U.S. patent application number 12/561653 was filed with the patent office on 2010-05-06 for customer reference generator.
This patent application is currently assigned to ORACLE INTERNATIONAL CORPORATION. Invention is credited to Marcos CAMPOS, Francisco V. CASAS, Krisztian Z. DANKO, Jooyoung John KIM, Ari MOZES, Peter J. STENGARD.
Application Number | 20100114665 12/561653 |
Document ID | / |
Family ID | 42132574 |
Filed Date | 2010-05-06 |
United States Patent
Application |
20100114665 |
Kind Code |
A1 |
STENGARD; Peter J. ; et
al. |
May 6, 2010 |
CUSTOMER REFERENCE GENERATOR
Abstract
A system generates a customer reference recommendation based on
similarity to other customers. The system includes a customer data
file including demographic data and purchasing pattern data for a
plurality of customers, a first cluster model trained on the
demographic data, and a second cluster model trained on the
purchasing pattern data. A customer reference generator produces a
customer reference recommendation based on cluster membership in
the first and second cluster models in response to a query from a
user interface.
Inventors: |
STENGARD; Peter J.; (St.
Pete Beach, FL) ; CASAS; Francisco V.; (San Mateo,
CA) ; KIM; Jooyoung John; (Palo Alto, CA) ;
DANKO; Krisztian Z.; (Waterloo, CA) ; MOZES; Ari;
(Lexington, MA) ; CAMPOS; Marcos; (Billerica,
MA) |
Correspondence
Address: |
Squire, Sanders & Dempsey L.L.P.;Oracle International Corporation
8000 Towers Crescent Drive, 14th Floor
Vienna
VA
22182
US
|
Assignee: |
ORACLE INTERNATIONAL
CORPORATION
Redwood Shores
CA
|
Family ID: |
42132574 |
Appl. No.: |
12/561653 |
Filed: |
September 17, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61110729 |
Nov 3, 2008 |
|
|
|
Current U.S.
Class: |
705/7.33 ;
705/348; 706/46; 715/764 |
Current CPC
Class: |
G06F 16/214 20190101;
G06F 16/2282 20190101; G06Q 30/0204 20130101; G06Q 10/067 20130101;
G06Q 30/0201 20130101; G06Q 30/02 20130101; G06Q 30/0202 20130101;
G06F 16/902 20190101 |
Class at
Publication: |
705/10 ; 705/348;
706/46; 715/764 |
International
Class: |
G06Q 10/00 20060101
G06Q010/00; G06Q 30/00 20060101 G06Q030/00 |
Claims
1. A computer-readable medium having instructions stored thereon
that, when executed by a processor, cause the processor to generate
a customer reference recommendation by: uploading customer data
including past purchasing pattern data and demographic data for a
plurality of customers; requesting a customer reference
recommendation; and receiving at least one customer reference
recommendation from a prediction model trained by the purchasing
pattern data and the demographic data.
2. The computer-readable medium of claim 1, wherein demographic
data includes at least one of: a customer location, industry,
headquarter country, public corporation status, importer status,
exporter status, number of locations, number of employees, and
annual revenue.
3. The computer-readable medium of claim 1, wherein the at least
one customer reference recommendation includes a similarity score
to another customer.
4. The computer-readable medium of claim 1, wherein the prediction
model comprises a first cluster model and a second cluster
model.
5. The computer-readable medium of claim 4, wherein the first
cluster model is built on the demographic data.
6. The computer-readable medium of claim 4, wherein the second
cluster model is built on the past purchasing pattern data.
7. The computer-readable medium of claim 4, wherein the first and
second cluster models are used to calculate a similarity score
based on cluster membership.
8. The computer-readable medium of claim 7, wherein a first
customer and a second customer both sharing membership in a first
cluster in the first cluster model and a second cluster in the
second cluster model generates a maximum similarity score.
9. A computer-implemented method for generating a customer
reference recommendation, comprising: receiving customer data
including purchasing pattern data and demographic data for a
plurality of customers; generating a prediction model trained by
the purchasing pattern data and the demographic data; receiving a
query for a customer reference recommendation; and responding to
the query with at least one customer reference recommended by the
prediction model.
