Methods and systems for customer relationship management

• US 20020194117A1
• Filed: 04/06/2001
• Published: 12/19/2002
• Est. Priority Date: 04/06/2001
• Status: Abandoned Application

• Current Assignee: General Electric Capital Corporation (General Electric Company)
• Original Assignee: General Electric Capital Corporation (General Electric Company)
• Inventors: Nabe, Oumar, Fogarty, David J., Samra, Balwinder S.
• Application Number:
  US09/828,414
• Publication Number:
  US 20020194117A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Methods and systems for managing customer relationships are disclosed where the method includes the steps of providing a database of customer information and customer spending data, predicting future customer behavior based on the customer information and customer spending data, and constructing customer campaigns with personalized offers for targeted customers.

Claims

1. A method for managing customer relationships, said method comprising the steps of:

providing a database of customer information and customer spending data;

predicting future customer behavior based on the customer information and customer spending data; and

constructing customer campaigns with personalized offers for targeted customers.

2. A method according to claim 1 wherein said step of predicting future customer behavior further comprises the step of identifying hit and run customers using a hit and run model.

3. A method according to claim 2 further comprising the step of rank ordering customer accounts that have had only one occurrence of spending activity, based upon a probability of there ever being future spending activity.

4. A method according to claim 1 wherein said step of predicting future customer behavior further comprises the step of identifying dormant customers using a dormancy model.

5. A method according to claim 4 further comprising the step of rank ordering customer accounts without spending activity over a predetermined unit of time.

6. A method according to claim 1 wherein said step of predicting future customer behavior further comprises the steps of clustering groups of customers using key performance indicators.

7. A method according to claim 1 wherein said step of predicting future customer behavior further comprises the step of predicting future spending of customers in the customer information database within a specified time period.

9. A method according to claim 1 wherein said step of constructing customer campaigns further comprises the step of identifying product purchase patterns and indicating trends using key variables.

10. A method according to claim 1 wherein said step of constructing customer campaigns further comprises the step of exposing the customer to new spending opportunities by segmenting previous spending patterns.

8. A method according to claim I wherein said step of constructing customer campaigns further comprises the step of determining a customer targeting list based on at least one of likelihood of response, an estimated overall response rate, and profitability margin.

11. A system configured for managing customer relationships, said system comprising:

at least one computer;

a server including a database of customer information and customer spending data, said server configured to predict future customer behavior based on the customer information and customer spending data, construct customer campaigns with personalized offers for targeted customers; and

a network connecting said server to said computer.

12. A system according to claim 11 wherein said server is configured to identify hit and run customers with a hit and run model.

13. A system according to claim 12 wherein said server is configured to rank order customer accounts that have had only one occurrence of spending activity, based upon a probability of there ever being future spending activity.

14. A system according to claim 11 wherein said server is configured to identify dormant customers with a dormancy model.

15. A system according to claim 14 wherein said server is configured to rank order customer accounts without spending activity over a predetermined unit of time.

16. A system according to claim 11 wherein said server configured to cluster groups of customers using key performance indicators.

17. A system according to claim 11 wherein said server configured to predict future spending of customers within a specified time period.

18. A system according to claim 11 wherein said server configured to determine a customer targeting list based on at least one of likelihood of response, an estimated overall response rate and profitability margin.

19. A system according to claim 11 wherein said server configured to identify product purchase patterns and indicate trends using key variables.

20. A system according to claim 11 wherein said server configured to present to customers new spending opportunities by segmenting previous spending patterns.

21. A system according to claim 11 wherein said server configured to gather customer information through at least one of point of sale, home shopping, E-commerce, credit card information, bank card information, world wide web and digital television.

22. A computer-readable medium comprising:

at least one record of customer information and customer spending data;

a record of key performance indicators;

a plurality of rules for matching customer information and customer spending data to at least one of said key performance indicators; and

a record of predictions of future customer behavior according to said plurality of rules.

23. A computer-readable medium according to claim 22 further comprising a record of clustered groups of customers based upon key performance indicators.

24. A computer-readable medium according to claim 22 further comprising a record of rank ordered customer accounts without spending activity over a predetermined unit of time.

25. A computer-readable medium according to claim 22 further comprising a record of rank ordered customer accounts that have had only one occurrence of spending activity, based upon a probability of there ever being future spending activity.

26. A computer-readable medium according to claim 22 further comprising a record of customer targets based on at least one of likelihood of response, an estimated overall response rate, and profitability margin.

28. A computer according to claim 27 wherein to prompt a user to select a database, said computer displays a computer generated screen listing of key performance indicators.

29. A computer according to claim 28 wherein to predict future customer behavior, said computer programmed to cluster groups of customers according to key performance indicators.

30. A computer according to claim 27 wherein to prompt a user with a strategy to manage the customer relationship, said computer displays a computer generated screen of rank ordered customer accounts according to spending activity.

27. A computer programmed to:

prompt a user to select a database of available customer information and customer spending data for analysis;

predict future customer behavior based upon customer information and customer spending data; and

prompt a user with a strategy to manage the customer relationship with personalized offers for customers based upon predicted future behavior.

31. A method for managing a customer relationship, said method comprising the steps of:

selecting, from an electronic interface, a record of customer information and customer spending data;

selecting, from the electronic interface, key performance indicators for an analysis of the customer information and customer spending data; and

requesting, from the electronic interface, a prediction of future customer behavior.

