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

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, which 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. Supervised segmentation is applied to merchant vectors to form 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. 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.

Description

CROSS-REFERENCE TO RELATED APPLICATION

where 0<_(t)<1, and _— is decreasing monotonically with time (e.g. linearly, starting from a small value like 0.01 or 0.02).

Meanwhile, adjustment of SV1 as shown in FIG. 11C may be described as
SV₁(t+1)=SV₁(t)+_(t)[MV(t)−SV₁(t)]

where 0<_(t)<1, and _— is decreasing monotonically with time (e.g. linearly, starting from a small value like 0.01 or 0.02).

If, in 1006, the selected segment does correspond to the segment label that has been assigned to the merchant, zero or more segment vectors are adjusted 1008. Either the segment vectors are left unchanged, or in an alternative embodiment, the assigned segment vector is moved closer to the merchant vector.

Referring to FIGS. 12A through 12C, there is shown an example of a segment vector adjustment that may be performed when the selected segment does correspond to the segment label assigned to the merchant. FIG. 12A depicts a starting position for a merchant vector MV and three segment vectors SV₁, SV₂, and SV₃. MV is assumed to have been manually assigned to segment 1, corresponding to segment vector SV₁. It can be seen from the starting positions shown in FIG. 12A that the segment vector closest to merchant vector MV is SV₁ which does correspond to the assigned segment. Accordingly, either the vectors are left unchanged as shown in FIG. 12B, or, as shown in FIG. 12C, segment vector SV₁ is moved closer to MV, so as to reflect the fact that MV was correctly assigned to SV₁.

The degree and direction of adjustment may be determined by any desired means. For example, as described in Kohonen (1990), adjustment of SV₁ as shown in FIG. 12C may be described as
SV₁(t+1)=SV₁(t)+_(t)[MV(t)−SV₁(t)]

where 0<_(t)<1, and _— is decreasing monotonically with time (e.g. linearly, starting from a small value like 0.01 or 0.02).

In yet another embodiment, segment membership is non-exclusive, so that a merchant may be a member of more than one segment. A tolerance radius is established around each segment vector; this tolerance radius may be expressed as a maximum allowable distance from the endpoint of the segment vector on the surface of a unit sphere. The tolerance radius may also be expressed as a minimum value resulting from a dot-product operation on the segment vector and a merchant vector; if the dot-product value exceeds this threshold value, the merchant is designated a member of the segment.

Rather than adjusting segment vectors based on a determination of which segment vector is closest to the merchant vector, in this embodiment segment vectors are adjusted based on a determination of the merchant vector falling within the tolerance radius for one or more segment vectors. Adjustment of segment vectors may be performed as follows. Segment labels are assigned to a merchant as described above in step 1003 of FIG. 10. The merchant vector is compared with segment vectors in order to determine whether the merchant vector falls within the predefined tolerance radius for each segment vector. For each segment for which the merchant vector falls within the tolerance radius of the segment vector:

• • If the segment is one whose label was not assigned to the merchant, adjust the segment vector to be farther from the merchant vector (FIG. 11B) and/or adjust other segment vectors corresponding to segments whose labels were assigned to the merchant to be closer to the merchant vector (FIG. 11C).
  • If the segment is one whose label was assigned to the merchant, either do nothing (FIG. 12B) or adjust the segment vector to be closer to the merchant vector (FIG. 12C).

Once segments have been adjusted (if appropriate), a determination is made 1007 as to whether more training is required. This determination is made based on known convergence determination methods, or by reference to a predefined count of training iterations, or by other appropriate means. One advantage to the present invention is that not all merchants need be manually labeled in order to effectively train the vector set; once the segment vectors are sufficiently trained, merchants will automatically become associated with appropriate segments based on the positioning of their vectors.

As will be apparent to one skilled in the art, the supervised segmentation approach provides an alternative to unsupervised data-driven segmentation methods, and facilitates analysis of particular market segments or merchant types that are of interest. Thus, the above-described approach may be employed in place of the clustering methods previously described.

As indicated above, the various techniques of the present invention can be applied to other domains and environments. Thus references to “merchants,” “accounts,” and “customers” are merely exemplary, and are not intended to limit the scope of the invention.

D. Data Preprocessing Module

The data preprocessing module 402 (DPM) does initial processing of consumer data received from a source of consumer accounts and transactions, such as a credit card issuer, in preparation for creating the merchant vectors, consumer vectors, and merchant segment predictive models. DPM 402 is used in both production and training modes. (In this disclosure, the terms “consumer,” “customer,” and “account holder” are used interchangeably).

The inputs for the DPM are the consumer summary file 404 and the consumer transaction file 406. Generally, the consumer summary file 404 provides account data on each consumer who transaction data is to be processed, such as account number and other account identifying and descriptive information. The consumer transaction file 406 provides details of each consumer's transactions. The DPM 402 processes these files to organize both sets of data by account identifiers of the consumer accounts, and merges the data files so that each consumer's summary data is available with their transactions.

Customer summary file 404: The customer summary file 404 contains one record for each customer that is profiled by the system, and includes account information of the customer's account, and optionally includes demographic information about the customer. The consumer summary file 404 is typically one that a financial institution, such as a bank, credit card issuer, department store, and the like maintains on each consumer. The customer or the financial institution may supply the additional demographic fields that are deemed to be of informational or of predictive value. Examples of demographic fields include age, gender and income; other demographic fields may be provided, as desired by the financial institution.

Table 1 describes one set of fields for the customer summary file 404 for a preferred embodiment. Most fields are self-explanatory. The only required field is an account identifier that uniquely identifies each consumer account and transactions. This account identifier may be the same as the consumer's account number; however, it is preferable to have a different identifier used, since a consumer may have multiple account relationships with the financial institution (e.g. multiple credit cards or bank accounts), and all transactions of the consumer should be dealt with together. The account identifier is preferably derived from the account number, such as by a one-way hash or encrypted value, such that each account identifier is uniquely associated with an account number. The pop_id field is optionally used to segment the population of customers into arbitrary distinct populations as specified by the financial institution, for example by payment history, account type, geographic region, etc.

TABLE 1

Customer Summary File
Description          Sample Format

Account_id           Char[max 24]
Pop_id               Char (‘1’-‘N’)
Account_number       Char[max 16]
Credit bureau        Short int as
score                string
Internal credit risk Short int as
score                string
Ytd purchases        Int as string
Ytd_cash_adv         Int as string
Ytd_ int_purchases   Int as string
Ytd_int_cash_adv     Int as string
State_code           Char[max 2]
Zip_code             Char[max 5]
Demographic_1        Int as string
.
.
.
Demographic_N        Int as string

Note the additional, optional demographic fields for containing demographic information about each consumer. In addition to demographic information, various summary statistics of the consumer's account may be included. These include any of the following:

TABLE 2

Example Demographic Fields for Customer Summary File
Description                Explanation

Cardholder zip code
Months on books or open
date
Number of people on the    Equivalent to number of plastics
account
Credit risk score
Cycles delinquent
Credit line
Open to buy
Initial month statement    Balance on the account prior to
balance                    the first month of transaction
                           data pull
Last month statement       Balance on the account at the
balance                    end of the transaction data
                           pulled
Monthly payment amount     For each month of transaction
                           data contributed or the average
                           over last year.
Monthly cash advance       For each month of transaction
amount                     data contributed or the average
                           over last year.
Monthly cash advance count For each month of transaction
                           data contributed or the average
                           over last year.
Monthly purchase amount    For each month of transaction
                           data contributed or the average
                           over last year.
Monthly purchase count     For each month of transaction
                           data contributed or the average
                           over last year.
Monthly cash advance       For each month of transaction
interest                   data contributed or the average
                           over last year.
Monthly purchase interest  For each month of transaction
                           data contributed or the average
                           over last year.
Monthly late charge        For each month of transaction
                           data contributed or the average
                           over last year.

Consumer transaction file 406. The consumer transaction file 406 contains transaction level data for the consumers in the consumer summary file. The shared key is the account_id. In a preferred embodiment, the transaction file has the following description.

TABLE 3

Consumer Transaction File
Description         Sample Format

Account_id          Quoted char(24) - [0-9]
Account_number      Quoted char(16) - [0-9]
Pop_id              Quoted char(1) - [0-128]
Transaction_code    Integer
Transaction_amount  Float
Transaction_time    HH:MM:SS
Transaction_date    YYYYMMDD
Transaction_type    Char(5)
SIC_code            Char(5) - [0-9]
Merchant_descriptor Char(25)
SKU Number          Variable length list
Merchant zip code   Char[max 5]

The SKU and merchant zip code data are optional, and may be used for more fine-grained filtering of which transactions are considered as co-occurring.

The output for the DPM is the collection of master files 408 containing a merged file of the account information and transaction information for each consumer. The master file is generated as a preprocessing step before inputting data to the profiling engine 412. The master file 408 is essentially the customer summary file 404 with the consumer's transactions appended to the end of each consumer's account record. Hence the master file has variable length records. The master files 408 are preferably stored in a database format allowing for SQL querying. There is one record per account identifier.

In a preferred embodiment, the master files 408 have the following information:

TABLE 4

Master File 408
Description               Sample Format

Account_id                Char[max 24]
Pop_id                    Char (‘1’-‘N’)
Account_number            Char[max 16]
Credit bureau score       Short int as string
Ytd purchases             Int as string
Ytd_cash_advances         Int as string
Ytd_interest_on_purchases Int as string
Ytd_interest_on_cash_advs Int as string
State_code                Char[max 2]
Demographic_1             Int as string
.
.
.
Demographic_N             Int as string
<transactions>

The transactions included for each consumer include the various data fields described above, and any other per-transaction optional data that the financial institution desires to track.

The master file 408 preferably includes a header that indicates last update and number of updates. The master file may be incrementally updated with new customers and new transactions for existing customers. The master file database is preferably be updated on a monthly basis to capture new transactions by the financial institution's consumers.

The DPM 402 creates the master file 408 from the consumer summary file 404 and consumer transaction file 406 by the following process:

• • a) Verify minimum data requirements. The DPM 402 determines the number of data files it is handling (since there maybe many physical media sources), and the length of the files to determine the number of accounts and transactions. Preferably, a minimum of 12 months of transactions for a minimum of 2 million accounts is used to provide fully robust models of merchants and segments. However, there is no formal lower bound to the amount of data on which system 400 may operate.
  • b) Data cleaning. The DPM 402 verifies valid data fields, and discards invalid records. Invalid records are records that are missing the any of the required fields for the customer summary file of the transaction file. The DPM 402 also indicates missing values for fields that have corrupt or missing data and are optional. Duplicate transactions are eliminated using account ID, account number, transaction code, transaction amount, date, and merchant description as a key.
  • c) Sort and merge files. The consumer summary file 404 and the consumer transaction file 406 are both sorted by account ID; the consumer transaction file 406 is further sorted by transaction date. Additional sorting of the transaction file, for example on time, type of transaction, merchant zip code, may be applied to further influence the determination of merchant co-occurrence. The sorted files are merged into the master file 408, with one record per account, as described above.

Due to the large volume of data involved in this stage, compression of the master files 408 is preferred, where on-the-fly compression and decompression is supported. This often improves system performance due to decreased I/O. In addition, as illustrated in FIG. 4a, the master file 408 may be split into multiple subfiles, such as splitting by population ID, or other variable, again to reduce the amount of data being handled at any one time.

E. Predictive Model Generation System

Referring to FIG. 4b, the predictive model generation system 440 takes as its inputs the master file 408 and creates the consumer profiles and consumer vectors, the merchant vectors and merchant segments, and the segment predictive models. This data is used by the profiling engine to generate predictions of future spending by a consumer in each merchant segment using inputs from the data postprocessing module 410.

FIG. 5 illustrates one embodiment of the predictive model generation system 440 that includes three modules: a merchant vector generation module 510, a clustering module 520, and a predictive model generation module 530.

1. Merchant Vector Generation

Merchant vector generation is application of a context vector type analysis to the account data of the consumers, and more particularly to the master files 408. The operations for merchant vector generation are managed by the merchant vector generation module 510.

