METHOD AND SYSTEM FOR PURCHASE-BASED SEGMENTATION

• US 20110231227A1
• Filed: 05/10/2011
• Published: 09/22/2011
• Est. Priority Date: 09/22/2003
• Status: Abandoned Application

• Current Assignee: Citicorp Credit Services Incorporated (Citigroup Incorporated)
• Original Assignee: Citicorp Credit Services Incorporated (Citigroup Incorporated)
• Inventors: TEMARES, Mark E., Menai, Noor A., Newman, Alan B.
• Application Number:
  US13/104,461
• Publication Number:
  US 20110231227A1
• Time in Patent Office: Days
• US Class Current: 705/7.33
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0234 Rebates after completed purchase
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching

Abstract

A method and system for purchased-based segmentation of potential customers employs the use of actual, observed purchases instead of presumptions and correlations to improve the accuracy of segmentation and involves collecting empirical data for a client on actual purchasing behavior of a group of customers and applying statistical modeling techniques to the empirical purchasing behavior data to identify segments or clusters of the customers that exhibit similar purchasing propensity characteristics. Thereafter, the segments or clusters are further differentiated from one another according to other factors having a tendency to directly affect actual purchasing behavior of the customers within the segments or clusters, and potential customers are then identified according to a correlation with the segments or clusters for customized marketing.

Claims

1. A method for purchased-based segmentation of customers, comprising:

collecting, using a computer having a processor and memory, empirical data on purchasing behavior of a subset of a group of customers, the empirical data comprising purchase information associated with purchase behavior of the group of customers;

analyzing, using the computer, the empirical purchasing behavior data to identify at least one segment or cluster that includes the group of customers;

analyzing, using the computer, the empirical purchasing behavior data to identify at least one segment or cluster for the subset of customers;

analyzing, using the computer, the empirical purchasing behavior data to identify at least one segment or cluster for the group of customers that does not include the subset of the group of customers;

identifying, using the computer, characteristics indicative of purchasing behavior for the segment or cluster for the group of customers;

analyzing, using the computer, the subset of the group of customers based upon the characteristics of the group of customers; and

identifying, using the computer, potential customers for according to a correlation with the segments or clusters and the information generated about the purchasing behavior of the group of customers that does not include the subset of the group of customers.

2. The method of claim 1, further comprising collecting purchase information from the use of payment devices.

3. The method of claim 2, wherein at least one payment device is selected from the group consisting of credit cards, debit cards, stored value cards, and radio frequency identification devices.

4. The method of claim 1, further comprising collecting purchase information from the use of credentials issued to the group of customers.

5. The method of claim 4, wherein at least one credential is selected from the group consisting of warranty cards, rebate forms, barcode scans, and proof of purchase data.

6. The method of claim 1, wherein the subset of the group of customers comprises customers of a particular product or service provider.

7. The method of claim 1, wherein the group of customers that does not include the subset of the group of customers comprises customers of a competitor product or service provider.

8. The method of claim 1, further comprising identifying, using the computer, common and different characteristics indicative of purchasing behavior between the subset of the group of customers and the group of customers that does not include the subset of the group of customers.

9. The method of claim 1, wherein identifying potential customers for customized marketing further comprises identifying customers who correlate with a particular segment or cluster.

10. The method of claim 9, wherein the particular segment or cluster is the segment or cluster for the subset of the group of customers.

11. The method of claim 9, wherein the particular segment or cluster is the segment or cluster the group of customers that does not include the subset of the group of customers.

12. The method of claim 1, wherein identifying potential customers comprises indexing.

13. A machine for purchased-based segmentation of customers, comprising:

a computer having a processor and memory, the processor being programmed for;

collecting empirical data on purchasing behavior of a subset of a group of customers, the empirical data comprising purchase information associated with purchase behavior of the group of customers;

analyzing the empirical purchasing behavior data to identify at least one segment or cluster that includes the group of customers;

analyzing the empirical purchasing behavior data to identify at least one segment or cluster for the subset of customers;

analyzing the empirical purchasing behavior data to identify at least one segment or cluster for the group of customers that does not include the subset of the group of customers;

identifying characteristics indicative of purchasing behavior for the segment or cluster for the group of customers;

analyzing the subset of the group of customers based upon the characteristics of the group of customers; and

identifying potential customers for according to a correlation with the segments or clusters and the information generated about the purchasing behavior of the group of customers that does not include the subset of the group of customers.

14. The machine of claim 13, further comprising collecting purchase information from the use of payment devices.

15. The machine of claim 14, wherein at least one payment device is selected from the group consisting of credit cards, debit cards, stored value cards, and radio frequency identification devices.

16. The machine of claim 13, further comprising collecting purchase information from the use of credentials issued to the group of customers.

17. The machine of claim 16, wherein at least one credential is selected from the group consisting of warranty cards, rebate forms, barcode scans, and proof of purchase data.

18. The machine of claim 13, wherein the subset of the group of customers comprises customers of a particular product or service provider.

19. The machine of claim 13, wherein the group of customers that does not include the subset of the group of customers comprises customers of a competitor product or service provider.

20. The machine of claim 13, further comprising identifying common and different characteristics indicative of purchasing behavior between the subset of the group of customers and the group of customers that does not include the subset of the group of customers.

21. The machine of claim 13, wherein identifying potential customers for customized marketing further comprises identifying customers who correlate with a particular segment or cluster.

22. The machine of claim 21, wherein the particular segment or cluster is the segment or cluster for the subset of the group of customers.

23. The machine of claim 21, wherein the particular segment or cluster is the segment or cluster the group of customers that does not include the subset of the group of customers.