Process and System for Integrating Information from Disparate Databases for Purposes of Predicting Consumer Behavior

• US 20090182625A1
• Filed: 01/02/2009
• Published: 07/16/2009
• Est. Priority Date: 12/30/1998
• Status: Active Grant

• Current Assignee: GfK US MRI, LLC
• Original Assignee: Experian Marketing Solutions Incorporated (Experian plc)
• Inventors: Kilger, Max F., Engel, William E.
• Granted Patent: US 8,200,525 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 16/27 Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

A process and system for integrating information stored in at least two disparate databases. The stored information includes consumer transactional information. According to the process and system, at least one qualitative variable which is common to each database is identified, and then transformed into one or more quantitative variables. The consumer transactional information in each database is then converted into converted information in terms of the quantitative variables. Thereafter, an integrated database is formed for predicting consumer behavior by combining the converted information from the disparate databases.

Claims

1. A method for predicting individual behavior from information stored in a plurality of disparate databases, the method comprising:

identifying a variable which is common to each database of the plurality of disparate databases;

using a processing device, performing a cluster analysis using the variable from each database of the plurality of disparate databases to form a plurality of clusters;

using a processing device, linking through the plurality of clusters the plurality of disparate databases to form an integrated database; and

using a processing device, creating a behavioral model, using corresponding data of the plurality of clusters of the integrated database, for predicting individual behavior.

2. The method of claim 1, wherein the step of identifying a variable further comprises:

identifying at least one qualitative variable which is common to each database of the plurality of disparate databases; and

transforming the at least one qualitative variable into one or more quantitative variables.

3. The method of claim 2, further comprising:

selecting at least one discriminating subset of the one or more quantitative variables to create one or more statistical drivers; and

evaluating a plurality of individuals represented in the plurality of disparate databases using the one or more statistical drivers.

4. The method of claim 3, wherein the selecting step further comprises:

identifying one or more industries which have discriminating consumers and grouping selected merchants into the at least one discriminating subset.

5. The method of claim 2, further comprising:

converting one or more clusters of the plurality of clusters into at least one super cluster; and

assigning the plurality of individuals to a corresponding cluster or super cluster using data from each database of the plurality of disparate databases.

6. The method of claim 2, wherein the at least one qualitative variable is a merchant and the one or more quantitative variable comprises one or more of the following:

mean number of transactions per person for the merchant,

mean amount per transaction for the merchant,

mean household income of shoppers shopping at the merchant, or

mean proportion of the shoppers for a particular area of the merchant.

7. The method of claim 1, further comprising:

prior to forming the integrated database, weighting data of the plurality of disparate databases to adjust for differences in size and in time encompassed.

8. The method of claim 1, wherein the information stored in at least one database of the plurality of disparate databases further comprises consumer transactional information and has instances of purchasing behavior by consumers.

9. The method of claim 8, wherein at least one of the disparate databases includes joint account information for at least two consumers, and wherein the method further comprises:

determining a consumer of the at least two consumers who generated at least a portion of the consumer transactional information.

10. A system for predicting individual behavior from information stored in a plurality of disparate databases, the system comprising:

a storage device adapted to store an integrated database; and

a processing device coupled to the storage device, the processing device adapted to perform a cluster analysis using at least one variable which is common to each database of the plurality of disparate databases to form a plurality of clusters, to link through the plurality of clusters the plurality of disparate databases to form the integrated database, and to generate a behavioral model, using corresponding data of the plurality of clusters of the integrated database, for predicting individual behavior.

11. The system of claim 10, wherein the processing device is further adapted to transform at least one qualitative variable, which is common to each database of the plurality of disparate databases, into one or more quantitative variables.

12. The system of claim 11, wherein the processing device is further adapted to determine at least one discriminating subset of the one or more quantitative variables to form one or more statistical drivers, and to evaluate a plurality of individuals represented in the plurality of disparate databases using the one or more statistical drivers.

13. The system of claim 12, wherein the processing device is further adapted to select the at least one discriminating subset by identifying one or more industries which have discriminating consumers, and further adapted to group selected merchants into the at least one discriminating subset.

14. The system of claim 11, wherein the at least one qualitative variable is a merchant and the one or more quantitative variable comprises one or more of the following:

mean number of transactions per person for the merchant,

mean amount per transaction for the merchant,

mean household income of shoppers shopping at the merchant, or

mean proportion of the shoppers for a particular area of the merchant.

15. The system of claim 10, wherein the processing device is further adapted, prior to the formation of the integrated database, to weight data of the plurality of disparate databases to adjust for differences in size and in time encompassed.

16. The system of claim 10, wherein the processing device is further adapted to convert one or more clusters of the plurality of clusters into at least one super cluster, and to assign the plurality of individuals to a corresponding cluster or super cluster using data from each database of the plurality of disparate databases.