In order to obtain the initial merchant vectors, additional processing of the master files 408 precedes the analysis of which merchants co-occur in the master files 408. There are two, sequential, processes that are used on the merchant descriptions, stemming and equivalencing. These operations normalize variations of individual merchants names to a single common merchant name to allow for consistent identification of transaction at the merchant. This processing is managed by the vector generation module 510.

Stemming is the process of removing extraneous characters from the merchant descriptions. Examples of extraneous characters include punctuation and trailing numbers. Trailing numbers are removed because they usually indicate the particular store in a large chain (e.g. Wal-Mart #12345). It is preferable to identify all the outlets of a particular chain of stores as a single merchant description. Stemming optionally converts all letters to lower case, and replaces all space characters with a dash. This causes all merchant descriptions to be an unbroken string of non-space characters. The lower case constraint has the advantage of making it easy to distinguish non-stemmed merchant descriptions from stemmed descriptions.

Equivalencing is applied after stemming, and identifies various different spellings of a particular merchant's description as being associated with a single merchant description. For example, the “Roto-Rooter” company may occur in the transaction data with the following three stemmed merchant descriptions: “ROTO-ROOTER-SEWER-SERV”, “ROTO-ROOTER-SERVICE”, and “ROTO-ROOTER-SEWER-DR”. An equivalence table is set up containing a root name and a list of all equivalent names. In this example, ROTO-ROOTER-SEWER-SERV becomes the root name, and the latter two of these descriptions are listed as equivalents. During operation, such as generation of subsequent master files 408 (e.g. the next monthly update), an identified equivalenced name is replaced with its root name from the equivalence table.

In one embodiment, equivalencing proceeds in two steps, with an optional third step. The first equivalencing step uses a fuzzy trigram-matching algorithm that attempts to find merchant descriptions with nearly identical spellings. This method collects statistics on all the trigrams (sets of three consecutive letters in a word) in all the merchant descriptions, and maintains a list of the trigrams in each merchant description. The method then determines a closeness score for any two merchant names that are supplied for comparison, based on the number of trigrams the merchant names have in common. If the two merchant names are scored as being sufficiently close, they are equivalenced. Appendix I, below, provides a novel trigram-matching algorithm useful for equivalencing merchant names (and other strings). This algorithm uses a vector representation of each trigram, based on trigram frequency in data set, to construct trigram vectors, and judges closeness based on vector dot products.

Preferably, equivalencing is applied only to merchants that are assigned the same SIC code. This constraint is useful since two merchants may have a similar name, but if they are in different SIC classifications there is a good chance that they are, in fact, different businesses.

The second equivalencing step consists of fixing a group of special cases. These special cases are identified as experience is gained with the particular set of transaction data being processed. There are two broad classes that cover most of these special cases: a place name is used instead of a number to identify specific outlets in a chain of stores, and some department stores append the name of the specific department to the name of the chain. An example of the first case is U-Haul, where stemmed descriptions look like U-HAUL-SAN-DIEGO, U-HAUL-ATLANTA, and the like. An example of the second case is Robinsons-May department stores, with stemmed descriptions like ROBINSONMAY-LEE-WOMEN, ROBINSONMAY-LEVI-SHORT, ROBINSONMAY-TRIFARI-CO, and ROBINSON-MAY-JANE-ASHLE. In both cases, any merchant description in the correct SIC codes that contain the root name (e.g. U-HAUL or ROBINSONMAY) are equivalenced to the root name.

A third, optional step includes a manual inspection and correction of the descriptions for the highest frequency merchants. The number of merchants subjected to this inspection varies, depending upon the time constraints in the processing stream. This step catches the cases that are not amenable to the two previous steps. An example is Microsoft Network, with merchant descriptions like MICROSOFT-NET and MSN-BILLING. With enough examples from the transaction data, these merchant descriptors can also be added to the special cases in step two, above.

Preferably, at least one set of master files 408 is generated before the equivalencing is determined. This is desirable in order to compile statistics on frequencies of each merchant description within each SIC code before the equivalencing is started.

Once the equivalencing table is constructed, the original master files 408 are re-built using the equivalenced merchant descriptions. This step replaces all equivalenced merchant descriptors with their associated root names, thereby ensuring that all transactions for the merchant are associated with the same merchant descriptor. Subsequent incoming transaction data can be equivalenced before it is added to the master files, using the original equivalence table.

Given the equivalence table, a merchant descriptor frequency list can be determined describing the frequency of occurrence of each merchant descriptor (including its equivalents).

Once the equivalence table is defined an initial merchant vector is assigned to each root name. The merchant vector training based on co-occurrence is then performed, processing the master files by account ID and then by date as described above.

2. Training of Merchant Vectors: The UDL Algorithm

As noted above, the merchant vectors are based on the co-occurrence of merchants in each consumer's transaction data. The master files 408, which are ordered by account and within account by transaction date, are processed by account, and then in date order to identify groups of co-occurring merchants. The co-occurrence of merchant names (once equivalenced) is the basis of updating the values of the merchant vectors.

The training of merchant vectors is based upon the unexpected deviation of co-occurrences of merchants in transactions. More particularly, an expected rate at which any pair of merchants co-occur in the transaction data is estimated based upon the frequency with which each individual merchant appears in co-occurrence with any other merchants, and a total number of co-occurrence events. The actual number of co-occurrences of a pair of merchants is determined. If a pair of merchants co-occur more frequently then expected, then the merchants are positively related, and the strength of that relationship is a function of the “unexpected” amount of co-occurrence. If the pair of merchants co-occurs less frequently than expected, then the merchants are negatively related. If a pair of merchants co-occurs in the data about the same as expected, then there is generally no relationship between them. Using the relationship strengths of each pair of merchants as the desired dot product between the merchant vectors, the values of the merchant vectors can be determined in the vector space. This process is the basis of the Unexpected Deviation Learning algorithm or “UDL”.

This approach overcomes the problems associated with conventional vector based models of representation, which tend to be based on overall frequencies of terms relative to the database as a whole. Specifically, in a conventional model, the high frequency merchants, that is merchants for which there are many, many purchases, would co-occur with many other merchants, and either falsely suggest that these other merchants are related to the high frequency merchants, or simply be so heavily down-weighted as to have very little influence at all. That is, a high frequency merchant names would be treated as high frequency English words like “the” and “and”, and so forth, which are given very low weights in conventional vector systems specifically because of their high frequency.

However, the present invention takes account of the high frequency presence of individual merchants, and instead analyses the expected rate at which merchants, including high frequency merchants, co-occur with other merchants. High frequency merchants are expected to co-occur more frequently. If a high frequency merchant and another merchant co-occur even more frequently than expected, then there is a positive correlation between them. The present invention thus accounts for the high frequency merchants in a manner that conventional methodologies cannot.

The overall process of modeling the merchant vectors using unexpected deviation is as follows:

• 1. First, count the number of times that the merchants co-occur with one another in the transaction data. The intuition is that related merchants occur together often, whereas unrelated merchants do not occur together often.
• 2. Next, calculate the relationship strength between merchants based on how much the observed co-occurrence deviated from the expected co-occurrence. The relationship strength has the following characteristics:
  • Two merchants that co-occur significantly more often than expected are positively related to one another.
  • Two merchants that co-occur significantly less often than expected are negatively related to one another.
  • Two merchants that co-occur about the number of times expected are not related.
• 3. Map the relationship strength onto vector space; that is, determine the desired dot product between the merchant vectors for all pairs of items given their relationship strength. The mapping results in the following characteristics:
  • The merchant vectors for positively related merchants have a positive dot product.
  • The merchant vectors for negatively related merchants have a negative dot product.
  • The merchant vectors for unrelated merchants have a zero dot product.
• 4. Update the merchant vectors from their initial assignments, so that the dot products between them at least closely approximate the desired dot products.

The next sections explain this process in further detail.

a) Co-Occurrence Counting

Co-occurrence counting is the procedure of counting the number of times that two items, here merchant descriptions, co-occur within a fixed size co-occurrence window in some set of data, here the transactions of the consumers. Counting can be done forwards, backwards, or bi-directionally. The best way to illustrate co-occurrence counting is to give an example for each type of co-occurrence count:

Example: Consider the sequence of merchant names:

M1 M3 M1 M3 M3 M2 M3

where M1, M2 and M3 stands for arbitrary merchant names as they might appear in a sequence of transactions by a consumer. For the purposes of this example, intervening data, such as dates of transactions, amounts, transaction identifiers, and the like, are ignored. Further assume a co-occurrence window with a size=3. Here, the co-occurrence window is based on a simple count of items or transactions, and thus the co-occurrence window represents a group of three transactions in sequence.

i) Forward Co-Occurrence Counting

The first step in the counting process is to set up the forward co-occurrence windows. FIG. 6a illustrates the co-occurrence windows 602 for forward co-occurrence counting of this sequence of merchant names. By definition, each merchant name is a target 604, indicated by an arrow, for one and only one co-occurrence window 602. Therefore, in this example there are seven forward co-occurrence windows 602, labeled 1 through 7. The other merchant names within a given co-occurrence window 602 are called the neighbors 606. In forward co-occurrence counting, the neighbors occur after the target. For window size=3 there can be at most three neighbors 606 within a given co-occurrence window 602. Obviously, the larger the window size, the more merchants (and transactions) are deemed to co-occur at a time.

The next step is to build a table containing all co-occurrence events. A co-occurrence event is simply a pairing of a target 604 with a neighbor 606. For the co-occurrence window #1 in FIG. 6a, the target is M1 and the neighbors are M3, M1 and M3. Therefore, the co-occurrence events in this window are: (M1, M3), (M1, M1), and (M1, M3). Table 5 contains the complete listing of co-occurrence events for every co-occurrence window in this example.

TABLE 5
Forward co-occurrence event table
Co-occurrence
Window	Target	Neighbor
1	M1	M3
1	M1	M1
1	M1	M3
2	M3	M1
2	M3	M3
2	M3	M3
3	M1	M3
3	M1	M3
3	M1	M2
4	M3	M3
4	M3	M2
4	M3	M3
5	M3	M2
5	M3	M3
6	M2	M3

The last step is to tabulate the number of times that each unique co-occurrence event occurred. A unique co-occurrence event is the combination (in any order) of two merchant names. Table 6 shows this tabulation in matrix form. The rows indicate the targets and the columns indicate the neighbors. For future reference, this matrix will be called the forward co-occurrence matrix.

TABLE 6

Forward Co-occurrence matrix

Neighbor
##STR00001##

ii) Backward Co-Occurrence Counting

Backward co-occurrence counting is done in the same manner as forward co-occurrence counting except that the neighbors precede the target in the co-occurrence windows. FIG. 6b illustrates the co-occurrence windows for the same sequence of merchant names for backward co-occurrence counting.

Once the co-occurence windows are specified, the co-occurrence events can be identified and counted.

TABLE 7
Backward co-occurrence event table
Co-occurrence
Window	Target	Neighbor
1	M3	M2
1	M3	M3
1	M3	M3
2	M2	M3
2	M2	M3
2	M2	M1
3	M3	M3
3	M3	M1
3	M3	M3
4	M3	M1
4	M3	M3
4	M3	M1
5	M1	M3
5	M1	M1
6	M3	M1

The number of times that each unique co-occurrence event occurred is then recorded in the backward co-occurrence matrix.

TABLE 8

Backward Co-occurrence matrix

Neighbor
##STR00002##

Note that the forward co-occurrence matrix and the backward co-occurrence matrix are the transpose of one another. This relationship is intuitive, because backward co-occurrence counting is the same as forward co-occurrence counting with the transaction stream reversed. Thus, there is no need to do both counts; either count can be used, and then the transpose the resulting co-occurrence matrix taken to obtain the other.

iii) Bi-Directional Co-Occurrence Counting

The bi-directional co-occurrence matrix is just the sum of the forward co-occurrence matrix and the backward co-occurrence matrix. The resulting matrix will always be symmetric. In other words, the co-occurrence between merchant names A and B is the same as the co-occurrence between merchant names B and A. This property is desirable because this same symmetry is inherent in vector space; that is for merchant vectors V_A and V_B for merchants A and B, V_A· V_B= V_B· V_A. For this reason, the preferred embodiment uses the bi-directional co-occurrence matrix.