10. The method of claim 9, wherein demographic data includes at
least one of: a customer location, industry, headquarter country,
public corporation status, importer status, exporter status, number
of locations, number of employees, and annual revenue.
11. The method of claim 9, wherein the at least one customer
reference includes a similarity score to another customer.
12. The method of claim 9, wherein the prediction model comprises a
first cluster model and a second cluster model.
13. The method of claim 12 wherein the first cluster model is built
on the demographic data.
14. The method of claim 12, wherein the second cluster model is
built on the purchasing pattern data.
15. The method of claim 12, wherein the first and second cluster
models are used to calculate a similarity score based on cluster
membership.
16. A system for generating a customer reference recommendation,
comprising: a customer data file including demographic data and
purchasing pattern data for a plurality of customers; a first
cluster model trained on the demographic data; a second cluster
model trained on the purchasing pattern data; a customer reference
generator that produces a customer reference recommendation based
on cluster membership in the first and second cluster models; and a
user interface for querying the customer reference generator.
17. The system of claim 16, wherein the customer reference
recommendation is based on a similarity score generated from
cluster memberships in the first cluster model and the second
cluster model.
18. The computer-readable medium of claim 1, further comprising
generating a buying pattern prediction for a new customer.
19. The computer-readable medium of claim 1, wherein the customer
reference recommendation comprises a list of customer references in
a ranked order.
20. The computer-readable medium of claim 1, further comprising
receiving feedback for at least one of the customer reference
recommendation.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to provisional application
No. 61/110,729 filed Nov. 3, 2008, which is hereby incorporated by
reference.
FIELD OF THE INVENTION
[0002] One embodiment is directed generally to customer sales, and
more particularly to an computer system for assisting with customer
sales.
BACKGROUND INFORMATION
[0003] A "sales lead" typically includes a name or other indicia of
identity (e.g., a phone number, mailing address or email address)
of a person or business that may have an interest in purchasing a
product or service. A sales lead provides a starting point for a
salesperson to further develop the lead by marketing a specific
vendor's product or service. The salesperson develops the sales
lead by gathering information about the potential customer and
providing the customer with information about the vendor. This
mutual exchange of information helps the salesperson to persuade
the potential customer to purchase a product or service from the
vendor. If the customer makes a purchase, the salesperson has
converted the sales lead into a sales transaction. A sales lead
with a high probability of being converted into a sales transaction
is considered a good lead.
[0004] Data mining can assist in generating sales leads by finding
patterns in information gathered about customers. In the past,
vendors have used data mining to match potential customers to
products. While this is helpful, a lead itself possesses no measure
of success in terms of converting the lead into a sale. There may
be other factors that figure into the success of a lead which are
not presently accounted for. For example, there may be other
customers with similar attributes and similar buying patterns, but
there are is no way to determine which of these customer references
are the best references for recommending a sales call.
SUMMARY OF THE INVENTION
[0005] One embodiment is a system that generates a customer
reference recommendation based on similarity to other customers.
The system includes a customer data file including demographic data
and past purchasing pattern data for a plurality of customers, a
first cluster model trained on the demographic data, and a second
cluster model trained on the past purchasing pattern data. A
customer reference generator produces a customer reference
recommendation based on cluster membership in the first and second
cluster models.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 is a block diagram of a system that can implement a
customer reference generator in accordance with an embodiment.
[0007] Fig. is a block diagram of a customer reference generator in
an accordance with an embodiment.
[0008] FIG. 3 is a flow diagram of the functionality of the
customer reference generator in accordance with an embodiment.
[0009] FIG. 4 is an example user interface of the customer
reference generator in accordance with an embodiment.
[0010] FIG. 5 is an example user interface that illustrates a
customer referenceability box that pops up in response to a user
selecting a feedback icon.
DETAILED DESCRIPTION
[0011] One embodiment is a customer reference generator that
identifies reference customers to use in selling to a perspective
new customer. The reference customers are generated from a hybrid
prediction model that includes a customer demographic cluster model
and a customer past purchases cluster model.