32. A method according to claim 31 wherein the analysis of customer information comprises the step of requesting, from the electronic interface, a group of customers clustered against the selected key performance indicators.

33. A method according to claim 32 wherein said step of requesting a prediction of future customer behavior comprises the step of requesting a rank ordering of customer accounts according to spending activity.

34. A method according to claim 31 wherein said step of requesting a prediction of future customer behavior further comprises the step of requesting a prediction of an optimal targeting list based upon at least one of a likelihood of response, an estimated overall response rate, and profitability margin.

36. Apparatus according to claim 35 wherein said means for predicting future customer behavior comprises means for examining key performance indicators.

37. Apparatus according to claim 35 wherein said means for predicting future customer behavior comprises means for clustering groups of customers using the key performance indicators.

38. Apparatus according to claim 35 wherein said means for predicting future customer behavior comprises means for identifying hit and run customers.

39. Apparatus according to claim 35 wherein said means for predicting future customer behavior comprises means for identifying dormant customers.

40. Apparatus according to claim 35 wherein said means for predicting future customer behavior comprises means for rank ordering customer accounts according to spending activity.

41. Apparatus according to claim 35 wherein said means for constructing customer campaigns comprises means for determining a customer targeting list based on at least one of likelihood of response, an estimated overall response rate, and profitability margin.

42. Apparatus according to claim 35 wherein said means for constructing customer campaigns comprises means for segmenting previous spending patterns to present new spending opportunities to customers.

35. Apparatus comprising:

means for storing customer information and customer spending data;

means for predicting future customer behavior according to the customer information and customer spending data; and

means for constructing customer campaigns to manage the customer relationship.

43. A database comprising:

data corresponding to customer information and customer spending data;

data corresponding to key performance indicators;

data corresponding to rules for matching customer information and customer spending data to at least one of the key performance indicators; and

data corresponding to predictions of future customer behavior.

44. A database according to claim 43 further comprising data corresponding to clustered groups of customers based upon key performance indicators.

45. A database according to claim 43 further comprising data corresponding to rank ordered customer accounts without spending activity over a predetermined unit of time.

46. A database according to claim 43 further comprising data corresponding to rank ordered customer accounts that have had only one occurrence of spending activity, based upon a probability of there ever being future spending activity.

47. A database according to claim 43 further comprising data corresponding to customer targets based on at least one of likelihood of response, an estimated overall response rate, and profitability margin.

49. The computer program as recited in claim 48 further comprising a code segment that accesses customer information and customer spending data stored in the centralized database.

50. The computer program as recited in claim 49 further comprising a code segment that cross references the customer information and customer spending data to unique identifiers for easy retrieval, update and storage.

51. The computer program as recited in claim 48 wherein said code segment that identifies a select list of customers and further comprising a code segment that:

rank orders customer accounts that have had only one occurrence of spending activity, based upon a probability of there ever being future spending activity; and

rank orders customer accounts without spending activity over a predetermined unit of time.

52. The computer program as recited in claim 48 further comprising a code segment that identifies customers using at least one of a dormancy model and a hit and run model.

53. The computer program as recited in claim 48 further comprising a code segment that:

clusters groups of customers using key performance indicators; and

predicts future spending of customers within a specified time period.

54. The computer program as recited in claim 48 further comprising a code segment that identifies a customer targeting list based on at least one of likelihood of response, an estimated overall response rate, product purchase patterns, future indicating trends, and profitability margin.

55. The computer program as recited in claim 48 further comprising a code segment that develops customer campaigns by exposing the customer to new spending opportunities by segmenting previous spending patterns.

56. The computer program as recited in claim 48 wherein the network is a wide area network operable using a protocol including at least one of TCP/IP and IPX.

57. The computer program as recited in claim 48 wherein the data is received from the user via a graphical user interface.

58. The computer program as recited in claim 48 further comprising a code segment that develops customer campaigns based on pre-stored assumptions in the database.

59. The computer program as recited in claim 48 wherein the client system and the server system are connected via a network and wherein the network is one of a wide area network, a local area network, an intranet and the Internet.

60. The computer program as recited in claim 48, and further comprising a code segment that monitors the security of the system by restricting access to unauthorized individuals.

48. A computer program embodied on a computer readable medium for managing customer relationships to enhance customer loyalty, using a network based system including a server system coupled to a centralized database and at least one client system, said computer program comprising:

a code segment that receives customer data into a centralized customer database;

a code segment that accesses the centralized customer database;

a code segment that predicts future customer behavior based on information available from the customer database;

a code segment that identifies a select list of customers based on predefined parameters stored in the centralized database; and

a code segment that develops customer campaigns with personalized offers for the select list of customers.

61. A database comprising:

data corresponding to customer information and customer spending;

data corresponding to key performance indicators; and

at least one rule to be applied to the customer information and customer spending data and the key performance indicator data to predict future customer behavior.

62. A database according to claim 61 further comprising:

data corresponding to clustered groups of customers; and

at least one rule to be applied to the cluster customer group data and the key performance indicators to predict future customer behavior.

63. A database according to claim 61 further comprising at least one rule to be applied to the customer information and customer spending data and the key performance indicators to rank order customer accounts without spending activity over a predetermined unit of time.

65. A database according to claim 61 further comprising at least one rule based upon at least one of a likelihood of response, an estimated overall response rate and a profitability margin, said rule to be applied to the customer information and customer spending data and the key performance indicators to target customers.