TABLE 9

Bi-directional Co-occurrence matrix

Neighbor
##STR00003##

FIGS. 7a and 7b illustrate the above concepts in the context of consumer transaction data in the master files 408. In FIG. 7a there is shown a portion of the master file 408 containing transactions of a particular customer. This data is prior to the stemming and equivalencing steps described above, and so includes the original names of the merchants with spaces, store numbers and locations and other extraneous data.

FIG. 7b illustrates the same data after stemming and equivalencing. Notice that the two transactions at STAPLES that previously identified a store number are now equivalenced. The two car rental transactions at ALAMO which transactions previously included the location are equivalenced to ALAMO, as are two hotel stays at HILTON that also previously included the hotel location. Further note that the HILTON transactions specified the location prior to the hotel name. Finally, the two transactions at NORDSTROMS that previously identified a department have been equivalenced to the store name itself.

Further, a single forward co-occurrence window 700 is shown with the target 702 being the first transaction at the HILTON, and the next three transactions being neighbors 704.

Accordingly, following the updating of the master files 408 with the stemmed and equivalenced names, the merchant vector generation module 510 performs the following steps for each consumer account:

• • 1. Read the transaction data in date order.
  • 2. Forward count the co-occurrences of merchant names in the transaction data, using a predetermined co-occurrence window.
  • 3. Generate the forward co-occurrence, backward co-occurrence and bi-directional co-occurrence matrixes.

One preferred embodiment uses a co-occurrence window size of three transactions. This captures the transactions as the co-occurring events (and not the presence of merchant names within three words of each other) based only on sequence. In an alternate embodiment the co-occurrence window is time-based using a date range in order to identify co-occurring events. For example, with a co-occurrence window of 1 week, given a target transaction, a co-occurring neighbor transaction occurs within one week of the target transaction. Yet another date approach is to define the target not as a transaction, but rather as a target time period, and then the co-occurrence window as another time period. For example, the target period can be a three-month block and so all transactions within the block are the targets, and then the co-occurrence window may be all transactions in the two months following the target period. Thus, each merchant having a transaction in the target period co-occurs with each merchant (same or other) having a transaction in the co-occurrence period. Those of skill in the art can readily devise alternate co-occurrence definitions that capture the sequence and/or time related principles of co-occurrence in accordance with the present invention.

b) Estimating Expected Co-Occurrence Counts

In order to determine whether two merchants are related, the UDL algorithm uses an estimate about the number of times transactions at such merchants would be expected to occur. Suppose the only information known about transaction data is the number of times that each merchant name appeared in co-occurrence events. Given no additional information, the correlation between any two merchant names, that is how strongly they are related, cannot be determined. In other words, we would be unable to determine whether the occurrence of a transaction at one merchant increases or decreases the likelihood of occurrence of a transaction at another merchant.

Now suppose that it is desired to predict the number of times two arbitrary merchants, merchant and merchant_j co-occur. In the absence of any additional information we would have to assume that merchant_i and merchant_j, are not correlated. In terms of probability theory, this means that the occurrence of a transaction at merchant_i will not affect the probability of the occurrence of a transaction at merchant_j:
P_j|i=P_j   [1]

The joint probability of merchant_i and merchant_j is given by
P_ij=P_iP_j|i   [2]

Substituting P_j for P_j|i into equation [2] gives
P_ij=P_iP_j|i=P_iP_j   [3]

However, the true probabilities P_i and P_j, are unknown, and so they must be estimated from the limited information given about the data. In this scenario, the maximum likelihood estimate {circumflex over (P)} for P_i and P_j is
{circumflex over (P)}_i=T_i/T   [4]
{circumflex over (P)}_j=T_j/T   [5]
where

T_i is the number of co-occurrence events that merchant, appeared in,

T_j is the number of co-occurrence events that merchant_j appeared in, and

T is the total number of co-occurrence events.

These data values are taken from the bi-directional co-occurrence matrix. Substituting these estimates into equation [3] produces

$\begin{array}{cc}{\hat{P}}_{\mathrm{ij}}={\hat{P}}_i{\hat{P}}_j=\frac{T_i{T}_j}{T^2}& \left[6\right]\end{array}$
which is the estimate for P_ij.

Since there are a total of T independent co-occurrence events in the transaction data, the expected number of co-occurring transactions of merchant_i and merchant_j is

$\begin{array}{cc}{\hat{T}}_{\mathrm{ij}}=T{\hat{P}}_{\mathrm{ij}}=\frac{T_i{T}_j}{T}& \left[7\right]\end{array}$

This expected value serves as a reference point for determining the correlation between any two merchants in the transaction data. If two merchants co-occur significantly greater than expected by {circumflex over (T)}_ij, the two merchants are positively related. Similarly, if two merchants co-occur significantly less expected, the two merchants are negatively related. Otherwise, the two merchants are practically unrelated.

Also, given the joint probability estimate {circumflex over (P)}_ij and the number of independent co-occurrence events T, the estimated probability distribution function for the number of times that merchant_i and merchant_j co-occur can be determined. It is well known, from probability theory, that an experiment having T independent trials (here transactions) and a probability of success {circumflex over (P)}_ij for each trial (success here being co-occurrence of merchant_i and merchant_j) can be modeled using the binomial distribution. The total number of successes k, which in this case represents the number of co-occurrences of merchants, has the following probability distribution:

$\begin{array}{cc}\mathrm{Pr}\left(t_{\mathrm{ij}}=k|T,T_i,T_j\right)=\frac{T!}{k!\left(T-k\right)!}·{\hat{P}}_{\mathrm{ij}}^k·{\left(1-{\hat{P}}_{\mathrm{ij}}\right)}^{T-k}& \left[8\right]\end{array}$
This distribution has mean:

$\begin{array}{cc}E\left[t_{\mathrm{ij}}\right]=T{\hat{P}}_{\mathrm{ij}}=\frac{T_i{T}_j}{T}& \left[9\right]\end{array}$
which is the same value as was previously estimated using a different approach. The distribution has variance:

$\begin{array}{cc}\mathrm{Var}\left[t_{\mathrm{ij}}\right]=T{\hat{P}}_{\mathrm{ij}}\left(1-{\hat{P}}_{\mathrm{ij}}\right)=\frac{T_i{T}_j}{T}·\left(1-\frac{T_i{T}_j}{T^2}\right)& \left[10\right]\end{array}$

The variance is used indirectly in UDL 1, below. The standard deviation of t_ij, σ_ij, is the square root of the variance Var[t_ij]. If merchant_i and merchant_j are not related, the difference between the actual and expected co-occurrence counts, T_ij−{circumflex over (T)}_ij, should not be much larger than σ_ij.

c) Desired Dot-Products Between Merchant Vectors

To calculate the desired dot product (d_ij) between two merchants' vectors, the UDL algorithm compares the number of observed co-occurrences (found in the bidirectional co-occurrence matrix) to the number of expected co-occurrences. First, it calculates a raw relationship measure (r_ij) from the co-occurrence counts, and then it calculates a desired dot product d_ij from r_ij There are at least three different ways that the relationship strength and desired dot product can be calculated from the co-occurrence data:

$\begin{array}{cc}\mathrm{Method}\text{:}\mathrm{UDL1}& \\ r_{\mathrm{ij}}=\frac{T_{\mathrm{ij}}-{\hat{T}}_{\mathrm{ij}}}{{\sigma }_{\mathrm{ij}}}\approx \frac{T_{\mathrm{ij}}-{\hat{T}}_{\mathrm{ij}}}{\sqrt{{\hat{T}}_{\mathrm{ij}}}}& \left(11\right)\\ d_{\mathrm{ij}}=\tanh \left(\frac{{\sigma }_0·r_{\mathrm{ij}}}{2{\sigma }_r}\right)& \left[12\right]\\ \mathrm{Method}\text{:}\mathrm{UDL2}& \\ r_{\mathrm{ij}}=\mathrm{sign}\left(T_{\mathrm{ij}}-{\hat{T}}_{\mathrm{ij}}\right)·\sqrt{2\ln \lambda }& \left[13\right]\\ d_{\mathrm{ij}}=\tanh \left(\frac{{\sigma }_0·r_{\mathrm{ij}}}{2{\sigma }_r}\right)& \left[12\right]\\ \mathrm{Method}\text{:}\mathrm{UDL3}& \\ r_{\mathrm{ij}}=\mathrm{sign}\left(T_{\mathrm{ij}}-{\hat{T}}_{\mathrm{ij}}\right)·\sqrt{\frac{2\mathrm{ln\lambda }}{\sqrt{T_{\mathrm{ij}}}}}=\mathrm{sign}\left(T_{\mathrm{ij}}-{\hat{T}}_{\mathrm{ij}}\right)·\sqrt{2\ln \lambda }·{\hat{T}}_{\mathrm{ij}}^{1/4}& \left[14\right]\\ d_{\mathrm{ij}}=\tanh \left(\frac{{\sigma }_0·r_{\mathrm{ij}}}{2{\sigma }_r}\right)& \left[12\right]\end{array}$
where T_ij is the actual number of co-occurrence events for merchant_i and merchant_j, and σ_r is the standard deviation of all the r_ij.

In UDL2 and UDL3, the log-likelihood ratio, Inλ is given by:

$\begin{array}{cc}\ln \lambda =T_{\mathrm{ij}}\ln \frac{T_{\mathrm{ij}}}{{\hat{T}}_{\mathrm{ij}}}+\left(T_i-T_{\mathrm{ij}}\right)\ln \frac{\left(T_i-T_{\mathrm{ij}}\right)}{\left(T_i-{\hat{T}}_{\mathrm{ij}}\right)}+\left(T_j-T_{\mathrm{ij}}\right)\ln \frac{\left(T_j-T_{\mathrm{ij}}\right)}{\left(T_j-{\hat{T}}_{\mathrm{ij}}\right)}+\left(T-T_i-T_j+T_{\mathrm{ij}}\right)\ln \frac{\left(T-T_i-T_j+T_{\mathrm{ij}}\right)}{\left(T-T_i-T_j+{\hat{T}}_{\mathrm{ij}}\right)}& \left[15\right]\end{array}$

Each technique calculates the unexpected deviation, that is, the deviation of the actual co-occurrence count from the expected co-occurrence count. In terms of the previously defined variables, the unexpected deviation is:
D_ij=T_ij−{circumflex over (T)}_ij   [16]
Thus, D_ij may be understood as a raw measure of unexpected deviation.

As each method uses the same unexpected deviation measure, the only difference between each technique is that they use different formulas to calculate r_ij from D_ij. (Note that other calculations of dot product may be used).

The first technique, UDL1, defines r_ij to be the unexpected deviation D_ij divided by the standard deviation of the predicted co-occurrence count. This formula for the relationship measure is closely related to chi-squared (χ), a significance measure commonly used by statisticians. In fact

$\begin{array}{cc}{\chi }^2=r_{\mathrm{ij}}^2=\frac{{\left(T_{\mathrm{ij}}-{\hat{T}}_{\mathrm{ij}}\right)}^2}{{\sigma }_{\mathrm{ij}}^2}& \left[17\right]\end{array}$

For small counts situations, i.e. when T_ij<<l, UDL1 gives overly large values for r_ij. For example, in a typical retail transaction data set, which has more than 90% small counts, values of r_ij on the order of 10⁹ have been seen. Data sets having such a high percentage of large relationship measures can be problematic; because in these cases, σ_r also becomes very large. Since the same σ_r is used by all co-occurrence pairs, large values of σ, causes

$\frac{r_{\mathrm{ij}}}{{\sigma }_r}$
to become very small for pairs that do not suffer from small counts. Therefore in these cases d_ij becomes

$\begin{array}{cc}{d}_{\mathrm{ij}}=\tanh \left(\frac{{\sigma }_0·r_{\mathrm{ij}}}{2{\sigma }_r}\right)\approx 0& \left[18\right]\end{array}$

This property is not desirable, because it forces the merchant vectors of two merchants too be orthogonal, even when the two merchants co-occur significantly greater than expected.

The second technique, UDL2, overcomes of the small count problem by using log-likelihood ratio estimates to calculate r_ij. It has been shown that log-likelihood ratios have much better small count behavior than χ², while at the same time retaining the same behavior as χ² in the non-small count regions.

