Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/10—Office automation; Time management
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/24—Querying
  • G06F16/245—Query processing
  • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
  • G06F16/2462—Approximate or statistical queries

Definitions

• the invention relates to an analysis of user data structured according to a database structure, such as customer or product data of a company. 0
• C RM customer relationship management systems
• SCM supply chain management systems
• data warehouses data warehouses
• Each data record Di represents a specific object from a group of objects, for example a specific customer from all registered customers of a company or a specific product from a product line of a company.
• Each data record comprises a predeterminable number of entries, Ai, Bi, Ci, ..., the individually recorded data, with categories or attributes A, B, C, ... These categories or attributes represent properties of an object group pe, such as age (A), income (B), product purchased (C), ....
• the entries Ai, Bi, Ci, ... for the respective categories A, B, C, ... can be numerical or semantic.
• a well-known and frequently used data mining process is a so-called decision tree [5].
• a disadvantage of many of the known and mentioned analysis methods is that they cannot be used adequately when analyzing large amounts of data. As a rule, there is one or multiple access to the whole Analyzing data stock, which is stored, for example, in a database, is necessary.
• a determination of a common probability model P (A, B, C, ..., X) for a data structure (A, B, C, ...) based on a hidden variable X is known from [7].
• the invention is based on the object of specifying an analysis method for the analysis of structured useful data which can also be used with large amounts of useful data and also has a high performance there.
• a common statistical probability model is first determined for the user data structured according to the database structure.
• the user data structured according to the database structure is then analyzed using a statistical analysis method, the statistical analysis method used in the analysis being applied to the common statistical probability model, not, as is customary, directly to the output data.
• the arrangement for analyzing user data structured according to a database structure has:
• a modeling unit with which a common statistical probability model for the user data structured according to the database structure can be determined
• the invention is clearly based on a two-stage procedure.
• the first step is to assume user data that can be specified and structured according to a database structure.
• a database structure of this type should be understood to mean that the user data is based on a higher-level, fixed structure, for example, data sets with the same structure (Ai, Bi, Ci, ...) with the same entry categories A, B, C, .... Such structures are generally known.
• a common, purpose-independent probability model as described, for example, in [7], [8], is formed from these user data to be analyzed and structured according to a database structure.
• the computer program according to the invention with program code means is set up to carry out all steps according to the analysis method according to the invention when the program is executed on a computer.
• the computer program product with program code means stored on a machine-readable carrier is set up to carry out all steps according to the analysis method according to the invention when the program is executed on a computer.
• the arrangement and the computer program with program code means, set up to carry out all steps according to the inventive analysis method when the program is executed on a computer, and the computer program product with program code means stored on a machine-readable medium, set up all steps according to the Carrying out analysis methods according to the invention when the program is executed on a computer are particularly suitable for carrying out the analysis method according to the invention or one of its further developments explained below.
• structured user data are used in user data records, for example user data records from a database.
• Each user data record represents a specific object from a group of objects.
• the user data associated with the respective user data record describe properties of the respective object.
• the statistical analysis method is applied to the common statistical probability model in such a way that a common probability is used as an input variable for the statistical analysis method. driving is used.
• the common probability results directly from the common probability model. This avoids unnecessary intermediate steps, costs computing time and extends response times.
• a method based on a data mining method [4], [10], [11], [12] can be used as the statistical analysis method, for example a clustering method [5] or a decision tree [6] or association rules [9].
• the analytical database image i.e. the common probability model is newly formed at predefinable time intervals, such as daily or weekly. Education can take place at night or on weekends.
• the complete analytical database image is then available as required to significantly speed up analyzes.
• the user data can be obtained from various data sources. The easiest way is to obtain the user data from a database in which the user data are stored and from which they are read. Because of the performance it can achieve in the analysis of data, the invention is particularly suitable where large amounts of data have to be processed or analyzed, such as in the area of customer relationship management (CRM) [1] or supply chain management [2]. or a data warehouse (DW) [3].
