Targeted incentives based upon predicted behavior

• US 20040034570A1
• Filed: 06/13/2003
• Published: 02/19/2004
• Est. Priority Date: 03/20/2002
• Status: Active Grant

• Current Assignee: Catalina Marketing Corporation
• Original Assignee: Catalina Marketing Corporation
• Inventors: Davis, Mark
• Granted Patent: US 7,653,594 B2
• Time in Patent Office: Days
• US Class Current: 705/26
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• 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 30/0211 Determining the effectiveness of discounts or incentives
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0229 Multi-merchant loyalty card systems
• CPC Class Codes: G06Q 30/0238 at point-of-sale [POS]
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]

Abstract

A system and method for anticipating consumer behavior and determining transaction incentives for influencing consumer behavior comprises a computer system and associated database for determining cross time correlations between transaction behavior, for applying the function derived from the correlations to consumer records to predict future consumer behavior, and for deciding on transaction incentives to offer the consumers based upon their predicted behavior.

Claims

27. A method, comprising:

providing a database;

providing a computer system having read and write access to said database; and

storing in said database a plurality of consumer records including a first consumer record for a first consumer;

wherein said first consumer record stores;

(1) CID data (consumer identification data) indicating a first consumer CID for said first consumer;

in association with said first consumer CID, at least the following;

(2) transaction data in a first transaction class field indicating items transacted by said first consumer in a first transaction class during a first prior time period; and

(3) predictive data in a first predictive field indicating at least one of a ranking, a probability, and a prediction that said first consumer will transact in a first correlated class during a correlated time period, and wherein said correlated time period is subsequent in time to said prior time period.

28. The method of claim 27 further comprising deciding whether to offer a transaction incentive to said first consumer based upon said predictive data in said first predictive field for said first consumer.

29. The method of claim 28 further comprising printing said transaction incentive.

30. The method of claim 28 wherein said printing comprises printing in the presence of said consumer.

31. A computer implemented method, comprising:

in a set of customer records containing transaction data, correlating transactions in a first set of input classes in a first time period to transactions in a first correlated class in a second time period, wherein the second time period is subsequent to said first time period, thereby defining correlation data for said first set of classes; and

deciding whether to issue a transaction incentive to a customer based at least in part upon transaction data for said customer in said first set of classes and said correlation data for said first set of classes.

32. The method of claim 31, further comprising:

correlating transactions in a second set of classes in said first time period to transactions in a second correlated class in said second time period, thereby defining correlation data for said second set of classes; and

deciding whether to issue a transaction incentive to a customer based at least in part upon (1) transaction data for said customer in said first set of classes and said correlation data for said first set of classes and (2) transaction data for said customer in said second set of classes and said correlation data for said second set of classes.

33. The method of claim 31 further comprising correlating transactions in a second set of classes in a second time period to transactions in a second correlated class in said second time period, thereby defining correlation data for said second set of classes;

and

deciding whether to issue a transaction incentive to a customer based at least in part upon (1) transaction data for said customer in said first set of classes and said correlation data for said first set of classes and (2) transaction data for said customer in said second set of classes and said correlation data for said second set of classes.

34. The method of claim 31 wherein a term of said transaction incentive is purchasing in said first correlated class.

35. The method of claim 31 wherein a term of said transaction incentive is purchasing a specified brand in said second correlated class.

36. The method of claim 32 wherein said transaction incentive is a transaction incentive for transacting in that one of said first correlated class and said second correlated class having correlated data with higher values.

37. The method of claim 31 wherein said deciding comprising determining whether said transaction data for said customer contains data indicating said customer transacted in at least one class of said first set having a relatively high correlation values compared to other classes of said first set.

38. The method of claim 37 wherein said at least one class comprises at least two classes.

39. The method of claim 37 wherein said at least one class comprises at least three classes.

40. The method of claim 37 wherein said at least one class comprises at least four classes.

41. The method of claim 31 wherein said deciding occurs while said customer is participating in a transaction at a POS terminal.

42. The method of claim 41 comprising offering said transaction incentive to said customer while said customer is participating in said transaction and at said POS terminal.

43. The method of claim 31 wherein said deciding occurs between transactions of said customer.

44. The method of claim 43 and further comprising mailing or e-mailing said transaction incentive to said customer.

45. The method of claim 43 and further comprising offering said transaction incentive to said customer when said customer is identified at a terminal.