The third technique, UDL3, is a slightly modified version of UDL2. The only difference is that the log likelihood ratio estimate is scaled by 1/. This scaling removes the  bias from the log likelihood ratio estimate. The preferred embodiment uses UDL2 in most cases.

Accordingly, the present invention generally proceeds as follows:

• • 1. For each pair of root merchant names, determine the expected number of co-occurrences of the pair from total number of co-occurrence transactions involving each merchant name (with any merchant) and the total number of co-occurrence transactions.
  • 2. For each pair of root merchant names, determine a relationship strength measure based on the difference between the expected number of co-occurrences and the actual number of co-occurrences.
  • 3. For each pair of root merchant names, determine a desired dot product between the merchant vectors from the relationship strength measure.

d) Merchant Vector Training

The goal of vector training is to position the merchant vectors in a high-dimensional vector space such that the dot products between them closely approximates their desired dot products. (In a preferred embodiment, the vector space has 280 dimensions, though more or less could be used). Stated more formally:

Given a set of merchant vectors V={V₁, V₂, . . . , V_N}, and the set of desired dot products for each pair of vectors D={d₁₂, d₁₃, . . . , d_1N,d₂₁,d₂₃, . . . , d_2N, d_3,1 . . . , d_N(N−1)}, position each merchant vector such that a cost function is minimized, e.g.:

$\begin{array}{cc}E=\frac{1}{2}\sum _{i=1,i\ne j}^N\sum _{j=i}^N{\left(d_{\mathrm{ij}}-{\stackrel{\rightharpoonup }{V}}_i·{\stackrel{\rightharpoonup }{V}}_j\right)}^2& \left[19\right]\end{array}$

In a typical master file 408 of typical transaction data, the set of merchants vectors contains tens thousand or more vectors. This means that if it desired to find the optimal solution, then there must be solved a system of ten thousand or more high-dimensional linear equations. This calculation is normally prohibitive given the types of time frames in which the information is desired. Therefore, alternative techniques for minimizing the cost function are preferred.

One such approach is based on gradient descent. In this technique, the desired dot product is compared to the actual dot product for each pair of merchant vectors. If the dot product between a pair of vectors is less than desired, the two vectors are moved closer together. If the dot product between a pair of vectors is greater than desired, the two vectors are moved farther apart. Written in terms of vector equations, this update rule is:

$\begin{array}{cc}{\stackrel{\rightharpoonup }{V}}_i\left(n+1\right)={\stackrel{\rightharpoonup }{V}}_i\left(n\right)+\alpha \left(d_{\mathrm{ij}}-{\stackrel{\rightharpoonup }{V}}_i·{\stackrel{\rightharpoonup }{V}}_j\right){\stackrel{\rightharpoonup }{V}}_j& \left[20\right]\\ {\stackrel{\rightharpoonup }{V}}_i\left(n+1\right)=\frac{{\stackrel{\rightharpoonup }{V}}_i\left(n+1\right)}{V_i\left(n+1\right)}& \left[21\right]\\ {\stackrel{\rightharpoonup }{V}}_j\left(n+1\right)={\stackrel{\rightharpoonup }{V}}_j\left(n\right)+\alpha \left(d_{\mathrm{ij}}-{\stackrel{\rightharpoonup }{V}}_i·{\stackrel{\rightharpoonup }{V}}_j\right){\stackrel{\rightharpoonup }{V}}_i& \left[22\right]\\ {\stackrel{\rightharpoonup }{V}}_j\left(n+1\right)=\frac{V_j\left(n+1\right)}{V_j\left(n+1\right)}& \left[23\right]\end{array}$

This technique converges as long as the learning rate (χ) is sufficiently small (and determined by analysis of the particular transaction data being used; typically in the range 0.1-0.5), however the convergence may be very slow.

An alternative methodology uses averages of merchant vectors. In this embodiment, the desired position of a current merchant vector is determined with respect to each other merchant vector given the current position of the other merchant vector, and the desired dot product between the current and other merchant vector. An error-weighted average of these desired positions is then calculated, and taken as the final position of the current merchant vector. Written in terms of vector equations, the update rule is:

$\begin{array}{cc}{\stackrel{\rightharpoonup }{V}}_i\left(n+1\right)=\left(1-\gamma \right){\stackrel{\rightharpoonup }{V}}_i+\gamma \sum _{j=1}^N{\stackrel{\rightharpoonup }{U}}_{\mathrm{ij}}\left(1-{\stackrel{\rightharpoonup }{V}}_i·{\stackrel{\rightharpoonup }{U}}_{\mathrm{ij}}\right)& \left[24\right]\end{array}$

where V_i (n+1) is the updated position of the current merchant vector V_i, and U_ij is the desired position of current merchant vector V_i with respect to each other merchant vector V_j, U_ij may be calculated using formula:

$\begin{array}{cc}{\stackrel{\rightharpoonup }{U}}_{\mathrm{ij}}={\left(\frac{1-d_{\mathrm{ij}}^2}{1-{\varepsilon }_{\mathrm{ij}}^2}\right)}^{1/2}{\stackrel{\rightharpoonup }{V}}_i+\left[d_{\mathrm{ij}}-{\left(\frac{1-d_{\mathrm{ij}}^2}{1-{\varepsilon }_{\mathrm{ij}}^2}\right)}^{1/2}{\varepsilon }_{\mathrm{ij}}\right]{\stackrel{\rightharpoonup }{V}}_j& \left[25\right]\end{array}$

where d_ij is the desired dot product between V_i and V_j, and ε_ij is the current dot product between V_i and V_j.

Since Ū_ij is a linear combination of merchant vectors V_i and V_j, it will always be in the plane of these vectors V_i and V_j

The result of any of these various approaches is a final set of merchant vectors for all merchant names.

Appendix II, below, provides a geometrically derived algorithm for the error weighted update process. Appendix III provides an algebraically derived algorithm of this process, which results in an efficient code implementation, and which produces the same results as the algorithm of Appendix II.

Those of skill in the art will appreciate that the UDL algorithm, including its variants above, and the implementations in the appendices, may be used in contexts outside of determining merchant co-occurrences. This aspect of the present invention may be for vector representation and co-occurrence analysis in any application domain, for example, where there is need for representing high frequency data items without exclusion. Thus, the ULD algorithm may be used in information retrieval, document routing, and other fields of information analysis.

3. Clustering Module

Following generation and training of the merchant vectors, the clustering module 520 is used to cluster the resulting merchant vectors and identify the merchant segments. Various different clustering algorithms may be used, including k-means clustering (MacQueen). The output of the clustering is a set of merchant segment vectors, each being the centroid of a merchant segment, and a list of merchant vectors (thus merchants) included in the merchant segment.

There are two different clustering approaches that may be usefully employed to generate the merchant segments. First, clustering may be done on the merchant vectors themselves. This approach looks for merchants having merchant vectors which are substantially aligned in the vector space, and clusters these merchants into segments and computes a cluster vector for each segment. Thus, merchants for whom transactions frequently co-occur and have high dot products between their merchant vectors will tend to form merchant segments. Note that it is not necessary for all merchants in a cluster to all co-occur in many consumers' transactions. Instead, co-occurrence is associative: if merchants A and B co-occur frequently, and merchants B and C co-occur frequently, A and C are likely to be in the same merchant segment.

A second clustering approach is to use the consumer vectors. For each account identifier, a consumer vector is generated as the summation of the vectors of the merchants at which the consumer has purchased in a defined time interval, such as the previous three months. A simple embodiment of this is:

$\begin{array}{cc}C=\sum _{i=1}^N{V}_i& \left[26\right]\end{array}$
where C is the consumer vector for an account, N is the number of unique root merchant names in the customer account's transaction data within a selected time period, and V_i is the merchant vector for the i^th unique root merchant name. The consumer vector is then normalized to unit length.

A more interesting consumer vector takes into account various weighting factors to weight the significance of each merchant's vector:

$\begin{array}{cc}C=\sum _{i=1}^N{W}_i{V}_i& \left[27\right]\end{array}$
where W_i is a weight applied to the merchant vector V_i. For example, a merchant vector may be weighted by the total (or average) purchase amount by the consumer at the merchant in the time period, by the time since the last purchase, by the total number of purchases in the time period, or by other factors.

However computed, the consumer vectors can then be clustered, so that similar consumers, based on their purchasing behavior, form a merchant segment. This defines a merchant segment vector. The merchant vectors that are closest to a particular merchant segment vector are deemed to be included in the merchant segment.

With the merchant segments and their segment vectors, the predictive models for each segment may be developed. Before discussing the creation of the predictive models, a description of the training data used in this process is described.

F. Data Postprocessing Module

Following identification of merchant segments, a predictive model of consumer spending in each segment is generated from past transactions of consumers in the merchant segment. Using the past transactions of consumer in the merchant segment provides a robust base on which to predict future spending, and since the merchant segments were identified on the basis of the actual spending patterns of the consumers, the arbitrariness of conventional demographic based predictions are minimized. Additional non-segment specific transactions of the consumer may also be used to provide a base of transaction behavior.

To create the segment models, the consumer transaction data is organized into groups of observations. Each observation is associated with a selected end-date. The end-date divides the observation into a prediction window and an input window. The input window includes a set of transactions in a defined past time interval prior to the selected end-date (e.g. 6 months prior). The prediction window includes a set of transactions in a defined time interval after the selected end-date (e.g. the next 3 months). The prediction window transactions are the source of the dependent variables for the prediction, and the input window transactions are the source of the independent variables for the prediction.

More particularly, the input for the observation generation module 530 is the master files 408. The output is a set of observations for each account. Each account receives three types of observations. FIG. 8 illustrates the observation types.

The first type of observations is training observations, which are used to train the predictive model that predicts future spending within particular merchant segments. If N is the length (in months) of the window over which observation inputs are computed then there are 2N−1 training observations for each segment.

In FIG. 8, there are shown a 16 months of transaction data, from March of one year, to June of the next. Training observations are selected prior to the date of interest, November 1. The input window includes the 4 months of past data to predict the next 2 months in the prediction window. The first input window 802a thus uses a selected date of July 1, includes March-June to encompass the past transactions; transactions in July-August form the prediction window 803a. The next input window 802b uses August 1 as the selected date, with transactions in April-July as the past transactions, August-September as prediction window 803b. The last input window for this set is 802e, which uses November 1 as its selected date, with a prediction window 803e of observations in November-December.

The second type of observations is blind observations. Blind observations are observations where the prediction window does not overlap any of the time frames for the prediction windows in the training observations. Blind observations are used to evaluate segment model performance. In FIG. 8, the blind observations 804 include those from September to February, as illustrated.

The third observation type is action observations, which are used in a production phase. Action observations have only inputs (past transactions given a selected date) and no target transactions after the selected date. These are preferably constructed with an input window that spans the final months of available data. These transactions are the ones on which the actual predictions are to be made. Thus, they should be the transactions in an input window that extends from a recent selected date (e.g. most recent end of month), back the length of the input window used during training. In FIG. 8, the action observations 806 span November 1 to end of February, with the period of actual prediction being from March to end of May.

FIG. 8 also illustrates that at some point during the prediction window, the financial institution sends out promotions 808 to selected consumers based on their predicted spending in the various merchant segments.

Referring to FIG. 4b again, the DPPM takes the master files 408, and a given selected end-date, and constructs for each consumer, and then for each segment, a set of training observations and blind observations from the consumer's transactions, including transactions in the segment, and any other transactions. Thus, if there are 300 segments, for each consumer there will be 300 sets of observations. If the DPPM is being used during production for prediction purposes, then the set of observations is a set of action observations.

For training purposes, the DPPM computes transactions statistics from the consumer's transactions. The transaction statistics serve as independent variables in the input window, and as dependent variables from transactions in the prediction window. In a preferred embodiment, these variables are as follows:

Prediction window: The dependent variables are generally any measure of amount or rate of spending by the consumer in the segment in the prediction window. A simple measure is the total dollar amount that was spent in the segment by the consumer in the transactions in the prediction window. Another measure may be average amount spent at merchants (e.g. total amount divided by number of transactions).