• CRM customer relationship management
• DW data warehouse
• the object is a customer, which is described by at least two of the following properties, age, income, product purchased, date of purchase, frequency of purchases.
• This enables eminently important issues to be solved for marketing departments, such as the customer behavior of certain customer groups.
• targeted target groups can be determined when customers are acquired, customer groups can be selected for specific products and marketing campaigns, and customers can be served with more foresight.
• Figure 1 sketch that schematically shows how a
• Figures 2a to g sketches showing analysis results of an analysis system for analyzing customer data according to an embodiment.
• Execution example Analysis system for analyzing customer behavior at a bank based on a customer relationship management system
• the exemplary embodiment relates to an analysis system for analyzing customer data from a bank.
• analysis system described below can be used not only at banks, but also at any company for the analysis of corresponding company data, such as for example in department stores or manufacturing companies.
• FIG. 1 shows schematically the functioning 100 of the analysis system for analyzing bank customer data 110.
• Functionality 100 is divided into knowledge acquisition 101 and implementation of the knowledge into intelligent operation of bank customers 102.
• customer data 110 Large and thus difficult-to-handle amounts of customer data 110 are first condensed 111 into a statistical model 112, a common probability model, of customer behavior.
• the shared probability model 112 used is one based on a hidden variable. The basics are described in [7].
• statistical methods 120 in general data mining methods and here in this case a decision tree, are used, which are based on the statistical model.
• the coupling is made possible by the fact that the data mining methods or decision tree 120 are based on a statistical framework, and thus use the same statistical terms or the same statistical language as the common probability model 112.
• the results of the questions can be further implemented 121 in intelligent customer service 130.
• Customer data ((Fig. 1, 110)
• the customer data 110 in the analysis system is collected as part of a customer relationship management (CRM) 150.
• CRM customer relationship management
• CRM 150 large amounts of data 110 about the bank customers from all sales channels of the bank, such as direct contacts, web, call center, are recorded and stored.
• the data is stored in a database in the form of customer-specific data records Di (AI, A2, ..., Bl-2, B2-3, ..., C, D, ...), the index i being the respective bank customer i features.
• the common probability model 112 used is one based on a hidden variable X. The foundations for this are described in [7].
• the common probability model 112 is written based on the hidden variable X as P (A, B, C, ..., X) for all attributes (A, B, C, ).
• Such a statistical image of data represents a highly compressed form of knowledge about customers and can be used to efficiently and interactively explore dependencies 120, 140.
• the common probability model 112 also provides quickly retrievable forecasts of further expected behavior and current needs of a customer.
• the forecasts can also be used to serve customers proactively and in a targeted manner and to provide proactive, personal offers 130.
• the decision tree [6] is placed 120 on the statistical model 112, the common probability model 112.
• the common distribution P (A, B, C, ..., X) goes over all attributes of the customers by summation over the hidden variable X.
• Structural learning immediately provides a common distribution P (A, B, C, ).
• the structure of the models for example those with a predefined hidden variable or those that were generated by structure learning, or a combination of the above, is used to efficiently calculate necessary sums over the common distribution.
• Decision trees are usually built using a known CHAID or a known CART procedure.
• the necessary probabilities or distributions for the construction of the decision tree can (as usual) be determined from the data or also from the most accurate probability model described in the above (inference process).
• 2a to 2g show examples of some of the possible interactive analyzes 140 that can be carried out with the decision tree 120 using the common probability model 112.
• A1 " Giro / Salary Account “), P (A3
• A1 " Giro / Salary Account ”) , P (A4
• A1 "Giro / Salary Account”), P (A5
• A1 "Giro / Salary Account”), P (B1-2
• A1 "Giro / Salary Account ⁇ ), P ( B2-3
• A1 "Gi ro / Salary Account “), P (B3-4
• A1 " Giro / Salary Account ”) and P (C
• A1 " Giro / Salary Account ”) and P (D
• A1 " Giro / Salary - Account “).
• P (A2 "insurance product
• Fig. 2e shows the probability distributions P (A1), P (A2) P (A3) P (A4), P (A5), P (Bl-2), P (B2-3), P (B3-4) and P (C) and P (D).

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• 2003
  • 2003-09-02 AU AU2003264251A patent/AU2003264251A1/en not_active Abandoned
  • 2003-09-02 EP EP03794981A patent/EP1629401A2/fr not_active Withdrawn
  • 2003-09-02 US US10/526,160 patent/US20060173889A1/en not_active Abandoned
  • 2003-09-02 WO PCT/EP2003/009752 patent/WO2004025501A2/fr active Application Filing
  • 2003-09-02 JP JP2004535418A patent/JP2005537585A/ja active Pending

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