SYSTEM AND METHOD FOR MATCHING CONSUMERS BASED ON SPEND BEHAVIOR

• US 20110178848A1
• Filed: 01/20/2010
• Published: 07/21/2011
• Est. Priority Date: 01/20/2010
• Status: Abandoned Application

• Current Assignee: Liberty Peak Ventures, LLC (Dominion Harbor Enterprises, LLC)
• Original Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Inventors: Schwartz, Melissa, Rane, Rajendra R.
• Application Number:
  US12/690,798
• Publication Number:
  US 20110178848A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• 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

Abstract

The present invention improves upon existing systems and methods by providing a passive profile creation method. The data accessible to a financial processor, such as spend level data, is leveraged using sophisticated data clustering and/or data appending techniques. Associations are established among entities (e.g., consumers), among merchants, and between entities and merchants. In one embodiment, a system and method for passively collecting spend level data for a transaction of a first entity, aggregating the collected spend level data for a plurality of entities; and clustering the first entity with a subset of the plurality of entities, based on aggregated spend level data of the first entity is provided.

Claims

1. A method comprising:

passively collecting spend level data for a transaction of a first entity;

aggregating the collected spend level data for a plurality of entities; and

clustering the first entity with a subset of the plurality of entities, based on aggregated spend level data of the first entity; and

matching a first cluster member with a second cluster member.

2. The method of claim 1, wherein clustering comprises,

assigning a weighted percentile to the spend level data of the first entity within merchant category codes for a plurality of merchant category codes;

selecting a weight percentile across a merchant category codes; and

grouping a first entity with other entities based upon the selecting.

3. The method of claim 2, the selected weight percentile is the median percentile of each cluster.

4. The method of claim 1, wherein the spend level data comprises at least one of transaction data, or consumer account data.

5. The method of claim 1, wherein passively collecting spend level data of the first entity includes acquiring the spend level data from a merchant.

6. The method of claim 1, wherein passively collecting the spend level data of a first entity includes collecting the spend level data from a transaction database.

7. The method of claim 1, wherein passively collecting spend level data of the first entity includes acquiring the spend level data in response to a transaction by the first entity with a merchant.

8. The method of claim 1, wherein aggregating the collected spend level data comprises combining a selectable range of collected spend level data.

9. The method of claim 1, wherein clustering the entity based on the aggregated spend level data of a first entity comprises using a computer implemented statistical analysis algorithm to:

assign a weighted percentile to the spend level data of the first entity for spend level data assigned a merchant category code for a plurality of merchant category codes;

select a weight percentile across a merchant category codes; and

group a first entity with other entities based upon the selecting.

10. The method of claim 1, wherein the attributes of the first entity within a first cluster are as similar to the aggregate attributes of other first cluster members as possible.

11. The method of claim 1, wherein the aggregate attributes of the members of a first cluster are as dissimilar to the aggregate attributes the members of a second cluster as possible.

12. The method of claim 1, further comprising

appending the clustered data with entity characteristic data;

analyzing the appended clustered data; and

drawing inferences about cluster members based on the analyzing.

13. The method of claim 12, wherein drawing inferences about cluster members comprises reporting measurable results based on the comparisons.

14. The method of claim 13, wherein the measurable results comprise at least one of age, payment method type, martial status, homeowner status, renter status, family member size, loyalty program membership, debt held, credit score, purchasing power, activities preferred, size of wallet, payments to a particular industry, top merchants within top merchant category, religious affiliation, employment status, sexual orientation, geographic highest education level completed, ethnicity, handicap status, change in spending habits, political affiliation, affinity group membership, income level, or frequency of transactions.

15. The method of claim 12, wherein drawing inferences about cluster members from appended clustered data comprises utilizing present and absent data.

16. The method of claim 1, wherein matching the first cluster member with the second cluster member comprises providing a forum for interaction between cluster members.

17. The method of claim 10, wherein the forum is an electronic communication forum.

18. The method of claim 10, wherein the forum is a website.

19. A system configured to:

passively collect spend level data for a transaction of a first entity;

aggregate the collected spend level data for a plurality of entities;

cluster the first entity with a subset of the plurality of entities, based on aggregated spend level data of the first entity; and

match a first cluster member with a second cluster member.

20. A computer readable medium having instructions stored thereon that, if executed by a computing device, cause the computing device to perform a method comprising:

passively collect spend level data for a transaction of a first entity;

aggregate the collected spend level data for a plurality of entities;

cluster the first entity with a subset of the plurality of entities, based on aggregated spend level data of the first entity; and

match a first cluster member with a second cluster member.