Input window: The independent variables are various measures of spending in the input window leading up to the end date (though some may be outside of it). Generally, the transaction statistics for a consumer can be extracted from various grouping of merchants. These groups may be defined as: 1) merchants in all segments; 2) merchants in the merchant segment being modeled; 3) merchants whose merchant vector is closest the segment vector for the segment being modeled (these merchants may or may not be in the segment); and 4) merchants whose merchant vector is closest to the consumer vector of the consumer. One preferred set of input variables includes:

• • (1) Recency. The amount of time in months between the current end date and the most recent transaction of the consumer in any segment. Recency may be computed over all available time and is not restricted to the input window.
  • (2) Frequency. The number of transactions by a consumer in the input window preceding the end-date for all segments.
  • (3) Monetary value of purchases. A measure of the amount of dollars spent by a customer in the input window preceding the end-date for all segments. The total or average, or other measures may be used.
  • (4) Recency_segment. The amount of time in months between the current end date and the most recent transaction of the consumer in the segment. Recency may be computed over all available time and is not restricted to the input window.
  • (5) Frequency_segment. The number of transactions in the segment by a customer in the input window preceding the current end date.
  • (6) Monetary_segment. The amount of dollars spent in the segment by a customer in the input window preceding the current end date.
  • (7) Recency nearest profile merchants. The amount of time in months between the current end date and the most recent transaction of the consumer in a collection of merchants that are nearest the consumer vector of the consumer. Recency may be computed over all available time and is not restricted to the input window.
  • (8) Frequency nearest profile merchants. The number of transactions in a collection of merchants that are nearest the consumer vector of the consumer by the consumer in the input window preceding the current end date.
  • (9) Monetary nearest frequency merchants. The amount of dollars spent in a collection of merchants that are nearest the consumer vector of the consumer by the consumer in the input window preceding the current end date.
  • (10) Recency nearest segment merchants. The amount of time in months between the current end date and the most recent transaction of the consumer in a collection of merchants that are nearest the segment vector. Recency may be computed over all available time and is not restricted to the input window.
  • (11) Frequency nearest segment merchants. The number of transactions in a collection of merchants that are nearest the segment vector by the consumer in the input window preceding the current end date.
  • (12) Monetary nearest segment merchants. The amount of dollars spent in a collection of merchants that are nearest the segment vector by the consumer in the input window preceding the current end date.
  • (13) Segment probability score. The probability that a consumer will spend in the segment in the prediction window given all merchant transactions for the consumer in the input window preceding the end date. A preferred algorithm estimates combined probability using a recursive Bayesian method.
  • (14) Seasonality variables. It is assumed that the fundamental period of the cyclic component is known. In the case of seasonality, it can be assumed that the cycle of twelve months. Two variables are added to the model related to seasonality. The first variable codes the sine of the date and the second variable codes the cosine of the date. The calculation for these variables are:
    Sin Input=sin(2.0*PI*(sample day of year)/365)
    Cos Input=cos(2.0*PI*(sample month of year)/365).
  • (15) (Segment Vector-Consumer Vector Closeness: As an optional input, the dot product of the segment vector for the segment and the consumer vector is used as an input variable.

In addition to these transaction statistics, variables may be defined for the frequency of purchase and monetary value for all cases of segment merchants, nearest profile merchants, nearest segment merchants for the same forward prediction window in the previous year(s).

G. Predictive Model Generation

The training observations for each segment are input into the segment predictive model generation module 530 to generate a predictive model for the segment. FIG. 9 illustrates the overall logic of the predictive model generation process. The master files 408 are organized by accounts, based on account identifiers, here illustratively, accounts 1 through N. There are M segments, indicated by segments 1 through M. The DPPM generates for each combination of account and merchant segment, a set of input and blind observations. The respective observations for each merchant segment M from the many accounts 1 . . . N are input into the respective segment predictive model M during training. Once trained, each segment predictive model is tested with the corresponding blind observations. Testing may be done by comparing for each segment a lift chart generated by the training observations with the lift chart generated from blind observations. Lift charts are further explained below.

The predictive model generation module 530 is preferably a neural network, using a conventional multi-layer organization, and backpropagation training. In a preferred embodiment, the predictive model generation module 530 is provided by HNC Software's Database Mining Workstation, available from HNC Software of San Diego, Calif.

While the preferred embodiment uses neural networks for the predictive models, other types of predictive models may be used. For example, linear regression models may be used.

As discussed above, the predictive model may also be trained to predict spending at vendors, responses to particular offers or other marketing schemes, and the like, that are not associated with a particular market segment. Referring now to FIG. 13, training set 1301 contains data describing customers who have previously been presented with the offer, including customers who accepted the offer (positive exemplars) and customers who rejected the offer (negative exemplars). Vector values in the appropriate merchant vector space are also provided. Based on the data in training set 1301, predictive model 1303 is trained using known techniques, such as those of predictive model generation module 530 as referenced above.

Once a trained model 1303 is available, predicted response 1304 for a customer can be generated based on vector values 1302 for the customer in a number of merchant segments. The particular response 1304 being predicted need not be associated with any particular market segment in order for an effective prediction to be generated. In this manner, the present invention is able to provide meaningful predictions even in industries or marketing environments where market segments are not available or are inapplicable.

For example, suppose a prediction is to be generated for a particular consumer's response to an offer for a home equity line of credit. Training set 1301 would include some aggregation of data that describes the responses to the same (or similar) offer of a number of consumers. Vector values for those consumers in a number of market segments, along with the responses to the offer, would be used to train predictive model 1303. Then, given the particular consumer's vector values for a number of market segments 1302, model 1303 is able to predict the consumer's response 1304 to the offer for the line of credit, even though no market segment has been established for the offer.

H. Profiling Engine

The profiling engine 412 provides analytical data in the form of an account profile about each customer whose data is processed by the system 400. The profiling engine is also responsible for updating consumer profiles over time as new transaction data for consumers is received. The account profiles are objects that can be stored in a database 414 and are used as input to the computational components of system 400 in order to predict future spending by the customer in the merchant segments. The profile database 414 is preferably ODBC compliant, thereby allowing the accounts provider (e.g. financial institution) to import the data to perform SQL queries on the customer profiles.

The account profile preferably includes a consumer vector, a membership vector describing a membership value for the consumer for each merchant segment, such as the consumer's predicted spending in each segment in a predetermined future time interval, and the recency, frequency, and monetary variables as previously described for predictive model training.

The profiling engine 412 creates the account profiles as follows.

1. Membership Function: Predicted Spending in Each Segment

The profile of each account holder includes a membership value with respect to each segment. The membership value is computed by a membership function. The purpose of the membership function is to identify the segments with which the consumer is mostly closely associated, that is, which best represent the group or groups of merchants at which the consumer has shopped, and is likely to shop at in the future.

In a preferred embodiment, the membership function computes the membership value for each segment as the predicted dollar amount that the account holder will purchase in the segment given previous purchase history. The dollar amount is projected for a predicted time interval (e.g. 3 months forward) based on a predetermined past time interval (e.g. 6 months of historical transactions). These two time intervals correspond to the time intervals of the input window and prediction windows used during training of the merchant segment predictive models. Thus, if there are 300 merchant segments, then a membership value set is a list of 300 predicted dollar amounts, corresponding to the respective merchant segments. Sorting the list by the membership value identifies the merchant segments at which the consumer is predicted to spend the greatest amounts of money in the future time interval, given their spending historically.

To obtain the predicted spending, certain data about each account is input in each of the segment predictive models. The input variables are constructed for the profile consistent with the membership function of the profile. Preferably, the input variables are the same as those used during model training, as set forth above. An additional input variable for the membership function may include the dot product between the consumer vector and the segment vector for the segment (if the models are so trained). The output of the segment models is a predicted dollar amount that the consumer will spend in each segment in the prediction time interval.

2. Segment Membership Based on Consumer Vectors

A second alternate, membership aspect of the account profiles is membership based upon the consumer vector for each account profile. The consumer vector is a summary vector of the merchants that the account has shopped at, as explained above with respect to the discussion of clustering. In this aspect, the dot product of the consumer vector and segment vector for the segment defines a membership value. In this embodiment, the membership value list is a set of 300 dot products, and the consumer is member of the merchant segment(s) having the highest dot product(s).

With either one of these membership functions, the population of accounts that are members of each segment (based on the accounts having the highest membership values for each segment) can be determined. From this population, various summary statistics about the accounts can be generated such as cash advances, purchases, debits, and the like. This information is further described below.

3. Updating of Consumer Profiles

As additional transactions of a consumer are received periodically (e.g. each month) the merchant vectors associated with the merchants in the new transactions can be used to update the consumer vector, preferably using averaging techniques, such as exponential averaging over the desired time interval for the update.

Updates to the consumer vector are preferably a function of dollars spent perhaps relative to the mean of the dollars spent at the merchant. Thus, merchant vectors are weighted in the new transaction period by both the time and the significance of transactions for the merchant by the consumer (e.g. weighted by dollar amount of transactions by consumer at merchant). One formula for weighting merchants is:
W_i=S_ie^λi   [28]

where

W_i is the weight to be applied to merchant i's merchant vector;

S_i is the dollar amount of transactions at merchant i in the update time interval;

t is the amount of time since the last transaction at merchant i; and

λ is a constant that controls the overall influence of the merchant.

The profiling engine 412 also stores a flag for each consumer vector indicating the time of the last update.

l. Reporting Engine

The reporting engine 426 provides various types of segment and account specific reports. The reports are generated by querying the profiling engine 412 and the account database for the segments and associated accounts, and tabulating various statistics on the segments and accounts.

1. Basic Reporting Functionality

The reporting engine 426 provides functionality to:

a) Search by merchant names, including raw merchant names, root names, or equivalence names.

b) Sort merchant lists by merchant name, frequency of transactions, transaction amounts and volumes, number of transactions at merchant, or SIC code.

c) Filter contents of report by number of transactions at merchant.

The reporting engine 426 provides the following types of reports, responsive to these input criteria:

2. General Segment Report

For each merchant segment a very detailed and powerful analysis of the segment can be created in a segment report. This information includes:

a) General Segment Information

Merchant Cohesion: A measure of how closely clustered are the merchant vectors in this segment. This is the average of the dot products of the merchant vectors with the centroid vector of this segment. Higher numbers indicate tighter clustering.

Number of Transactions: The number of purchase transactions at merchants in this segment, relative to the total number of purchase transactions in all segments, providing a measure of how significant the segment is in transaction volume.

Dollars Spent: The total dollar amount spent at merchants in this segment, relative to the total dollar amount spent in all segments, providing a measure of dollar volume for the segment.

Most Closely Related Segments: A list of other segments that are closest to the current segment. This list may be ranked by the dot products of the segment vectors, or by a measure of the conditional probability of purchase in the other segment given a purchase in the current segment.

The conditional probability measure M is as follows: P(A|B) is probability of purchase in segment A segment in next time interval (e.g. 3 months) given purchases in segment B in the previous time interval (e.g. 6 months). P(A|B)/P(A)=M. If M is >1, then a purchase in segment B is positively influencing the probability of purchase in segment A, and if M<1 then a purchase in segment B negatively influences a purchase in segment A. This is because if there is no information about the probability of purchases in segment B, then P(A|B)=P(A), so M=1. The values for P(A|B) are determined from the co-occurrences of purchases at merchants in the two segments, and P(A) is determined and from the relative frequency of purchases in segment A compared to all segments.

A farthest segments list may also be provided (e.g. with the lowest conditional probability measures).

b) Segment Members Information

Detailed information is provided about each merchant that is a member of a segment. This information comprises:

Merchant Name and SIC code;

Dollar Bandwith: The fraction of all the money spent in this segment that is spent at this merchant (percent);

Number of transactions: The number of purchase transactions at this merchant;

Average Transaction Amount: The average value of a purchase transaction at this merchant;

Merchant Score: The dot product of this merchant's vector with the centroid vector of the merchant segment. (A value of 1.0 indicates that the merchant vector is at the centroid);

SIC Description: The SIC code and its description;

This information may be sorted along any of the above dimensions.

c) Lift Chart

A lift chart useful for validating the performance of the predictive models by comparing predicted spending in a predicted time window with actual spending.