[0012] In one embodiment, an administrator will first upload
customer data with which to train a prediction model. The customer
data may include past purchasing history, assets, demographics,
etc. A hybrid prediction model is then trained based on both the
demographic pattern data and the past purchasing pattern data in
the customer data. The model is considered a "hybrid" because it
uses two types of data when creating the model. One type of data
includes two-dimensional demographics data where each customer has
one row with some attribute values set for a constant set of
attributes. The other type of data, transactional data, relates to
the customers' orders. The number of orders and products differ per
customer. Once the models are built, a sales person may query the
models to acquire customer references based on another customer or
a product. A data mining operation based on a clustering model
returns customer references based on similarity. Similarity is
based on both demographic attributes of customers and their past
purchasing patterns.
[0013] FIG. 1 is a block diagram of a system 10 that can implement
an embodiment of a customer reference generator. System 10 includes
a bus 12 or other communication mechanism for communicating
information, and a processor 22 coupled to bus 12 for processing
information. Processor 22 may be any type of general or specific
purpose processor. System 10 further includes a memory 14 for
storing information and instructions to be executed by processor
22. Memory 14 can be comprised of any combination of random access
memory ("RAM"), read only memory ("ROM"), static storage such as a
magnetic or optical disk, or any other type of computer readable
media. System 10 further includes a communication device 20, such
as a network interface card, to provide access to a network.
Therefore, a user may interface with system 10 directly, or
remotely through a network (such as the Internet) or any other
method.
[0014] Computer readable media may be any available media that can
be accessed by processor 22 and includes both volatile and
nonvolatile media, removable and non-removable media, and
communication media. Communication media may include computer
readable instructions, data structures, program modules or other
data in a modulated data signal such as a carrier wave or other
transport mechanism and includes any information delivery
media.
[0015] Processor 22 is further coupled via bus 12 to a display 24,
such as a Liquid Crystal Display ("LCD"), for displaying
information to a user. A keyboard 26 and a cursor control device
28, such as a computer mouse, is further coupled to bus 12 to
enable a user to interface with system 10.
[0016] In one embodiment, memory 14 stores software modules that
provide functionality when executed by processor 22. The modules
include an operating system 15 that provides operating system
functionality for system 10. The modules further include a customer
reference generator module 120. This module is described in greater
detail below. The modules further include enterprise resource
planning ("ERP") modules 18 of an ERP system that may interact with
customer reference generator module 120. An ERP system is a
computer system that integrates several data sources and processes
of an organization into a unified system. A typical ERP system uses
multiple components of computer software and hardware to achieve
the integration. A unified ERP database 17, coupled to bus 12, is
used to store data for the various system modules. In one
embodiment, ERP modules 18 are part of the "Oracle E-Business Suite
Release 12" ERP system from Oracle Corp. In other embodiments,
customer reference generator module 120 may be a stand-alone system
and not integrated with an ERP system, or may be part of any other
integrated system. In some embodiments, the functions of customer
reference generator module 120, described below, are directed and
utilized remotely from a user's computer 50 through communication
device 20. In one embodiment, the functionality disclosed below may
be accessed remotely by a user as a software as a service
("SAAS").
[0017] FIG. 2 is a block diagram of a customer relationship
management ("CRM") system 200 in which customer reference generator
module 120 may be used in accordance with an embodiment. CRM module
220 is an ERP module for managing customer information, including
demographic data, past purchasing patterns, assets owned, etc. CRM
module 220 includes schemas for the customer data, and interacts
with database 17 to store the customer data in database 17 in
accordance with these schemas. Customer reference generator module
120 includes a user interface (described below) for viewing
customer data, for entering queries for customer references, and
for viewing the results of those queries. Customer reference
generator 120 performs data mining in database 17 to retrieve the
results of the queries.
[0018] FIG. 3 illustrates a flow diagram of the functionality of
customer reference generator module 120 in accordance with an
embodiment. In one embodiment, the functionality of the flow
diagram of FIG. 3 is implemented by software stored in memory and
executed by a processor. In other embodiments, the functionality
may be performed by hardware (e.g., through the use of an
application specific integrated circuit ("ASIC"), a programmable
gate array ("PGA"), a field programmable gate array ("FPGA"),
etc.), or any combination of hardware and software.
