Method and system for purchase-based segmentation

• US 7,966,226 B1
• Filed: 01/22/2008
• Issued: 06/21/2011
• Est. Priority Date: 09/22/2003
• Status: Active Grant

• Current Assignee: Citicorp Credit Services Incorporated (Citigroup Incorporated)
• Original Assignee: Citicorp Credit Services Incorporated (Citigroup Incorporated)
• Inventors: Menai, Noor A., Newman, Alan B., Temares, Mark E.
• Primary Examiner(s): Garg; Yogesh C
• Application Number:
  US12/010,185
• Time in Patent Office: 1,246 Days
• Field of Search: 705/10, 705/14.53, 705/14.66, 705/26.7
• US Class Current: 705/26.7
• 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 for a client on actual purchasing behavior of a group of customers, said empirical data consisting of actual observed customer and purchase information associated with purchase behavior of the client'"'"'s customers and third parties'"'"' customers collected as a byproduct of use of payment devices and benefit credentials issued by the client and the third parties to their respective customers forming part of the group of customers;

applying, using the computer, statistical modeling techniques to the empirical purchasing behavior data to identify segments or clusters for an overall category of the client'"'"'s and the third parties'"'"' customers and for separate categories for the client'"'"'s customers and the third parties'"'"' customers;

identifying, using the computer, characteristics indicative of purchasing behavior for the segment or cluster for the overall category, comparing a relative presence of the client'"'"'s customers and the third parties'"'"' customers in the segment or cluster for the overall category and generating information about the purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers based at least in part on the identified characteristics of the segment or cluster for the overall category;

identifying, using the computer, characteristics indicative of purchasing behavior for overlapping and non-overlapping ones of the separate segments or clusters and generating information about purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers based at least in part on a comparison of the identified characteristics of the overlapping and non-overlapping ones of the separate segments or clusters; and

identifying, using the computer, potential customers for customized marketing according to a correlation with the segments or clusters and the information generated about the purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers.

2. The method of claim 1, wherein collecting the empirical data further comprises collecting the empirical data as a byproduct of use of payment devices provided to the customers selected from a group consisting at least in part of credit cards, debit cards, stored value cards, and radio frequency identification devices.

3. The method of claim 1, wherein collecting the empirical data further comprises collecting the empirical data as a byproduct of use of benefit credentials selected from a group consisting at least in part of warranty cards, rebate forms, barcode scans, and proof of purchase data.

4. The method of claim 1, wherein identifying potential customers for customized marketing further comprises identifying customers within the segments or clusters who are customers of the client for customized marketing by the client according to a correlation with the segments or clusters.

5. The method of claim 1, wherein identifying potential customers for customized marketing further comprises identifying customers within the segments or clusters who are customers of a third party for customized marketing by the client according to a correlation with the segments or clusters.

6. The method of claim 1, wherein identifying potential customers for customized marketing further comprises identifying potential customers for customized marketing according to a correlation with the segments or clusters via indexing.

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

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

collecting empirical data for a client on actual purchasing behavior of a group of customers, said empirical data consisting of actual observed customer and purchase information associated with purchase behavior of the client'"'"'s customers and third parties'"'"' customers collected as a byproduct of use of payment devices and benefit credentials issued by the client and the third parties to their respective customers forming part of the group of customers;

applying statistical modeling techniques to the empirical purchasing behavior data to identify segments or clusters for an overall category of the client'"'"'s and the third parties'"'"' customers and for separate categories for the client'"'"'s customers and the third parties'"'"' customers;

identifying characteristics indicative of purchasing behavior for the segment or cluster for the overall category, comparing a relative presence of the client'"'"'s customers and the third parties'"'"' customers in the segment or cluster for the overall category and generating information about the purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers based at least in part on the identified characteristics of the segment or cluster for the overall category;

identifying characteristics indicative of purchasing behavior for overlapping and non-overlapping ones of the separate segments or clusters and generating information about purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers based at least in part on a comparison of the identified characteristics of the overlapping and non-overlapping ones of the separate segments or clusters; and

identifying potential customers for customized marketing according to a correlation with the segments or clusters and the information generated about the purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers.

8. A computer-implemented method for purchased-based segmentation of customers, comprising:

collecting, by a computer of a service provider, empirical data for a client on actual purchasing behavior of a group of customers, said empirical data consisting of actual observed customer and purchase information associated with purchase behavior of the client'"'"'s customers directly by the provider as a byproduct of use of payment devices and benefit credentials issued by the client to its customers forming part of the group of customers and consisting further of purchase information associated with purchase behavior of third parties'"'"' customers acquired indirectly by the provider from other sources collected as a byproduct of use of payment devices and benefit credentials issued by the third party to its customers forming part of the group of customers;

applying, by a computer, statistical modeling techniques to the empirical purchasing behavior data to identify segments or clusters for an overall category of the client'"'"'s and the third parties'"'"' customers and for separate categories for the client'"'"'s customers and the third parties'"'"' customers;

identifying, by a computer, characteristics indicative of purchasing behavior for the segment or cluster for the overall category, comparing a relative presence of the client'"'"'s customers and the third parties'"'"' customers in the segment or cluster for the overall category and generating information about the purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers based at least in part on the identified characteristics of the segment or cluster for the overall category;

identifying, by a computer, characteristics indicative of purchasing behavior for overlapping and non-overlapping ones of the separate segments or clusters and generating information about purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers based at least in part on a comparison of the identified characteristics of the overlapping and non-overlapping ones of the separate segments or clusters; and

identifying, by a computer, potential customers for customized marketing according to a correlation with the segments or clusters and the information generated about the purchasing behavior of the client'"'"'s customers and the third parties'"'"' customers via indexing.