Table 10 illustrates a sample lift chart for merchant segment:

TABLE 10
A sample segment lift chart.
		Cumulative	Cumulative	Cumulative
	Bin	segment lift	segment lift in	Population
	1	5.56	$109.05	50,000
	2	4.82	$94.42	100,000
	3	3.82	$74.92	150,000
	4	3.23	$63.38	200,000
	5	2.77	$54.22	250,000
	6	2.43	$47.68	300,000
	7	2.20	$43.20	350,000
	8	2.04	$39.98	400,000
	9	1.88	$36.79	450,000
	10	1.75	$34.35	500,000
	11	1.63	$31.94	550,000
	12	1.52	$29.75	600,000
	13	1.43	$28.02	650,000
	14	1.35	$26.54	700,000
	15	1.28	$25.08	750,000
	16	1.21	$23.81	800,000
	17	1.16	$22.65	850,000
	18	1.10	$21.56	900,000
	19	1.05	$20.57	950,000
	20	1.00	$19.60	1,000,000
	Base-line	—	$19.60

Lift charts are created generally as follows:

As before, there is defined input window and prediction window, for example 6 and 3 months respectively. Data from the total length of these windows relative to end of the most recent spending data available is taken. For example, if data on actual spending in the accounts is available through the end of the current month, then the prior three months of actual data will be used as the prediction window, and the data for the six months prior to that will be data for input window. The input data is then used to “predict” spending in the three-month prediction window, for which in fact there is actual spending data. The predicted spending amounts are now compared with the actual amounts to validate the predictive models.

For each merchant segment then, the consumer accounts are ranked by their predicted spending for the segment in the prediction window period. Once the accounts are ranked, they are divided into N (e.g. 20) equal sized bins so that bin 1 has the highest spending accounts, and bin N has the lowest ranking accounts. This identifies the accounts holders that the predictive model for the segment indicated should be are expected to spend the most in this segment.

Then, for each bin, the average actual spending per account in this segment in the past time period, and the average predicted spending is computed. The average actual spending over all bins is also computed. This average actual spending for all accounts is the baseline spending value (in dollars), as illustrated in the last line of Table 10. This number describes the average that all account holders spent in the segment in the prediction window period.

The lift for a bin is the average actual spending by accounts in the bin divided by the baseline spending value. If the predictive model for the segment is accurate, then those accounts in the highest ranked bins should have a lift greater than 1, and the lift should generally be increasing, with bin 1 having the highest lift. Where this the case, as for example, in Table 10, in bin 1, this shows that those accounts in bin 1 in fact spent several times the baseline, thereby confirming the prediction that these accounts would in fact spend more than others in this segment.

The cumulative lift for a bin is computed by taking the average spending by accounts in that bin and all higher ranking bins, and dividing it by the baseline spending (i.e. the cumulative lift for bin 3 is the average spending per account in bins 1 through 3, divided by the baseline spending.) The cumulative lift for bin N is always 1.0. The cumulative lift is useful to identify a group of accounts that are to be targeted for promotional offers.

The lift information allows the financial institution to very selectively target a specific group of accounts (e.g. the accounts in bin 1) with promotional offers related to the merchants in the segment. This level of detailed, predictive analysis of very discrete groups of specific accounts relative to merchant segments is not believed to be currently available by conventional methods.

d) Population Statistics Tables

The reporting engine 426 further provides two types of analyses of the financial behavior of a population of accounts that are associated with a segment based on various selection criteria. The Segment Predominant Scores Account Statistics table and the Segment Top 5% Scores Account Statistics table present averaged account statistics for two different types of populations of customers who shop, or are likely to shop, in a given segment. The two populations are determined as follows.

Segment Predominant Scores Account Statistics Table: All open accounts with at least one purchase transaction are scored (predicted spending) for all of the segments. Within each segment, the accounts are ranked by score, and assigned a percentile ranking. The result is that for each account there is a percentile ranking value for each of the merchant segments.

The population of interest for a given segment is defined as those accounts that have their highest percentile ranking in this segment. For example, if an account has its highest percentile ranking in segment #108, that account will be included in the population for the statistics table for segment #108, but not in any other segment. This approach assigns each account holder to one and only one segment.

Segment Top 5% Scores Account Statistics. For the Segment Top 5% Scores Account Statistics table, the population is defined as the accounts with percentile ranking of 95% or greater in a current segment. These are the 5% of the population that is predicted to spend the most in the segment in the predicted future time interval following the input data time window. These accounts may appear in this population in more than one segment, so that high spenders will show up in many segments; concomitantly, those who spend very little may not assigned to any segment.

The number of accounts in the population for each table is also determined and can be provided as a raw number, and as a percentage of all open accounts (as shown in the titles of the following two tables).

Table 11 and Table 12 provide samples of these two types of tables:

TABLE 11
Segment Predominant Scores Account Statistics: 8291 accounts
(0.17 percent)
	Mean	Std	Population	Relative
Category	Value	Deviation	Mean	Score
Cash Advances	$11.28	$53.18	$6.65	169.67
Cash Advance Rate	0.03	0.16	0.02	159.92
Purchases	$166.86	$318.86	$192.91	86.50
Purchase Rate	0.74	1.29	1.81	40.62
Debits	$178.14	$324.57	$199.55	89.27
Debit Rate	0.77	1.31	1.84	41.99
Dollars in Segment	4.63%	14.34	10.63%	43.53
Rate in Segment	3.32%	9.64	11.89%	27.95

TABLE 12
Segment Top 5% Scores Account Statistics: 154786 accounts
(3.10 percent)
	Mean	Std	Population	Relative
Category	Value	Deviation	Mean	Score
Cash Advances	$9.73	$51.21	$7.27	133.79
Cash Advance Rate	0.02	0.13	0.02	125.62
Purchases	$391.54	$693.00	$642.06	60.98
Purchase Rate	2.76	4.11	7.51	36.77
Debits	$401.27	$702.25	$649.34	61.80
Debit Rate	2.79	4.12	7.53	37.00
Dollars in Segment	1.24%	8.14	1.55%	80.03
Rate in Segment	0.99%	6.70	1.79%	55.04

i) Segment Statistics

The tables present the following statistics for each of several categories, one category per row. The statistics are:

Mean Value: the average over the population being scored;

Std Deviation: the standard deviation over the population being scored;

Population Mean: the average, over all the segments, of the Mean Value (this column is thus the same for all segments, and are included for ease of comparison); and

Relative Score: the Mean Value, as a fraction of the Population Mean (in percent).

ii) Row Descriptions

Each table contains rows for spending and rate in Cash Advances, Purchases, Debits, and Total Spending.

• The rows for spending (Cash Advances, Purchases, and Debits) show statistics on dollars per month for all accounts in the population over the time period of available data.
• The rate rows (Cash Advance Rate, Debit Rate, and Purchase Rate) show statistics on the number of transactions per month for all accounts in the population over the time period of available data.
• Debits consist of Cash Advances and Purchases.
• The Dollars in Segment shows the fraction of total spending that is spent in this segment. This informs the financial institution of how significant overall this segment is.
• The Rate in Segment shows the fraction of total purchase transactions that occur in this segment.

The differences between these two populations are subtle but important, and are illustrated by the above tables. The segment predominant population identifies those individuals as members of a segment who, relative to their own spending, are predicted to spend the most in the segment. For example, assume a consumer whose predicted spending in a segment is $20.00, which gives the consumer a percentile ranking of 75^th percentile. If the consumer's percentile ranking in every other segment is below the 75^th percentile, then the consumer is selected in this population for this segment. Thus, this may be considered an intra-account membership function.

The Top 5% scores population instead includes those accounts holders predicted to spend the most in the segment, relative to all other account holders. Thus, the account holder who was predicted to spend only $20.00 in the merchant segment will not be member of this population since he is well below the 95^th percentile, which may be predicted to spend, for example $100.00.

In the example tables these differences are pronounced. In Table 11, the average purchases of the segment predominant population is only $166.86. In Table 12, the average purchase by top 5% population is more than twice that, at $391.54. This information allows the financial institution to accurately identify accounts that are most likely to spend in a given segment, and target these accounts with promotional offers for merchants in the segment.

The above tables may also be constructed based on other functions to identify accounts associated with segments, including dot products between consumer vectors- and segment vectors.

J. Targeting Engine

The targeting engine 422 allows the financial institution to specify targeted populations for each (or any) merchant segment, to enable selection of the targeted population for receiving predetermined promotional offers.

A financial institution can specify a targeted population for a segment by specifying a population count for the segment, for example, the top 1000 accounts holders, or the top 10% account holders in a segment. The selection is made by any of the membership functions, including dot product, or predicted spending. Other targeting specifications may be used in conjunction with these criteria, such as a minimum spending amount in the segment, such as $100. The parameters for selecting the targeting population are defined in a target specification document 424, which is an input to the targeting engine 422. One or more promotions can be specifically associated with certain merchants in a segment, such as the merchants with the highest correlation with the segment vector, highest average transaction amount, or other selective criteria. In addition, the amounts offered in the promotions can be specific to each consumer selected, and based on their predicted or historical spending in the segment. The amounts may also be dependent on the specific merchant for whom a promotion is offered, as a function of the merchant's contributions to purchases in the segment, such as based upon their dollar bandwidth, average transaction amount, or the like.

The selected accounts can be used to generate a targeted segmentation report 430 by providing the account identifiers for the selected accounts to the reporting engine 426, which constructs the appropriate targeting report on the segment. This report has the same format as the general segment report but is compiled for the selected population.

An example targeting specification 424 is shown below:

TABLE 13
Target population specification
ID associated		Customer
with	Segment	target	Selection
promotional offer	ID	count	Criteria	Filter Criteria
1	122	75,000	Predicted	Average
			Spending	Transaction in
			in Segment	Segment >$50
1	143	Top 10%	Dot	Total Spending
			Product	in Segment
				>$100
2	12 and 55	87,000	Predicted	None
			Spending
			in this
			Segment
			12 and 55

Table 13 shows a specification of a total of at least 228,000 customer accounts distributed over four segments and two promotional offers (ID 1 and ID 2). For each segment or promotional offer, there are different selection and filtering criteria. For promotion #1 the top 75,000 consumers in segment #122 based on predicted spending, and who have an average transaction in the segment greater than $50, are selected. For this promotion in segment #413, the top 10% of accounts based on the dot product between the consumer vector and segment vector are selected, so long as they have a minimum spending in the segment of $100. Finally, for promotion #2, 87,000 consumers are selected across two segments. Within each offer (e.g. offer ID 1) the segment models may be merged to produce a single lift chart, which reflects the offer as a composition of the segments.

The targeting engine 422 then provides the following additional functionality:

1. Select fields from the account profile of the selected accounts that will be inserted to the mail file 434. For example, the name, address, and other information about the account may be extracted.

2. The mail file 434 is then exported to a useful word processing or bulk mailing system.

3. Instruct the reporting engine 426 to generate reports that have summary and cumulative frequencies for select account fields, such as including purchase, debit, cash advance, or any other account data.

4. Instruct the reporting engine 426 to generate lift charts for the targeting population in the segment, and for overlapped (combined) segments.

K. Segment Transition Detection

As is now apparent, the system of the present invention provides detailed insight into which merchant segments a consumer is associated with based on various measures of membership, such as dot product, predicted spending, and the like. Further, since the consumers continue to spend over time, the consumer accounts and the consumers' associations with segments are expected to change over time as their individual spending habits change.

The present invention allows for detection of the changes in consumer spending via the segment transition detection engine 420. In a given data period (e.g. next monthly cycle or multiple month collection of data) a set of membership values for each consumer is defined as variously described above, with respect to each segment. Again, this may be predicted spending by the consumer in each segment, dot product between the consumer vector and each segment vectors, or other membership functions.

In a subsequent time interval, using additional spending and/or predicted data, the membership values are recomputed. Each consumer will have the top P and the bottom Q increases in and decreases in segment membership. That is, there will be two changes of interest: the P (e.g. 5) segments with the greatest increase in membership values for the consumer; the Q segments with the greatest decrease in segment membership.

An increase in the membership value for a segment indicates that the consumer is now spending (or predicted to spend) more money in a particular segment. Decreases show a decline in the consumer's interest in the segment. Either of these movements may reflect a change in the consumer's lifestyle, income, or other demographic factors.

