Predictive Modeling of Consumer Financial Behavior Using Supervised Segmentation and Nearest-Neighbor Matching

• US 20070244741A1
• Filed: 01/15/2007
• Published: 10/18/2007
• Est. Priority Date: 05/06/1999
• Status: Active Grant

• Current Assignee: Callahan Cellular LLC (Intellectual Ventures LLC)
• Original Assignee: Fair Isaac Corporation
• Inventors: Blume, Matthias, Sitze, Kevin, Vernhes, Frederique, Lazarus, Michael, Peranich, Larry, Brown, Kenneth, Russell, Gerald, Caid, William, Dunning, Ted
• Granted Patent: US 7,533,038 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• 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 30/0207 Discounts or incentives, e.g. coupons or rebates
• 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 30/0601 Electronic shopping [e-shopping]

Abstract

Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer'"'"'s spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors, thus facilitating the targeting of promotional offers to consumers most likely to respond positively.

Claims

1. A method of predicting financial behavior of a target consumer with respect to an offer or merchant, comprising:

generating a merchant vector for at least a subset of merchants;

for a reference set of consumers, obtaining consumer vectors and data describing financial behavior;

obtaining a consumer vector for the target consumer;

identifying at least one nearest neighbor to the target consumer vector among the reference set consumer vectors; and

generating a behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors.

2. The method of claim 1, wherein the data describing financial behavior comprises offer response data for the consumer.

3. The method of claim 1 wherein the data describing financial behavior comprises purchase history data for the consumer.

4. A method of predicting financial behavior of a target consumer with respect to an offer or merchant, comprising:

generating a merchant vector for at least a subset of merchants;

generating consumer vectors for a plurality of consumers;

defining at least one consumer segment having predicted financial behavior data;

determining a consumer segment for the target consumer; and

based on the determined consumer segment, generating predicted financial behavior for the target consumer.

5. A system for predicting financial behavior of a target consumer with respect to an offer or merchant, comprising:

a merchant vector build module, for generating a merchant vector for at least a subset of merchants;

an input device for obtaining, for a reference set of consumers, consumer vectors and data describing financial behavior; and

at least one merchant segment predictive model, coupled to the build module and the input device, for identifying at least one nearest neighbor to a target consumer vector among the reference set consumer vectors, and generating a behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors.

6. The system of claim 5, wherein the data describing financial behavior comprises offer response data for the consumer.

7. The system of claim 5, wherein the data describing financial behavior comprises purchase history data for the consumer.

8. A system for predicting financial behavior of a target consumer with respect to an offer or merchant, comprising:

a merchant vector build module, for generating a merchant vector for at least a subset of merchants;

a consumer vector build module, for generating consumer vectors for a plurality of consumers;

at least one consumer segment predictive model, coupled to the vector build modules, for defining at least one consumer segment having predicted financial behavior data, determining a consumer segment for the target consumer, and, based on the determined consumer segment, generating predicted financial behavior for the target consumer.

9. A computer-readable medium comprising computer-readable code for predicting financial behavior of a target consumer with respect to an offer or merchant, comprising:

computer-readable code adapted to generate a merchant vector for at least a subset of merchants;

computer-readable code adapted to, for a reference set of consumers, obtain consumer vectors and data describing financial behavior;

computer-readable code adapted to obtain a consumer vector for the target consumer;

computer-readable code adapted to identify at least one nearest neighbor to the target consumer vector among the reference set consumer vectors; and

computer-readable code adapted to generate a behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors.

10. The computer-readable medium of claim 9, wherein the data describing financial behavior comprises offer response data for the consumer.

11. The computer-readable medium of claim 9, wherein the data describing financial behavior comprises purchase history data for the consumer.

12. A computer-readable medium comprising computer-readable code for predicting financial behavior of a target consumer with respect to an offer or merchant, comprising:

computer-readable code adapted to generate a merchant vector for at least a subset of merchants;

computer-readable code adapted to generate consumer vectors for a plurality of consumers;

computer-readable code adapted to define at least one consumer segment having predicted financial behavior data;

computer-readable code adapted to determine a consumer segment for the target consumer; and

computer-readable code adapted to, based on the determined consumer segment, generate predicted financial behavior for the target consumer.