Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Discount or incentive
Reexamination Certificate
1999-05-06
2002-08-06
Choi, Kyle J. (Department: 2763)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Discount or incentive
C706S006000, C705S014270, C705S026640
Reexamination Certificate
active
06430539
ABSTRACT:
BACKGROUND
1. Field of Invention
The present invention relates generally to analysis of consumer financial behavior, and more particularly to analyzing historical consumer financial behavior to accurately predict future spending behavior, and more particularly, future spending in specifically identified data-driven industry segments.
2. Background of Invention
Retailers, advertisers, and many other institutions are keenly interested in understanding consumer spending habits. These companies invest tremendous resources to identify and categorize consumer interests, in order to learn how consumers spend money. If the interests of an individual consumer can be determined, then it is believed that advertising and promotions related to these interests will be more successful in obtaining a positive consumer response, such as purchases of the advertised products or services.
Conventional means of determining consumer interests have generally relied on collecting demographic information about consumers, such as income, age, place of residence, occupation, and so forth, and associating various demographic categories with various categories of interests and merchants. Interest information may be collected from surveys, publication subscription lists, product warranty cards, and myriad other sources. Complex data processing is then applied to the source of data resulting in some demographic and interest description of each of a number of consumers.
This approach to understanding consumer behavior often misses the mark. The ultimate goal of this type of approach, whether acknowledged or not, is to predict consumer spending in the future. The assumption is that consumers will spend money on their interests, as expressed by things like their subscription lists and their demographics. Yet, the data on which the determination of interests is made is typically only indirectly related to the actual spending patterns of the consumer. For example, most publications have developed demographic models of their readership, and offer their subscription lists for sale to others interested in the particular demographics of the publication's readers. But subscription to a particular publication is a relatively poor indicator of what the consumer's spending patterns will be in the future.
Even taking into account multiple different sources of data, such as combining subscription lists, warranty registration cards, and so forth still only yields an incomplete collection of unrelated data about a consumer.
One of the problems in these conventional approaches is that spending patterns are time based. That is, consumers spend money at merchants which are of interest to them in typically a time related manner. For example, a consumer who is a business traveler spends money on plane tickets, car rentals, hotel accommodations, restaurants, and entertainment all during a single business trip. These purchases together more strongly describe the consumer's true interests and preferences than any single one of the purchases alone. Yet conventional approaches to consumer analysis typically treats these purchases individually and as unrelated in time.
Yet another problem with conventional approaches is that categorization of purchases is often based on standardized industry classifications of merchants and business, such as the SIC codes. This set of classification is entirely arbitrary, and has little to do with actual consumer behavior. Consumer do not decide which merchants to purchase from based on their SIC code. Thus, the use of arbitrary classifications to predict financial behavior is doomed to failure, since the classifications have little meaning in the actual data of consumer spending.
A third problem is that different groups of consumers spend money in different ways. For example, consumers who frequent high-end retailers have entirely different spending habits than consumers who are bargain shoppers. To deal with this problem, most systems focus exclusively on very specific, predefined types of consumers, in effect, assuming that the interests or types of consumers are known, and targeting these consumers with what are believed to be advertisements or promotions of interest to them. However, this approach essentially puts the cart before the proverbial horse: it assumes the interests and spending patterns of a particular group of consumers, it does not discover them from actual spending data. It thus begs the questions as to whether the assumed group of consumers in fact even exists, or has the interest that are assumed for it.
Accordingly, what is needed is the ability to model consumer financial behavior based on actual historical spending patterns that reflect the time-related nature of each consumer's purchase. Further, it is desirable to extract meaningful classifications of merchants based on the actual spending patterns, and from the combination of these, predict future spending of an individual consumer in specific, meaningful merchant groupings.
In the application domain of information, and particularly text retrieval, vector based representations of documents and words is known. Vector space representations of documents are described in U.S. Pat. No. 5,619,709 issued to Caid et. al, and in U.S. Pat. No. 5,325,298 issued to Gallant. Generally, vectors are used to represent words or documents. The relationships between words and between documents is learned and encoded in the vectors by a learning law. However, because these uses of vector space representations, including the context vectors of Caid, are designed for primarily for information retrieval, they are not effective for predictive analysis of behavior when applied to documents such as credit card statements and the like. When the techniques of Caid were applied to the prediction problems, it had numerous shortcomings. First, it had problems dealing with high transaction count merchants. These are merchants whose names appear very frequently in the collections of transaction statements. Because Caid's system downplays the significance of frequently appearing terms, these high transaction frequency merchants were not being accurately represented. Excluding high transaction frequency merchants from the data set however undermines the system's ability to predict transactions in these important merchants. Second, it was discovered that past two iterations of training, Caid's system performance declined, instead of converging. This indicates that the learning law is learning information that is only coincidental to transaction prediction, instead of information that is specifically for transaction prediction. Accordingly, it is desirable to provide a new methodology for learning the relationships between merchants and consumers so as to properly reflect the significance of the frequency with which merchants appears in the transaction data.
SUMMARY OF THE INVENTION
The present invention overcomes the limitations of conventional approaches to consumer analysis by providing a system and method of analyzing and predicting consumer financial behavior that uses historical, and time-sensitive, spending patterns of individual consumers to create both meaningful groupings (segments) of merchants which accurately reflect underlying consumer interests, and a predictive model of consumer spending patterns for each of the merchant segment. Current spending data of an individual consumer or groups of consumers can then be applied to the predictive models to predict future spending of the consumers in each of the merchant clusters.
In one aspect, the present invention includes the creation of data-driven grouping of merchants, based essentially on the actual spending patterns of a group of consumers. Spending data of each consumer is obtained, which describes the spending patterns of the consumers in a time-related fashion. For example, credit card data demonstrates not merely the merchants and amounts spent, but also the sequence in which purchases were made. One of the features of the invention is its ability to use the
Blume A. U. Mattias
Brown Kenneth B.
Caid William R.
Dunning Ted E.
Lazarus Michael A.
Choi Kyle J.
Fenwick & West LLP
HNC Software
Robertson D
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