Significant increases in merchant segments that previously had low membership values are particularly useful to target promotional offers to the account holders who are moving into the segment. This is because the significant increase in membership indicates that the consumer is most likely to be currently receptive to the promotional offers for merchants in the segment, since they are predicted to be purchasing more heavily in the segment.

Thus, the segment transition detection engine 420 calculates the changes in each consumer's membership values between two selected time periods, typically using data in a most recent prediction window (either ending or beginning with a current statement date) relative to memberships in prior time intervals. The financial institution can define a threshold change value for selecting accounts with changes in membership more significant than the threshold. The selected accounts may then be provided to the reporting engine 426 for generation of various reports, including a segment transition report 432, which is like the general segment report except that it applies to accounts that are considered to have transitioned to or from a segment. This further enables the financial institution to selectively target these customers with promotional offers for merchants in the segments in which the consumer had the most significant positive increases in membership.

L. Nearest-Neighbor Techniques

In another embodiment, the present invention employs nearest-neighbor techniques to predict responses to offers or to determine other marketing-related value of consumers. Referring now to FIG. 14, there is shown a flowchart depicting a nearest-neighbor technique for predicting a response rate for a consumer with regard to a particular offer. One skilled in the art will recognize that the technique shown in FIG. 14 can be applied to predicting other types of market value associated with identifiable consumers.

Once consumer vectors have been developed as discussed above, a reference set of consumers is selected 1401, the consumers in the reference set having known response rates to offers (or having other characteristics that are known to be related to or good predictors of response rates). The reference set typically includes consumers who have been presented with an offer and given a chance to respond. If offer history data for consumers is not available, the reference set may include consumers for whom other relevant data is available. Preferably, the available data provides some degree of predictive accuracy concerning the likelihood of a positive response to an offer. For example, if response history data is unavailable, the system might instead use a sample of consumers, and consider those who have purchased the product to have accepted the offer, and those who have not purchased the product to have rejected the offer.

Once the set of reference consumers has been established, the system obtains 1402 a consumer vector for each consumer in the reference set, as well as a value describing the known or predicted response rate relevant to the offer being analyzed.

The consumer vector for a proposed target consumer is then obtained 1403, and the nearest neighbors in the reference set are identified 1404. The nearest neighbors may be identified, for example, by performing dot-product operations on the target consumer vector with reference consumer vectors, and selecting those reference consumer vectors that provide the highest dot-product value (i.e., those that are closest to the target consumer vector). In one embodiment, the nearest-neighbor set is defined according to a threshold dot-product value; in another embodiment, a fixed number N of consumer vectors is identified, selected among those that are closest to the target consumer vector.

In one embodiment, hierarchical clustered searches are used in determining and identifying nearest neighbors. Such an approach can save computation time by obviating the need to compute dot-product results involved each vector in the entire set of consumer vectors. As is known in the art, a hierarchical clustered search improves efficiency of such operations by breaking down the reference population into groups according to a hierarchy, so that dot products need only be computed for those vectors within the relevant group(s).

The response rate among the nearest neighbors is aggregated 1405 and used as a predictor of the likely response rate for the target consumer. For example, for the consumers in the nearest-neighbor set, the system determines the ratio of those who responded positively to those who responded negatively (or did not respond at all). This ratio is the target consumer's score with respect to the offer, and may be provided as output 1406 by the system. Based on this score for a number of potential target consumers, the marketing effort can be targeted at those consumers most likely to respond favorably, thus improving the efficiency of the marketing campaign.

Using the techniques of FIG. 14, the present invention is able to generate recommendations as to which offers to present to which consumers. Examples of the types of recommendations include:

• • recommend offers to which the consumer is mostly likely to respond positively (highest score);
  • recommend offers that are likely to achieve the highest profit (maximizing score×product profit×cost of making the offer);
  • recommend offers that are optimal based on some other measure, taking into account any other relevant factors, such as product availability, portfolio risk, and the like.

In one embodiment, the nearest-neighbor response rate may be fused with other data for more advanced analysis. For example, the aggregated response rate could be provided as an input to a second-level predictive model, along with other input data (such as demographic information, for example). The second-level predictive model could be trained on the input data, using techniques known in the art, in order to improve response prediction accuracy for target consumers.

In one embodiment, in developing consumer vectors for the reference set, the present invention excludes purchases of the target product itself. If such purchases are included in developing the reference set vectors, the vectors for consumers who purchased the product will by definition differ from vectors for desirable target consumers (since those target consumers have not yet purchased the target product), so that a systemic bias results. What is desired is an indication of which target consumers are most similar to consumers in the reference set except for the fact that they have not yet purchased the product being targeted. Thus, exclusion of target product purchases may provide more accurate predictions.

In one embodiment, the selection of reference consumers in step 1401 is performed by random selection means among a representative population, so as to obtain a random sample including those that responded positively and those that responded negatively (or did not respond at all). Often, however, truly representative selection is not possible, since previous offers of the product or service may have been targeted at selected populations, so that the data set from which the sample is being obtain may itself be skewed. Accordingly, it may be difficult to obtain a random selection from a representative population.

In addition, random sampling tends to yield many more non-responders and negative responders than positive responders, by virtue of the fact that, in general, the vast majority of people respond negatively (or not at all) to offers. Thus, random selection of reference consumers tends to result in an undue emphasis on non-responders and negative responders, with a corresponding lack of predictive data points for positive responders. This is an unfavorable result, since it weakens the ability of the system to develop sufficient numbers of vectors for the very population segment that is of the most interest, namely those who responded positively in the past.

Accordingly, the present invention may select reference consumers by some non-random means. If non-random selection is employed, the effect of the non-randomness is reversed so that the score developed by the system is a true probability estimate. For example, if the number of positive responders is over-represented in the reference set by a factor of 10, the resulting score is divided by 10 to compensate for the bias towards positive responders.

M. Supervised Segmentation of Consumer Vectors

Supervised segmentation of merchant vectors is described above as a technique for developing merchant segments that are of interest. In one embodiment, the present invention employs supervised segmentation of consumer vectors as an alternative to the nearest-neighbor technique described above for predicting response rates of consumers. Such a technique may be performed, for example, using an LVQ methodology similar to that described above in connection with merchant vectors.

Referring now to FIG. 15, there is shown a flowchart depicting a technique of supervised segmentation of consumer vectors for predicting a response rate for a consumer with regard to a particular offer. A set of reference consumers is labeled 1501 according to their response history for an offer. For each product offer, there are two classes of individuals—responders (those who responded positively) and non-responders (those who responded negatively or did not respond at all). Alternatively, multiple segment vectors can be trained with different ratios (or ranges of ratios) of responders to non-responders in order to model the response likelihood contours in the feature space.

A set of segment vectors are initialized 1502 for the specified consumer segments. The initial segment vectors may be orthogonal to one another, or randomly assigned. Typically, the segment vectors occupy the same space as do consumer vectors, so that memberships, degrees of similarity, and affinities between consumers and segments can be defined and quantified. In another alternative embodiment, more than one segment vector may be assigned to each segment in order to identify discontinuous regions of high response likelihood and to better approximate the decision boundaries.

A labeled reference consumer is selected 1503. A consumer vector is obtained for the selected reference consumer, and a segment is selected 1504 for the consumer based on the consumer vector. As described previously, segment selection may be performed according to any one of several methods, including for example determining which segment vector is most closely aligned with the consumer vector. If, in 1505, the selected segment does not correspond to the segment label that has been assigned to the consumer, one or more segment vectors are adjusted 1506 in an effort to “train” the segment vectors. Either the segment vector for the assigned segment is moved farther from the consumer vector, or the “correct” segment vector (i.e., the segment vector closest to the consumer vector) is moved closer to the segment vector, or both vectors are adjusted. Examples discussed above in connection with FIGS. 11A through 11C and 12A through 12C are applicable.

Once segments have been adjusted (if appropriate), a determination is made 1507 as to whether more training is required. This determination is made based on known convergence determination methods, or by reference to a predefined count of training iterations, or by another other appropriate means. One advantage to the present invention is that not all consumer vectors need be manually labeled in order to effectively train the vector set; once the segment vectors are sufficiently trained, consumers will automatically become associated with appropriate segments based on the positioning of their vectors.

Thus, the present invention provides a technique for developing segment vectors such that probability of response for each region of feature space may be determined. For a new target customer, the consumer vector is compared with segment vectors; based on a determination of response rate for a corresponding segment vector, the estimated response probability for the target customer can be generated. Such a technique is advantageous in that it results in reduced search time over a nearest-neighbor technique, and is more likely to provide accurate results in the present of abrupt response likelihood boundaries in the feature space.

In summary then, the present invention provides a variety of powerful analytical methods for predicting consumer financial behavior in discretely defined merchant segments, and with respect to predetermined time intervals. The clustering of merchants in merchant segments allows analysis of transactions of consumers in each specific segment, both historically, and in the predicted period to identify consumers of interest. Identified consumers can then be targeted with promotional offers precisely directed at merchants within specific segments. Supervised segmentation techniques may be employed to facilitate definition and analysis of particular market segments. Nearest-neighbor techniques may be used in place of segment-based models to develop predictions of consumer behavior.

Appendix I: N-Gram Matching Algorithm

• 1. A set of training examples is presented to the algorithm. In this case, the training examples are all the merchant names that are being processed.
• 2. Each training example is broken down into all possible n-grams, for a selected value of n (n=3 for trigrams) E.g. the merchant name “wal-mart” yields the trigrams ^w, ^wa, wal, al-, l-m, -ma, mar, art, rt^, t^, where ^ is an “end of string” token.
• 3. The frequencies with which each trigram appears anywhere in the training examples are counted.
• 4. In the preferred embodiment, each trigram is assigned a weight, given by
•  where xyz indicates the particular trigram, F_xyz is the number of times the trigram appeared anywhere in the training examples, and N is the maximum value of F for all trigrams. Thus, frequently occurring trigrams are assigned low weights, while rare trigrams are assigned high weights. Other weighting schemes, including uniform weights, are possible.
• 5. A high dimensional vector space is constructed, with one dimension for each trigram that appears in the set of training examples.
• 6. To compare two particular strings of characters (merchant names), string1 and string2, each string is represented by a vector in the vector space. The vector for a string1 is constructed by:
  • a) counting the frequency of each trigram in the string, f_xyz
  • b) assembling a weighted sum of unit vectors,
  •  where xyz ranges over all trigrams in string1, and ū_xyz is a unit vector in the direction of the xyz dimension in the vector space.
  • c) normalizing V_string1 to length a length of one (preferred embodiment), or utilizing another normalization, or providing no normalization at all.
  • d) construct the similar vector corresponding to the other string, V_string2
  • e) take the dot product of V_string1 and V_string2. A high dot product (near one) indicates that the two strings are closely related, while a low dot product (near zero) indicates that the two strings are not related.
• 7. Two merchant names are equivalenced if their vectors' dot product is greater than a particular threshold. This threshold is typically in the range of 0.6 to 0.9 for the preferred embodiment.

Appendix II: Geometrically Derived Vector Training Algorithm

Initialize:

• • For each stem, iε{all stems in corpus}
    • V_i=rand_vector //random vector for stem i
    • Normalize V_i to length 1
    • Δ V_i= 0, //zero initialized update vector for stem i
  • END
  • For each stem, iε{all stems in corpus}
    • Calculate Updates:
    • For each stem, jε{all stems that co-occurred with stem i}, j≠i
    • We wish to calculate a new vector, U_ij, that is the ideal position of V_i with respect to V_j. In other words, we want the dot product of U_ij with V_j to be d_ij, we want U_ij to have unit length, and we want U_ij to lie in the plane defined by V_i and V_j.
      • D= V_i− V_j //vector difference between vectors for stems j and i.
      • θ= D− V_i·dot( V_j, D) //θ is vector of components of D which are orthogonal to Vj. This defines a plane between Vj and θ in which Vi lies.