46. The method of claim 45 wherein said terminal is a POS terminal.

47. The method of claim 45 wherein said terminal is a kiosk.

48. The method of claim 31 wherein said deciding whether to issue a transaction incentive is based at least in part upon non transaction data for said consumer.

49. The method of claim 48 wherein said non transaction data comprises demographic data.

50. The method of claim 48 wherein said non transaction data comprises loyalty quotient data.

51. The method of claim 31 wherein said deciding whether to issue a transaction incentive is based at least in part upon block data.

52. A computer implemented system, comprising:

means for, in a set of customer records containing transaction data, correlating transactions in a first set of input classes in a first time period to transactions in a first correlated class in a second time period, wherein the second time period is subsequent to said first time period, thereby defining correlation data for said first set of classes; and

means for deciding whether to issue a transaction incentive to a customer based at least in part upon transaction data for said customer in said first set of classes and said correlation data for said first set of classes.

53. A computer implemented method, comprising:

in a set of customer records containing transaction data, correlating transactions in a first set of input classes in a first time period to a change in transactions in a first correlated class between said first time period and a second time period, wherein the second time period is subsequent to said first time period, thereby defining correlation data for said first set of classes; and

deciding whether to issue a transaction incentive to a customer based at least in part upon transaction data for said customer in said first set of classes and said correlation data for said first set of classes.

54. The method of claim 53, further comprising:

correlating transactions in a second set of classes in said first time period to a change in transactions in a second correlated class between said first time period and said second time period, thereby defining correlation data for said second set of classes; and

deciding whether to issue a transaction incentive to a customer based at least in part upon (1) transaction data for said customer in said first set of classes and said correlation data for said first set of classes and (2) transaction data for said customer in said second set of classes and said correlation data for said second set of classes.

55. A method comprising:

(1) anticipating a consumer'"'"'s behavior for purchasing a first product based at least in part upon at least a portion of that part of said consumer'"'"'s transaction history including said consumer'"'"'s transactions for other than for purchase of said first product; and

(2) basing an incentive determination upon said anticipating.

56. A method comprising:

(1) anticipating a consumer'"'"'s behavior for purchasing a first product based at least in part upon at least a portion of that part of said consumer'"'"'s transaction history including said consumer'"'"'s transactions for purchases of at least one product other than said first product; and

(2) basing an incentive determination upon said anticipating.

1. A system, comprising:

a database;

a computer system having read and write access to said database; and

wherein said database stores a plurality of consumer records including a first consumer record for a first consumer;

wherein said first consumer record stores;

(1) CID data (consumer identification data) indicating a first consumer CID for said first consumer;

in association with said first consumer CID, at least the following;

(2) transaction data in a first transaction class field indicating items transacted by said first consumer in a first transaction class during a first prior time period;

(3) predictive data in a first predictive field indicating at least one of a ranking, a probability, and a prediction that said first consumer will transact in a first correlated class during a correlated time period, and wherein said correlated time period is subsequent in time to said prior time period.

2. The system of claim 1 wherein said first class and said second class define the same class.

3. The system of claim 1 wherein said first class defines a different class than said second class.

4. The system of claim 1 wherein said first class is distinct from said second class.

5. The system of claim 1 wherein said first class is a genus and said second class is a species of said genus.

6. The system of claim 1 wherein said first class defines a species of a genus and said second class defines said genus.

7. The system of claim 1 wherein said database stores, in association with said first consumer CID, transaction data in a second transaction class field indicating items transacted by said consumer in a second transaction class during said first prior time period.

8. The system of claim 7 wherein said database stores, in association with said first consumer CID, transaction data in a third transaction class field indicating items transacted by said consumer in a third transaction class during said first prior time period.

9. The system of claim 8 wherein said database stores, in association with said first consumer CID, transaction data in a fourth transaction class field indicating items transacted by said consumer in a fourth transaction class during said first prior time period.

10. The system of claim 9 wherein said database stores, in association with said first consumer CID, transaction data in a fifth transaction class field indicating items transacted by said consumer in a fifth transaction class during said second prior time period.

11. The system of claim 10 wherein said database stores, in association with said first consumer CID, transaction data in a sixth transaction class field indicating items transacted by said consumer in a sixth transaction class during said second prior time period.

12. The system of claim 11 wherein said database stores, in association with said first consumer CID, transaction data in a seventh transaction class field indicating items transacted by said consumer in a seventh transaction class during said second prior time period.

