Methods and systems for accessing multi-dimensional customer data

• US 6,901,406 B2
• Filed: 12/29/2000
• Issued: 05/31/2005
• Est. Priority Date: 12/29/1999
• Status: Expired due to Term

• Current Assignee: GE Capital US Holdings, Inc. (General Electric Company)
• Original Assignee: General Electric Capital Corporation (General Electric Company)
• Inventors: Souery, Michael A., Nabe, Oumar
• Primary Examiner(s): Le, Uyen
• Assistant Examiner(s): CHEN, TE Y
• Application Number:
  US09/751,859
• Publication Number:
  US 20020049701A1
• Time in Patent Office: 1,614 Days
• Field of Search: 707/1, 707 3- 4, 707/10, 707100-1041, 707203-204, 709250-252, 717/3, 717/180, 717/170, 705 36- 38, 706 21- 23
• US Class Current: 707/694
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: Y10S 707/954 Relational
• CPC Class Codes: Y10S 707/957 Multidimensional
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

Methods and systems for modeling customer data into a multi-dimensional structure for access to enable efficient customer targeting are described. The method includes the steps of compiling data from multiple sources to create a relational database, using tools to model data within the relational database, scoring the modeled data, integrating scores into a multi-dimensional structure and providing access to end users to the multi-dimensional structure.

Claims

1. A method for providing to an end user, multi-dimensional customer profiles, allowing the end user to effectively manage customer targeting, said method comprising the steps of:

compiling data from multiple sources to create a relational database;

using tools to model the relational database and produce a first modeling result including at least one marketing model and at least one risk model for a customer, wherein the at least one marketing model includes a net present value/profitability model, a prospect pool model, a net conversion model, an attrition model, a response model, a revolver model, a balance transfer model, and a reactivation model, the tools include non-statistical tools including artificial intelligence;

using the tools to compare the first modeling result to prior modeling results, and then select a modeling result to facilitate customer targeting;

scoring the modeled database using the selected modeling result;

integrating scores into a multi-dimensional structure; and

providing access to end users to the multi-dimensional structure.

2. A method according to claim 1 wherein said step of compiling data from multiple sources further comprises the step of compiling data from at least one of world wide web, legacy customer data, and user input.

3. A method according to claim 1 wherein said step of using tools to model the relational database further comprises the step of using statistical analysis software to model the relational database.

4. A method according to claim 1 wherein said step of using tools to model the relational database further comprises the step of using non-statistical tools to model the relational database.

5. A method according to claim 4 wherein said step of using non-statistical tools to model the relational database further comprises the step of using at least one of rule based methods and user input to model the relational database.

6. A method according to claim 1 wherein said step of using tools to model the relational database further comprises the step of using the tools to generate an algorithm for use in scoring customer accounts stored within the relational database.

7. A method according to claim 1 wherein said step of scoring the modeled database further comprises the step of scoring each customer account stored within the relational database, and assigning each account at least one of a numerical value, a non numerical value, and an economic worth.

8. A method according to claim 7 wherein said step of scoring each customer account and assigning each account further comprises the step of assigning accounts having an assigned non-numerical value to at least one of classes, deciles and clusters.

9. A method according to claim 1 wherein said step of integrating scores into a multi-dimensional structure further comprises the step of defining and calculating dimensions of the structure.

10. A method according to claim 1 wherein said step of integrating scores into a multi-dimensional structure further comprises the step of transforming modeled data into information for decision making.

11. A method according to claim 1 wherein said step of providing access to end users further comprises the step of accessing information within the multi-dimensional structure through a graphical user interface.

12. A method according to claim 1 wherein said step of providing access to end users further comprises the step of generating at least one of reports, graphs and tracking results.

13. A method according to claim 1 wherein said step using tools to model the relational database further comprises the step of using tools to model the relational database to produce a plurality of marketing models and a plurality of risk models for a customer, wherein the marketing models include net present value/profitability model, a prospect pool model, a net conversion model, an attrition model, a response model, a revolver model, a balance transfer model, and a reactivation model, and wherein the risk models include a payment behavior prediction model, a delinquency model, a bad debt model, a fraud detection model, a bankruptcy model, and a hit and run model.

14. A method according to claim 1 wherein said step using tools to model the relational database further comprises the step of using tools to produce at least one marketing model and at least one risk model for a customer, wherein the at least one risk model includes a payment behavior prediction model, a delinquency model, a bad debt model, a fraud detection model, a bankruptcy model, and a hit and run model.

15. A system configured to provide to an end user, multi-dimensional customer profiles, allowing the end user to effectively manage customer targeting, said system comprising:

at least one computer;

a server configured to compile data from multiple sources to create a relational database, use tools including artificial intelligence to model data within the relational database and produce a first modeling result including at least one marketing model and at least one risk model, use tools to compare the first modeling result to prior modeling results and then select a modeling result to facilitate customer targeting, score the modeled data using the selected modeling results, integrate the scores into a multi-dimensional structure and provide access to the multi-dimensional structure, wherein the at least one risk model includes a payment behavior prediction model, a delinquency model, a bad debt model, a fraud detection model, a bankruptcy model, and a hit and run model; and

a network connecting said computer to said server.

16. A system according to claim 15 wherein said server configured to compile data from at least one of world wide web, legacy customer data, and user input.

17. A system according to claim 15 wherein said server configured to use statistical analysis software to model data within the relational database.

18. A system according to claim 15 wherein said server configured to use non-statistical tools to model data within the relational database.

19. A system according to claim 18 wherein said server configured to use at least one of rule based methods and user input to model data within the relational database.

20. A system according to claim 15 wherein said server configured to generate an algorithm for use in scoring customer accounts stored within the relational database.

21. A system according to claim 15 wherein said server configured to score each customer account stored within the relational database, and assign each account at least one of a numerical value, a non numerical value, and an economic worth.

22. A system according to claim 21 wherein said server configured to assign accounts having an assigned non-numerical value to at least one of classes, deciles and clusters.

23. A system according to claim 15 wherein said server configured to define and calculate dimensions of the structure.

24. A system according to claim 15 wherein said server configured to transform modeled data into information for decision making.

25. A system according to claim 15 wherein said server configured to allow access to information within the multi-dimensional structure through a graphical user interface.

26. A system according to claim 15 wherein said server configured to generate at least one of reports, graphs and tracking results.

27. A system according to claim 15 wherein said at least one marketing model includes a net present value/profitability model, a prospect pool model, a net conversion model, an attrition model, a response model, a revolver model, a balance transfer model, and a reactivation model.

28. A system according to claim 15 wherein said server is further configured to use tools to model data within the relational database to produce a plurality of marketing models and a plurality of risk models for a customer, wherein the marketing models include a net present value/profitability model, a prospect pool model, a net conversion model, an attrition model, a response model, a revolver model, a balance transfer model, and a reactivation model, and wherein the risk models include a payment behavior prediction model, a delinquency model, a bad debt model, a fraud detection model, a bankruptcy model, and a hit and run model.