higher profit activities.
Measurement involves a detailed analysis of the types of games played, services utilized, number of games played, time spent per session by patrons and tender utilized for each patron visit. In addition, all non-gaming activity and spending activity will be captured so detail analysis can take place. A visit profile will be developed for each patron visit based on frequency, probability and profitability of each game mix and non-gaming activities. Listed below are some examples of the analysis with this approach:
The casino operator needs to determine the factors that lead to the defection of its top patrons. Once these factors are determined, data mining techniques can be utilized to see if these factors are influencing any current high-level patrons to identify them as potential candidates for defection. Detailed analysis like the examples below can be performed to understand the prospects to target for retention:
The casino needs to optimize its marketing efforts. It should promote games that drive the highest margin visits by patrons and eliminate games and services that drive low-margin visits by patrons. Casinos have a need to perform a detailed analysis of the types of games played, services utilized, number of games played, time spent per session by patrons for each patron visit in response to a promotion. The Model needs to show game mix played per visit compared to target game mix analyzed and promoted and provide input to promotion effectiveness by providing promotional impact on frequency of play, affinity of promo game to non-promoted game for each patron responding to a given promotion. Listed below are some examples of the promotional analysis with this approach:
The casino operator needs to identify potential high value patrons and understand how best to attract them to the casino. They will be able to do the following:
The Model enables detailed analysis such as the examples shown below that can then be performed to understand the best new prospects to target as future patrons:
The benefit of Slot Optimization is higher margin on slot machines. It allows the casino operator to populate the casino floor with the machines that patrons want to use most while negotiating the most favorable terms from the slot machine vendors. The overall benefit will be higher producing slot machines with lower overall costs which mean increased slot machine profitability.
The casino operator needs to analyze performance of slot machines by various slot attributes and identify which slot machines are candidates for deletion from the casino floor. These means collecting detail data on each slot machine's performance from a variety of perspectives. Some examples of this slot analysis are:
In order to negotiate better terms with Slot Machine vendors, the casino company needs detail data regarding the performance of the company's slot machines. Some examples of Slot Manufacture analysis follows:
Key gaming content in the Model includes detailed support for the following:
Casino Group Events and Junkets. Customers are provided travel arrangements to visit a Casino from a distant city or cities for a specified series of dates. The Casino expects to gain revenue from the events and have expenses, commissions and other event parameters that help to define their expected Net profit. The Group Events and Junkets become part of the Casino and Gaming Model history and such information may be used in future for Marketing and Analysis.
Detailed Player Gaming Session History per Round. Whenever a gaming customer plays at a slot machine, table game or other type of gaming activity, the details of his play are placed into The Model to provide the Casino the capability of analyzing all of the activities that occurred. The Model provides for a “per round” level of detail that includes the type of game being played and the specific machine or table being used, the sequence of and total number of rounds played, all information about wagers and adds, jackpots, etc. Casinos can focus on player techniques and wagering tendencies as well as support a well-rounded management of the Customer Relationship with the Casino.
Detailed Player Gaming Session History. As well as a per Round perspective, The Model also provides a higher level of aggregation for the “game session.” These statistics provide even more capabilities for business analysis that gives keen insight into the long-term trends that affect the Casino's profitability and Customers' marketability. At this level The Model provides details of customer session Wins and Losses including Jackpots, Buy-in, Pay-out, Turnover, Theoretical and Average Wins, Lifetime Wins, Losses, Comps and loyalty earnings as well as the Casino Rating of each Customer. The Model includes game configuration particulars such as manual payments of Jackpots, Hopper Fills, Game Type and specific Configuration in use at the time of each session.
Detailed Player Accounting. A detailed accounting of each Player (customer) is provided in The Model which tracks all money transactions including Marker Issues (linked to specific gaming sessions), Debt Repayment or Written Off, Front Deposits, approvals of Credit with specified Limits and Collateral and a Balance History.
Player Comps (Compensation). During play or as part of a larger Promotional effort, Casinos provide compensation to Customers in the form of “comps” (a reward) to encourage the Customers' continued patronage. Comps may be “points” given and recorded in the Customer's Loyalty Account or as a physical coupon or other ticket format. The comps may be drawn upon to pay for various activities or services offered by the Casino and may be part of the Customer's Folio Account. All details are recorded in The Model in the form of related transactions.
Multiple Configurations of Slot and Table Games. In order to maximize their Customer's enjoyment and the Casino's profits, all gaming machines and tables may be configured in different ways. Slot machines may offer different games and game types and may offer (or not) Progressive game play. Settings may be changed “on the fly” in some machines or manually in others providing the Casino a means to record different Theoretical Percentages, Maximum and Minimum wagers, Denomination, Number of Players per game or per the House and all recorded in The Model keeping all changes over time for immediate and future analysis.
Player Alert. Casinos need to know about their Customers' voluntary Disassociations or other bans on Customers imposed by the Casino or the State or Territory in which the Casino is located. In addition to supporting the Casino's need to comply with local regulatory bodies, the Casino may also record any other types of alerts that apply to Customers such as bad Markers, Returned Checks and Limitations. The model provides for historical details giving the Casino the ability for immediate or long-term analysis of Customer status.