17. The system of claim 10, wherein the information stored in the plurality of disparate databases further comprises one or more of the following types of information:

consumer transactional information;

instances of purchasing behavior by consumers;

survey information;

self-reported behavioral information;

or media behavior.

18. The system of claim 17, wherein at least one of said disparate databases includes joint account information for at least two consumers, and wherein the processing device is further adapted to determine a consumer of the at least two consumers who generated at least a portion of the consumer transactional information.

19. A method for creating a behavioral model from information stored in a plurality of disparate databases, the method comprising:

determining a plurality of variables from each database, at least a portion of the plurality of variables common to each database of the plurality of disparate databases;

using a processing device, performing a first cluster analysis using corresponding data of the plurality of variables common to each database of the plurality of disparate databases to create a plurality of cluster solutions;

using a processing device, linking through a cluster solution, of the plurality of cluster solutions, the information stored in the plurality of disparate databases; and

validating the cluster solution of the plurality of cluster solutions as a discriminatory behavioral model for predicting individual behavior.

20. The method of claim 19, wherein the determination of the plurality of variables further comprises:

selecting at least one qualitative variable which is common to each database of the plurality of disparate databases; and

transforming the at least one qualitative variable from each database to a plurality of quantitative variables.

21. The method of claim 20, further comprising:

performing a principal components analysis on the plurality of quantitative variables using the information stored in each database of the plurality of disparate databases to create a plurality of statistical drivers.

22. The method of claim 21, further comprising:

converting the information stored in the plurality of disparate databases according to the plurality of statistical drivers to create the corresponding data of the plurality of variables.

23. The method of claim 21, wherein the validation step further comprises:

determining whether the cluster solution provides corresponding discrimination on a plurality of other variables which are not statistical drivers in the plurality of disparate databases.

24. The method of claim 21, further comprising:

standardizing the plurality of quantitative variables;

transforming the standardized plurality of quantitative variables to be substantially orthogonal; and

differentially weighting the orthogonal, standardized plurality of quantitative variables to form the plurality of statistical drivers.

25. The method of claim 19, further comprising:

evaluating corresponding discrimination power of the plurality of statistical drivers using a second cluster analysis.

26. The method of claim 19, wherein at least one database of the plurality of disparate databases stores one or more of the following types of information:

behavioral and attitudinal information;

consumer transactional information; and

media consumption information.

27. The method of claim 19, further comprising:

describing each cluster of a plurality of clusters of the validated simultaneous cluster solution using information stored in at least one database of the plurality of disparate databases.

28. The method of claim 19, wherein the validation step further comprises:

determining whether the cluster solution provides corresponding discrimination separately within each database of the plurality of disparate databases.

29. A system for creating a behavioral model from information stored in a plurality of disparate databases, the system comprising:

a storage device storing data from one or more of the plurality of disparate databases; and

a processing device coupled to the storage device, the processing device adapted to determine a plurality of variables from each database, at least a portion of the plurality of variables common to each database of the plurality of disparate databases;

to perform a first cluster analysis using corresponding data of the plurality of variables common to each database of the plurality of disparate databases to create a plurality of cluster solutions;

to link through a cluster solution, of the plurality of cluster solutions, the information stored in the plurality of disparate databases; and

to validate the cluster solution of the plurality of cluster solutions as a discriminatory behavioral model for predicting individual behavior.

30. The system of claim 29, wherein the processing device is further adapted to determine the plurality of variables by selecting at least one qualitative variable which is common to each database of the plurality of disparate databases, and transforming the at least one qualitative variable from each database to a plurality of quantitative variables.

31. The system of claim 30, wherein the processing device is further adapted to perform a principal components analysis on the plurality of quantitative variables using the information stored in each database of the plurality of disparate databases to create a plurality of statistical drivers.

32. The system of claim 31, wherein the processing device is further adapted to convert the information stored in the plurality of disparate databases according to the plurality of statistical drivers to create the corresponding data of the plurality of variables.

33. The system of claim 31, wherein the processing device is further adapted to validate the cluster solution by determining whether the cluster solution provides corresponding discrimination on a plurality of other variables which are not statistical drivers in the plurality of disparate databases.

34. The system of claim 31, wherein the processing device is further adapted to standardize the plurality of quantitative variables;

transform the standardized plurality of quantitative variables to be substantially orthogonal; and

to differentially weight the orthogonal, standardized plurality of quantitative variables to form the plurality of statistical drivers.

35. The system of claim 31, wherein the processing device is further adapted to evaluating a corresponding discrimination power of the plurality of statistical drivers using a second cluster analysis.

36. The system of claim 29, wherein at least one database of the plurality of disparate databases stores one or more of the following types of information:

behavioral and attitudinal information;

consumer transactional information; and

media consumption information.

37. The system of claim 29, wherein the processing device is further adapted to determine whether the cluster solution provides corresponding discrimination separately within each database of the plurality of disparate databases.