13. The system of claim 12 wherein said database stores, in association with said first consumer CID, transaction data in an eighth transaction class field indicating items transacted by said consumer in an eighth transaction class during said second prior time period.

14. The system of claim 1 programmed to decide whether to offer a transaction incentive to said first consumer based upon said predictive data in said first predictive field for said first consumer.

15. The system of claim 14 wherein a term of said transaction incentive is purchase in said first correlated class.

16. The system of claim 14 wherein a term of said transaction incentive is purchase of a specified quantity in said first correlated class.

17. The system of claim 14 wherein a term of said transaction incentive is purchase of a specified brand in said first correlated class.

18. The system of claim 14 wherein terms of said transaction incentive are purchase in said first correlated class during said correlated time period.

19. The system of claim 14 wherein a term of said transaction incentive is purchase in a class other than said first correlated class.

20. The system of claim 14 wherein a term of said transaction incentive is purchase of a specified brand not in said first correlated class.

21. The system of claim 14 wherein terms of said transaction incentive are purchase in a class other than said first correlated class during said correlated time period.

22. The system of claim 1 further comprising a terminal for presenting said transaction incentive to said first consumer.

23. The system of claim 1 further comprising a printer for printing said terms of transaction incentive.

24. The system of claim 23 wherein said printer is at a POS terminal.

25. The system of claim 23 wherein said printer is at a Kiosk.

26. The system of claim 23 wherein said printer is at a consumer'"'"'s computer.

57. The method of claim 56 wherein said incentive determination is based at least in part upon differences between said consumer'"'"'s anticipated and prior purchase behavior for said product.

58. The method of claim 57 wherein said differences are based at least in part upon variations in frequency of purchase.

59. The method of claim 57 wherein said differences are based at least in part upon variations in quantity of purchase.

63. The method of claim 57 wherein said differences are based at least in part upon variations in utilization of prior incentive for purchase.

61. The method of claim 57 wherein said differences are based at least in part upon variations in brand loyalty.

62. The method of claim 56 wherein said anticipating is based at least in part upon analysis of data in consumer records.

63-1. The method of claim 56 wherein said anticipating is based at least in part upon a model of consumer transaction behavior.

64. A system comprising:

a retailer computer system for managing transaction data, said retailer computer system comprising a POS terminal for receiving transaction data;

a communications network;

a central computer system including a database, said central computer system capable of;

(1) receiving transaction data from said retailer computer system over said communication network,

(2) analyzing consumer transaction data stored in said database,

(3) determining transaction incentives to offer to consumers, and wherein said central computer system is programmed to employ predictive modeling on said transaction data to determine a predictive modeling function, to determine at least one of rankings, probabilities, and predictions of future consumer transactions by applying consumer transaction data to said predictive modeling function, and determining transaction incentives based upon said at least one of rankings, probabilities, and predictions.

65. The system of claim 64 wherein said central computer system is programmed to determine timing and method of transmission of said transaction incentives that will most likely result in said transaction incentives being redeemed.

66. A method comprising:

receiving transaction data at a POS terminal in a retailer computer system;

transmitting said transaction data over a communications network to a central computer system;

storing said transaction data in a database to which said central computer system has read and write access;

(2) analyzing consumer transaction data stored in said database;

(3) determining transaction incentives to offer to consumers associated with said transaction data;

wherein said central computer system is programmed to employ predictive modeling on said transaction data to determine a predictive modeling function, to determine at least one of rankings, probabilities, and predictions of future consumer transactions by applying consumer transaction data to said predictive modeling function, and determining transaction incentives based upon said at least one of rankings, probabilities, and predictions.

67. The system of claim 66 further comprising determining timing and method of transmission of said transaction incentives that will most likely result in said transaction incentives being redeemed.

68. The system of claim 67 further comprising transmitting said transaction incentive over said communication network to said retailer computer system.

69. A system for anticipating consumer behavior and determining transaction incentives for influencing consumer behavior comprising:

a computer system and associated database, wherein said computer system is programmed to determine cross time correlations between transaction behavior, apply a function derived from the correlations to consumer records to predict future consumer behavior, and to decide on transaction incentives to offer the consumers based upon their predicted behavior.

70. A computer implemented method for anticipating consumer behavior and determining transaction incentives for influencing consumer behavior comprising:

determining cross time correlations between transaction behavior;

applying a function derived from the correlations to consumer records to predict future consumer behavior; and

deciding on transaction incentives to offer consumers based upon their predicted behavior.