Equipment Instances. When gaming machines or tables are placed into service, the Casino must track each one separately over the life of its operations in the specific location to which it is allotted within the Casino. The layout of games throughout the Casino may be reconfigured as needed and faithfully recorded in The model complete with historical details providing both instant and long-term analysis of games. This permits the Casino to find which locations and machines bring the most profit and/or Customer enjoyment thus helping to maximize the overall operational plan. This area of interest records many historical details of game machines, their configurations and Themes, the Manufacturers, Series and Serial Numbers, and In-Service and Retire dates.
Equipment Maintenance. It is critical for a Casino to know not only where all gaming equipment is located within the Casino, but also which machines are out-of-service or have been “flagged” as broken and when they will be returned to service. The Model permits real-time notification of Customer-Identified failures (available as a “call” button on some machines) and also tracks equipment that is pulled out-of-service, who repairs the equipment and why and how long the machine was “down” for repairs.
Near Real-Time Game Session Monitoring. In modern Casinos is often found a security center that runs a full-time monitoring of all Customers and Game play that occurs on the premises. The Model provides for great detail surrounding each Game Session (at either Slots or Tables) which is necessary for the Casino to monitor its revenue and gaming session details. Detailed data outlined further may be aggregated at regular, flexible intervals which allow the spotting of trends, anomalies, special circumstances, VIP customers gaming progress, etc. This area tracks such data as Game Configurations, Player Counts, Minimum, Maximum and Average Bets, Jackpots, Number of Rounds Played, Theoretical and Actual Win Amounts, Buy-in, Turnover and Payout, Wagers and Adds, Statistics, Round and Session Start and End dates and times. All of this data may be viewed per Gaming Machine or Table or aggregated as needed for advanced analysis.
Sweepstakes. In order to effectively market to its Customers, Casino offer promotions in the form of Sweepstakes which customers may enter. The Model records overall campaign details such as expected Revenue (the Goal), the specific Promotion being offered, relationship to specific Gaming Sessions and Customer Visit as well as linkages to Loyalty Accounts maintained for the Customers' benefits.
Incorporation of the foregoing, key subject matter provides a strong and varied business information foundation for the casino and gaming venue. The Travel and Hospitality Logical Data Model includes subject areas developed to logically model these key business information needs. These subject areas, as well as the entities included within the subject area, are illustrated in
The subject areas modeling the key business information needs of casinos and gaming venues, and shown in
ACCOUNT (DEFINITION) Subject Area, illustrated in
ACCOUNT (LOYALTY) Subject Area, illustrated in
ADDRESS Subject Area, illustrated in
ADDRESS (GEOGRAPHY) Subject Area, illustrated in
ASSOCIATE LABOR Subject Area, illustrated in
CASINO (OVERVIEW) Subject Area, illustrated in
CASINO COMPS Subject Area, illustrated in
CASINO GAME SESSIONS Subject Area, illustrated in
DEMOGRAPHICS Subject Area, illustrated in
EQUIPMENT Subject Area, illustrated in
HOSPITALITY GROUP EVENT Subject Area, illustrated in
LOCATION (OVERVIEW) Subject Area, illustrated in
PARTY Subject Area, illustrated in
PRIVACY Subject Area, illustrated in
PROMOTION Subject Area, illustrated in
QUALITY FEEDBACK Subject Area, illustrated in
TIME PERIOD Subject Area, illustrated in
WEB VISIT Subject Area, illustrated in
The core areas of interest in modeling the business information needs of casinos and gaming venues are the casino specific diagrams of the CASINO (OVERVIEW), CASINO COMPS, CASINO GAME SESSIONS, and EQUIPMENT (CASINO) subject areas shown in
A listing of all the entities included within the logical data model, and those included in the subject areas illustrated in
This application claims priority under 35 U.S.C. §119(e) to the following co-pending and commonly-assigned patent application, which is incorporated herein by reference: Provisional Patent Application Ser. No. 61/018,154, entitled “SYSTEM AND METHOD FOR CAPTURING AND STORING CASINO INFORMATION IN A RELATIONAL DATABASE SYSTEM”; filed on Dec. 31, 2007 by Pieter Lessing, David W. Hubbard, and Mark L. Crosby. This application is related to the following co-pending and commonly-assigned patent applications, which are incorporated by reference herein: application Ser. No. 10/027,967, entitled “SYSTEM AND METHOD FOR CAPTURING AND STORING BUSINESS INFORMATION FOR THE TRAVEL AND TRANSPORTION INDUSTRIES”; filed on Dec. 21, 2001 by Pieter Lessing, William Black, John Kumar, David Hubbard, and Kim Nguyen-Hargett; application Ser. No. 10/888,765, entitled “SYSTEM AND METHOD FOR CAPTURING, STORING AND ANALYZING REVENUE MANAGEMENT INFORMATION FOR THE TRAVEL AND TRANSPORTATION INDUSTRIES”; filed on Jul. 9, 2004 by Pieter Lessing, David W. Hubbard and Sreedhar Srikant; and application Ser. No. 11/016,002; entitled “SYSTEM AND METHOD FOR CAPTURING, STORING AND ANALYZING TRANSACTION AND INTERACTION INFORMATION FOR THE HOSPITALITY AND GAMING INDUSTRIES”; filed on Dec. 17, 2004 by Sreedhar Srikant, Norman C. Nicholl, Gregory P. Churak, and Pieter Lessing.
Number | Date | Country | |
---|---|---|---|
61018154 | Dec 2007 | US |