$\stackrel{\rightharpoonup }{\theta }=\frac{\theta }{\stackrel{\rightharpoonup }{\theta }}//\mathrm{normalize}\theta$ $l=\mathrm{sqrt}\left(\frac{1-d_{\mathrm{ij}}^2}{d_{\mathrm{ij}}^2}\right)//l\mathrm{is}\mathrm{weight}\mathrm{for}\theta$

• • • • IF d_ij>0 THEN //if positive relationship between stems j and i
        U_ij=V_j+l·θ
      • ELSE IF d_ij<0 THEN //if negative relationship
        U_ij=−V_j+l·θ
      • END IF
        U_ij=Ū_iji∥Ūj_ij|// normalize
      • We construct a weighted sum of the U_ij for all j to derive an estimate of where V_i should be.
      • IF weight_mode==LOG_FREQ THEN
        Δ V_i=Δ V_i+Ū_ij·└1−dot(Ū_ij, V_i)┘└1+log F└j┘┘
      • ELSE IF weight_mode==FREQ THEN
        Δ V_i=Δ V_i+Ū_if·└1−dot(Ū_ij, V_i)┘F└j┘
      • ELSE
        Δ V_i=Δ V_i+Ū_ij·└1−dot(U_ij, V_i)┘
        END IF
    • END j
    • Perform Update:
      V_i^new=(1−gamma)· V_i+gamma·Δ V_i
      V_i^new= V_i^newΛ V_i^new∥
  • END i
• Notes:
• 1) Stems here are root merchant names.
• 2) The list of stems j (merchant names) that co-occur with stem i is known from the co-occurrence data.
• 3) dij is relationship strength measure, calculated by UDL1, UDL2, or UDL3.
• 4) F[j] is the frequency at which stem j appears in the data.
• 5) Weight_mode is a user-controlled value that determines the influence that F[j] has on the U. If weight_mode is FREQ then the frequency of stem j directly effects U, so that higher frequency stems (merchant names) strongly influence the resulting merchant vector of merchant i. A slower influence is provided by weight_mode=LOG FREQ, which uses the log of F[j]. If weight_mode is not set, then the default is no influence by F[j].
• 6) Gamma is a learning rate 0-1, typically 0.5 to 0.9

Appendix III: Algebraically Derived Vector Training Algorithm

Initialize:

• • For each stem, iε{all stems in corpus}
    • V_i=rand_vector //initialize a random vector for stem i
    • Normalize V_i //normalize vector to unit length
    • Δ V_i= 0, // define a zero initialized update vector for stem i
  • END
  • For each stem, iε{all stems in corpus}
    • Calculate Updates:
    • For each stem, jε{all stems that co-occurred with stem i}, j≠i // this is all merchants j that co-occur with merchant i We wish to calculate a new vector, U_ij, that is the ideal position of V_i with respect to V_j. In other words, we want the dot product of U_ij with V_j to be d_ij, we want U_ij to have unit length, and we want U_ij to lie in the plane defined by V_i and V_j.
      • U_ij can be expressed as a linear combination of V_i and V_j where:

${\varepsilon }_{\mathrm{ij}}=\mathrm{dot}\left(V_i,V_j\right),{\rho }_{\mathrm{ij}}={\left(\frac{1-d_{\mathrm{ij}}^2}{1-{\varepsilon }_{\mathrm{ij}}^2}\right)}^{1/2},\mathrm{and}$ ${\stackrel{\rightharpoonup }{U}}_{\mathrm{ij}}=\left(d_{\mathrm{ij}}-{\varepsilon }_{\mathrm{ij}}{\rho }_{\mathrm{ij}}\right)V_j+{\rho }_{\mathrm{ij}}{V}_i.$

• • • • We construct a weighted sum of the U_ij for all j to derive an estimate of where V_i should be. IF weight_mode==LOG_FREQ THEN
        ΔV_i=ΔV_i+U_ij·└1−dot(U_ij, V_i)┘└1+log F└j┘┘
      • ELSE IF weight_mode==FREQ THEN
        ΔV_i=ΔV_i+U_ij·└1−dot(U_ij, V_i)┘F└j┘
      • ELSE
        Δ V_i=Δ V_i+Ū_ij·└1−dot(Ū_ij, V_i)┘
      • END IF
    • END j
    • Perform Update:
      V_i^new=(1−gamma)· V_i+gamma·Δ V_i
      V_i^new= V_i^new/∥ V_i^new∥
  • END i
    Notes:
• 1) Stems here are root merchant names.
• 2) The list of stems j (merchant names) that co-occur with stem i is known from the co-occurrence data.
• 3) dij is relationship strength measure, calculated by UDL1, UDL2, or UDL3.
• 4) F[j] is the frequency at which stem j appears in the data.
• 5) Weight_mode is a user-controlled value that determines the influence that F[j] has on the U. If weight_mode is FREQ then the frequency of stem j directly effects U, so that higher frequency stems (merchant names) strongly influence the resulting merchant vector of merchant i. A slower influence is provided by weight_mode=LOG FREQ, which uses the log of F[j]. If weight_mode is not set, then the default is no influence by F[j].
• 6) Gamma is a learning rate 0-1, typically 0.5 to 0.9

Claims

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

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 of consumers; and

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

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein identifying at least one nearest neighbor comprises identifying consumer vectors having a dot product between the consumer vector and the target consumer vector that exceeds a predetermined threshold.

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

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 of consumers; and

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

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein identifying at least one nearest neighbor comprises identifying a predetermined number of consumer vectors having the highest dot products between the consumer vector and the target consumer vector.

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

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 of consumers; and

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

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

further comprising fusing the generated behavior prediction with additional data to generate a second-level behavior prediction.

4. The method of claim 3, wherein fusing comprises:

training a second-level predictive model with generated behavior predictions and additional data; and

applying the generated behavior prediction and additional data to the trained second-level predictive model to obtain a second-level behavior prediction.

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

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 of consumers; and

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

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the consumer vectors for the reference set exclude target product purchases.

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

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 of consumers; and

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

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the reference set of consumers is selected randomly.

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

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 of consumers; and

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

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the reference set of consumers is selected non-randomly, and further comprising adjusting the generated behavior prediction to compensate for the non-randomness of the reference set selection.

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

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;

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein defining at least one consumer segment comprises:

initializing a set of consumer segment vectors;

accepting at least one segment label for at least one of the consumers;

for each of at least a subset of the labeled consumers:

selecting at least one consumer segment vector for a consumer;

determining whether the selected consumer segment vector matches the segment label for the consumer; and

responsive to the determination, adjusting zero or more of the consumer segment vectors.

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

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:

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the target consumer is associated with a target consumer vector, and wherein determining a consumer segment for the target consumer comprises selecting a consumer segment corresponding to a consumer segment vector having the highest dot product between the consumer segment vector and the target consumer vector.

10. 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 one or more merchant vectors for at least a subset of merchants;

a segmentation module for applying segmentation to said one or more merchant vectors to provide at least one merchant segment;

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

said system configured for:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the merchant segment predictive model identifies at least one nearest neighbor by identifying consumer vectors having a dot product between the consumer vector and the target consumer vector that exceeds a predetermined threshold.

11. 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 one or more merchant vectors for at least a subset of merchants;

a segmentation module for applying segmentation to said one or more merchant vectors to provide at least one merchant segment;

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

said system configured for:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the merchant segment predictive model identifies at least one nearest neighbor by identifying a predetermined number of consumer vectors having the highest dot products between the consumer vector and the target consumer vector.

12. 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 one or more merchant vectors for at least a subset of merchants;

a segmentation module for applying segmentation to said one or more merchant vectors to provide at least one merchant segment;

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

said system configured for:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the merchant segment predictive model fuses the generated behavior prediction with additional data to generate a second-level behavior prediction.

13. 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 one or more merchant vectors for at least a subset of merchants;

a segmentation module for applying segmentation to said one or more merchant vectors to provide at least one merchant segment;

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

said system configured for:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the consumer vectors for the reference set exclude target product purchases.

14. 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 one or more merchant vectors for at least a subset of merchants;

a segmentation module for applying segmentation to said one or more merchant vectors to provide at least one merchant segment;

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

said system configured for:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the reference set of consumers is selected randomly.

15. 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 one or more merchant vectors for at least a subset of merchants;

a segmentation module for applying segmentation to said one or more merchant vectors to provide at least one merchant segment;

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

said system configured for:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein the reference set of consumers is selected non-randomly, and wherein the predictive model adjusts the generated behavior prediction to compensate for the non-randomness of the reference set selection.

16. 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, 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 financial behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors;

wherein the computer-readable code adapted to generate a behavior prediction comprises:

computer-readable code to train a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

computer-readable code to use an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

computer-readable code to apply the predictive model to the consumer vector of the target consumer and output a predicted spending amount for said target consumer; and

wherein the computer-readable code adapted to identify at least one nearest neighbor comprises computer-readable code adapted to identify consumer vectors having a dot product between the consumer vector and the target consumer vector that exceeds a predetermined threshold.

17. 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, 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 financial behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors;

wherein the computer-readable code adapted to generate a behavior prediction comprises:

computer-readable code to train a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

computer-readable code to use an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

computer-readable code to apply the predictive model to the consumer vector of the target consumer and output a predicted spending amount for said target consumer; and

wherein the computer-readable code adapted to identify at least one nearest neighbor comprises computer-readable code adapted to identify a predetermined number of consumer vectors having the highest dot products between the consumer vector and the target consumer vector.

18. 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, 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 financial behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors;

wherein the computer-readable code adapted to generate a behavior prediction comprises:

computer-readable code to train a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

computer-readable code to use an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

computer-readable code to apply the predictive model to the consumer vector of the target consumer and output a predicted spending amount for said target consumer; and

further comprising computer-readable code adapted to fuse the generated behavior prediction with additional data to generate a second-level behavior prediction.

19. The computer-readable medium of claim 18, wherein the computer-readable code adapted to fuse comprises:

computer-readable code adapted to train a second-level predictive model with generated behavior predictions and additional data; and

computer-readable code adapted to apply the generated behavior prediction and additional data to the trained second-level predictive model to obtain a second-level behavior prediction.

20. 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, 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 financial behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors;

wherein the computer-readable code adapted to generate a behavior prediction comprises:

computer-readable code to train a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

computer-readable code to use an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

computer-readable code to apply the predictive model to the consumer vector of the target consumer and output and predicted spending amount for said target consumer; and

wherein the consumer vectors for the reference set exclude target product purchases.

21. 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, 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 financial behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors;

wherein the computer-readable code adapted to generate a behavior prediction comprises:

computer-readable code to train a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

computer-readable code to use an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

computer-readable code to apply the predictive model to the consumer vector of the target consumer and output a predicted spending amount for said target consumer; and

wherein the reference set of consumers is selected randomly.

22. 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, 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 financial behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors;

wherein the computer-readable code adapted to generate a behavior prediction comprises:

computer-readable code to train a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

computer-readable code to use an unexpected deviation learning approach to determine values of the merchant vectors:

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

computer-readable code to apply the predictive model to the consumer vector of the target consumer and output a predicted spending amount for said target consumer; and

wherein the reference set of consumers is selected non-randomly, and further comprising computer-readable code adapted to adjust the generated behavior prediction to compensate for the non-randomness of the reference set selection.

23. 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 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;

wherein the computer-readable code adapted to generate a behavior prediction comprises:

computer-readable code to train a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

computer-readable code to use an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

computer-readable code to apply the predictive model to the consumer vector of the target consumer and output a predicted spending amount for said target consumer; and

wherein the computer-readable code adapted to define at least one consumer segment comprises:

computer-readable code adapted to initialize a set of consumer segment vectors;

computer-readable code adapted to accept at least one segment label for at least one of the consumers;

computer-readable code adapted to, for each of at least a subset of the labeled consumers:

select at least one consumer segment vector for a consumer;

determine whether the selected consumer segment vector matches the segment label for the consumer; and

responsive to the determination, adjust zero or more of the consumer segment vectors.

24. 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 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;

wherein the computer-readable code adapted to generate a behavior prediction comprises:

computer-readable code to train a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

computer-readable code to use an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

computer-readable code to apply the predictive model to the consumer vector of the target consumer and output a predicted spending amount for said target consumer; and

wherein the target consumer is associated with a target consumer vector, and wherein the computer-readable code adapted to determine a consumer segment for the target consumer comprises computer-readable code adapted to select a consumer segment corresponding to a consumer segment vector having the highest dot product between the consumer segment vector and the target consumer vector.