[0019] An administrator of system 10 first imports customer data
into the customer reference generator module 120 in the form of a
Comma Separated Value ("CSV") file (310). The customer data may be
imported, for example, from database 17, and includes both past
purchasing pattern attributes and demographic attributes. The CSV
files are a fixed file format that include five record types:
customers, products, orders, order lines, and target customers by
user. The CSV file formats follow a specific format, described
below, in an embodiment. The CSV files may be imported into
customer reference generator module 120 in any order. For customer
reference generator module 120 to provide references or make a
sales prediction, at least the following record types should be
present: customers, products, orders, and order lines. In the CSV
file, each column is separated by a comma, and each record starts
on a new line.
[0020] A sample customers.csv file is now described. Table 1
illustrates the data types for customer records:
TABLE-US-00001 TABLE 1 Data Max. Re- Column Type Length quired
Description Customer_ID String 30 Char. Yes Customer identifier
Customer_Name String 200 Char. Yes Customer name Customer_Location
String 300 Char. No Customer location Owner_Email String 100 Char.
Yes Email address of account owner Owner_Name String 200 Char. Yes
Name of the customer account owner VarChar_1 String 200 Char. No
Industry VarChar_2 String 200 Char. No Headquarter's country
VarChar_3 String 200 Char. No Public or Private VarChar_4 String
200 Char. No Importer or Exporter VarChar_5 String 200 Char. No
Custom string field Numeric_1 Number 10 digits No Annual revenue
Numeric_2 Number 10 digits No Number of employees Numeric_3 Number
10 digits No Number of locations Numeric_4 Number 10 digits No
Custom numeric field Date_1 Date N/A No Custom date field
An example customers.csv file is presented below: [0021]
Customer_ID, Customer_Name, Customer_Location, Owner_Email,
Owner_Name, VarChar.sub.--1, VarChar.sub.--2, VarChar.sub.--3,
VarChar.sub.--4, VarChar.sub.--5, VarChar.sub.--6, VarChar.sub.--7,
VarChar.sub.--8, VarChar.sub.--9, VarChar.sub.--10,
Numeric.sub.--1, Numeric.sub.--2, Numeric.sub.--3, Numeric.sub.--4,
Numeric.sub.--5, Numeric.sub.--6, Numeric.sub.--7, Numeric.sub.--8,
Numeric.sub.--9, Numeric.sub.--10, Date.sub.--1, Date.sub.--2,
Date.sub.--3, Date.sub.--4, Date.sub.--5, Date.sub.--6,
Date.sub.--7, Date.sub.--8, Date 9, Date.sub.--10 Cust-01, Customer
1 Name, brenda.moore@company.com, Brenda Moore, Manufacturing,
Canada, Public, Both, . . . , 4521, 135, 3, . . . , Cust-02,
Customer 2 Name, USA, donna.parker@company.com, Donna Parker,
Financial Services, USA, Public, Importer, . . . , 12129, 929, 6, .
. . ,
[0022] A sample products.csv file is now described. Table 2
illustrates the data types for product records:
TABLE-US-00002 TABLE 2 Column Data Type Max. Length Required
Description Product_ID String 30 Char. Yes Product identifier
Product_Name String 100 Char. Yes The product name
An example products.csv file is presented below: [0023] Product_ID,
Product_Name [0024] Prod-01, Oracle Database 10g [0025] Prod-02,
Oracle Database 11g
[0026] A sample orders.csv file is now described. Table 3
illustrates the data types for order records:
TABLE-US-00003 TABLE 3 Data Max. Column Type Length Required
Description Order_ID String 30 Char. Yes Order header identifier
Customer_ID String 30 Char. Yes Identifies customer on this
order
An example orders.csv file is presented below: [0027] Order_ID,
Customer_ID [0028] Order-01, Cust-01 [0029] Order-02, Cust-02
[0030] A sample order_lines.csv file is now described. Table 4
illustrates the data types for order line records:
TABLE-US-00004 TABLE 4 Data Max. Re- Column Type Length quired
Description Order_Line_ID String 30 Char. Yes Order line identifier
Order_ID String 30 Char. Yes Order identifier Product_ID String 30
Char. Yes Product_ID references a product in the Products.csv file
Quantity Number 15 digits No Quantity sold on this with 2 order
line decimal places Amount Number 15 digits No The order line
amount with 2 decimal places Close_Date Date N/A No The date when
the product sale is closed Lead_Date Date N/A No The date when the
lead that resulted in this order line was received
An example order_line.csv file is presented below: [0031]
Order_Line_ID, Order_ID, Product_ID, Quantity, Amount, Close_Date,
Lead_Date [0032] Order-Ln-01, Order-01, Prod-01, 10, 10,
2007-02-06, 2008-10-15 [0033] Order-Ln-02, Order-02, Prod-02, 50,
50, 2007-03-09, 2009-02-15
[0034] A sample member_customers.csv file is now described. Table 5
