Systems And Methods For Aggregating And Utilizing Retail Transaction Records At The Customer Level

• US 20090132347A1
• Filed: 11/11/2008
• Published: 05/21/2009
• Est. Priority Date: 08/12/2003
• Status: Abandoned Application

• Current Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Original Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Inventors: Chen, Yingxia, Sarkissian, Robert, Anderson, Russell Wayne, He, Xiaofeng
• Application Number:
  US12/268,773
• Publication Number:
  US 20090132347A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/04 Billing or invoicing
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

A method and system is provided for storing and manipulating customer purchase information received from a plurality of sources. A computer system may be used comprising a storage device for storing the customer purchase information and a processor for processing the customer purchase information. The method may include receiving the customer purchase information; organizing the customer purchase information within a predetermined organizational structure; creating a customer preference based at least in part on the customer purchase information; and aggregating customer purchases for merchant classes based on the customer purchase information so as to generate aggregated customer purchase information. The method may further include generating marketing information based on at least one of the customer preference and the aggregated customer purchase information.

Claims

1-24. -24. (canceled)

25. A method for modeling consumer behavior to estimate consumer spend, comprising:

receiving individual and aggregated consumer data including consumer bureau data, purchase data and existing customer data;

analyzing the individual and aggregated consumer data to determine spending behavior for at least one category of consumers;

generating a model of consumer spending patterns for the at least one category based on said analyzing; and

validating the model using consumer data.

26. The method of claim 25, further comprising:

refining the model based on additional consumer data.

27. The method of claim 25, further comprising:

receiving purchase data for a plurality of accounts of an individual consumer over a previous period of time;

identifying balance data of the plurality of accounts, based on the purchase data;

determining spending behavior for any of the plurality of accounts for any portion of the previous period of time in which a balance transfer to such account is identified; and

estimating purchase information of the individual consumer based on the purchase data, spending behavior and the model.

28. The method of claim 27, said previous period of time comprising a period of months.

29. The method of claim 28, said portion of the previous period comprising one month.

30. The method of claim 27, said plurality of accounts including at least one of:

a credit card account, a debit card account, and a checking account.

31. The method of claim 27, said generating a model further comprising:

determining at least two categories of customers based on the aggregated customer data, the at least two categories of customers relating to preferences of the customers.

32. The method of claim 31, further comprising:

assigning one of the first and second categories to the individual customer based on the purchase data.

33. The method of claim 27, further comprising:

changing the handling of a credit account of the individual consumer based on said estimating.

34. The method of claim 33, said changing the handling further comprising:

targeting customers based on distinguishing preferences.

35. The method of claim 33, said changing further comprising:

providing a discount effect based on the number of accounts of a customer.

36. The method of claim 27, further comprising:

selecting the individual consumer from a set of customers based on delinquency events.

37. The method of claim 25, said validating further comprising:

validating the model using data from existing consumers.

38. The method of claim 26, wherein the additional consumer data is existing customer data.

39. The method of claim 27, wherein the purchase information is customer preference information.

40. A method for estimating a purchasing ability of a consumer, comprising:

receiving purchase data for a plurality of accounts of an individual consumer for a previous period of time;

identifying balance changes of the at least one of the plurality of accounts, based on the purchase data;

providing a discount effect based on the number of accounts of a customer; and

estimating a purchasing ability of the individual consumer based on the purchase data, said discount effect and a model of consumer spending derived from individual and aggregate consumer data including purchase data, existing customer data and bureau data.

41. A system, maintained by a business, for modeling consumer behavior to estimate consumer spend, the system comprising:

a communication portion, maintained by the business, that inputs both individual and aggregated consumer data including consumer bureau data, purchase data and existing customer data;

a processing portion, maintained by the business, that analyses the individual and aggregated consumer data to determine spending behavior for at least one category of consumers;

the processing portion generating a model of consumer spending patterns for the at least one category based on said analyzing; and

the processing portion validating the model using consumer data.

42. The system of claim 41, wherein the processing portion further receives purchase data for a plurality of accounts of an individual consumer over a previous period of time, and identifies balance data of the plurality of accounts, based on the purchase data;

the processing portion determining spending behavior for at least one of the plurality of accounts for a portion of the previous period of time in which a balance transfer to such account is identified; and

the processing portion estimating purchase information of the individual consumer based on the purchase data, spending behavior and the model.

43. The system of claim 42, said plurality of accounts including at least one selected from the group consisting of a credit card account and a checking account.

44. The system of claim 43, the generating the model further comprising:

determining at least two categories of customers based on the aggregated customer data, the at least two categories of customers relating to preferences of the customers.

45. A method for modeling consumer behavior to estimate consumer spend, comprising:

receiving individual and aggregated consumer data including consumer bureau data, purchase data and existing customer data;

analyzing the individual and aggregated consumer data to determine spending behavior for at least one category of consumers;

generating a model of consumer spending patterns for the at least one category based on said analyzing; and

validating the model using consumer data;

the method further comprising refining the model based on additional consumer data; and

the method including;

receiving purchase data for a plurality of accounts of an individual consumer over a previous period of time;

identifying balance data of the plurality of accounts, based on the purchase data;

determining spending behavior for any of the plurality of accounts for any portion of the previous period of time in which a balance transfer to such account is identified; and

estimating purchase information of the individual consumer based on the purchase data, spending behavior and the model; and

the generating a model further comprising;

determining at least two categories of customers based on the aggregated customer data, the at least two categories of customers relating to preferences of the customers; and

assigning one of the first and second categories to the individual customer based on the purchase data;

the method further comprising changing handling of a credit account of the individual consumer based on said estimating, the changing the handling further comprising;

targeting customers based on distinguishing preferences; and

providing a discount effect based on the number of accounts of a customer; and

the method further comprising validating the model using data from existing consumers.