Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching

• US RE42,577 E1
• Filed: 03/22/2010
• Issued: 07/26/2011
• Est. Priority Date: 05/06/1999
• Status: Active Grant

• Current Assignee: Callahan Cellular LLC (Intellectual Ventures LLC)
• Original Assignee: Kuhuro Investments AG LLC (Intellectual Ventures LLC)
• Inventors: Lazarus, Michael A., Russell, Gerald S., Sitze, Kevin L., Caid, William R., Dunning, Ted E., Peranich, Larry S., Vernhes, Frederique, Blume, Matthias
• Primary Examiner(s): Sterrett; Jonathan G
• Application Number:
  US12/729,215
• Time in Patent Office: 491 Days
• Field of Search: 705/10
• 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
• 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 computer-implemented method of predicting a target consumer'"'"'s response rates to a given offer, the method being performed by execution of computer readable program code by at least one processor of at least one computer system, the method comprising:

establishing, using at least one of the processors, a set of reference consumers, where actual financial behavior data is available for each reference consumer, the actual financial behavior data including known or substantially predictable response rates to the given offer arising from having been presented with the given offer or a substantially similar offer and given a chance to respond;

for each merchant in a merchant group, utilizing data from consumer transaction records to generate, using at least one of the processors, a merchant vector characterizing the merchant by relatedness to other merchants, the merchant vector being generated using an unexpected deviation learning approach to determine values of the merchant vectors, the unexpected deviation learning approach comparing co-occurrences of merchant descriptions in the actual financial behavior data to determine if a pair of merchants are either positively or negatively concurrent, the positive or negative concurrency determining values for the merchant vectors;

for the target consumer and each of the reference consumers, computing, using at least one of the processors, a consumer vector by summarizing merchant vectors for merchants at which the consumer made purchases during a prescribed period of time;

identifying, using at least one of the processors, one or more reference consumers whose consumer vectors are substantially similar to the consumer vector of the target consumer according to predetermined criteria;

computationally determining, using at least one of the processors, a predicted response rate of the target consumer to the given offer by aggregating said actual financial behavior data for a group of consumers limited to the following;

the identified consumers;

providing a machine-readable output of the prediction operation.

2. The method of claim 1, wherein the actual financial behavior data comprises one or more of:

consumers'"'"' offer response data;

consumers'"'"' purchase history data.

3. The method of claim 1, the identifying operation comprising:

performing, using at least one of the processors, respective dot-product operations upon the consumer vector of the target consumer with each consumer vector of the reference consumers;

selecting, using at least one of the processors, consumer vectors of the reference consumers according to one of the following;

highest dot-product value;

dot-product value exceeding a prescribed threshold;

a fixed number of consumer vectors with dot-product nearest to the consumer vector of the target consumer.

4. The method of claim 1, further comprising:

for various different offers, repeating the operations of predicting a target consumer'"'"'s response rates;

outputting recommendations as to which offers to present to which consumers, based on one or more of the following basis;

(1) recommending offers having a highest predicted response rate,

(2) recommending offers that are likely to achieve highest profit based on predicted response rate and product profit and cost of making the offer.

5. The method of claim 1, the aggregating operation comprising:

computing, using at least one of the processors, a ratio of reference consumers that responded positively to the given offer to reference consumers that did not respond positively.

6. The method of claim 1, where the operation of computing the consumer vector is performed so as to exclude one or both of the following:

(1) purchases of products represented by the given offer, (2) purchases from a given vendor.

7. The method of claim 1, where the operation of generating merchant vectors comprises operations of:

stemming and then equivalencing, where equivalencing further comprises;

applying fuzzy trigram matching, including using a vector representation of each trigram, based on trigram frequency in data set, to construct trigram vectors and judging closeness based on vector dot products;

determine a closeness score for each merchant name pair based on how many trigrams the merchant pair has in common, where sufficiently close scores are deemed to be equivalenced.

8. The method of claim 1, where the given offer comprises one or more of the following:

an offer of a specific product or service, a promotion, an offer to purchase generally from a specified vendor.

9. The method of claim 1, where the operation of computing consumer vectors is conducted so as to exclude consideration of demographic information pertaining to consumers.

10. A computer-implemented method of predicting a target consumer'"'"'s response rates to a given offer, the method being performed by execution of computer readable program code by at least one processor of at least one computer system, the method comprising:

computationally determining, using at least one of the processors, a predicted response rate of the target consumer to the given offer by aggregating actual financial behavior data for a group of consumers; and

providing a machine-readable output of the prediction operation;

wherein;

a set of reference consumers being established,

the actual financial behavior data being available for each reference consumer, the actual financial behavior data including known or substantially predictable response rates to the given offer arising from having been presented with the given offer or a substantially similar offer and given a chance to respond,

data from consumer transaction records for each merchant in a merchant group being utilized to generate a merchant vector characterizing the merchant by relatedness to other merchants, the merchant vector being generated using an unexpected deviation learning approach to determine values of the merchant vectors, the unexpected deviation learning approach comparing co-occurrences of merchant descriptions in the actual financial behavior data to determine if a pair of merchants are either positively or negatively concurrent, the positive or negative concurrency determining values for the merchant vectors,

a consumer vector being computed for the target consumer and each of the reference consumers, by summarizing merchant vectors for merchants at which the consumer made purchases during a prescribed period of time,

one or more reference consumers whose consumer vectors are substantially similar to the consumer vector of the target consumer being identified according to predetermined criteria.