illustrates the data types for user-to-customer mapping
records:
TABLE-US-00005 TABLE 5 Data Max. Re- Column Type Length quired
Description User_Email String 100 Char. Yes The email address of a
community member Customer_ID String 30 Char. Yes The customer
identifier Operation String 2 Char. Yes Indicates whether the
current record should be inserted or deleted in the database, as
follows: "I" indicates an insert. The insert does not succeed if
the record already exists "UC" indicates an update by customer "UM"
indicates an update by member (the sales representative) "D"
indicates delete
An example member_customers.csv file is presented below: [0035]
User_Email, Customer_ID, Operation [0036] brenda.moore@company.com,
Cust-01, I [0037] brenda.moore@company.com, Cust-02, I [0038]
donna.Parker@company.com, Cust-04, I
[0039] Next, a hybrid prediction model is built for predicting, for
a product, one or more existing or previous customers that would be
the best references for the product (320). One embodiment uses a
clustering function to organize customers into clusters. Two
clustering models are built, one based on customers' demographic
attributes and another based on past purchase behavior. In one
embodiment, attributes for the clustering model based on
demographics include the customer location, industry type,
headquarter country, public or private, an importer or an exporter,
the number of locations, the number of employees, annual revenue,
etc. In one embodiment, attributes for the clustering model based
on past purchases uses ownership of other products, sold by the
same or a different vendor, as input parameters. As one example,
two customers that purchased Oracle Database, Oracle Collaboration
Suite and Oracle Application server are more likely to end up in
the same cluster than those with that only purchased one common
product.
[0040] In one embodiment, the clustering algorithm used to generate
the models is the K-means algorithm. The K-means algorithm is an
algorithm to cluster n objects based on attributes into k
partitions, k<n. It assumes that the object attributes
(demographic and purchasing pattern attributes) form a vector
space. The objective it tries to achieve is to minimize total
intra-cluster variance. The most common form of the algorithm uses
an iterative refinement heuristic known as Lloyd's algorithm.
Lloyd's algorithm starts by partitioning the input points into k
initial sets, either at random or using some heuristic data. It
then calculates the mean point, or centroid, of each set. It
constructs a new partition by associating each point with the
closest centroid. Then the centroids are recalculated for the new
clusters, and the algorithm is repeated by alternate application of
these two steps until convergence, which is obtained when the
points no longer switch clusters (or alternatively centroids are no
longer changed). In other embodiments, clustering can be performed
using Enhanced K-means (based on distance metric) or O-cluster
(based on density). In one embodiment, the "Data Mining Option" for
the "Oracle Database 11G" from Oracle Corp. can be used to provide
the clustering function.
[0041] In addition to the two clustering models, a global
associations rules model is built across all customers. One
embodiment uses the Apriori data mining algorithm to build
association rules. Apriori is designed to operate on databases
containing transactions (for example, collections of items bought
by customers, or details of a website frequentation). As is common
in association rule mining, given a set of transactions (for
instance, sets of retail transactions, each listing individual
items purchased), the algorithm attempts to find subsets of items
which are common to at least a minimum number "S" (the support
threshold) of the transactions. Apriori uses a "bottom up"
approach, where frequent subsets are extended one item at a time (a
step known as "candidate generation"), and groups of candidates are
tested against the data. The algorithm terminates when no further
successful extensions are found. Apriori uses breadth-first search
and may use a tree structure to count candidate itemsets
efficiently. It generates candidate itemsets of length k from
itemsets of length k-1. It then prunes the candidates which have an
infrequent sub pattern. According to the downward closure lemma,
the candidate set contains all frequent k-length itemsets. Next, it
scans the transaction database to determine frequent itemsets among
the candidates. One of ordinary skill in the art will recognize
that other data mining algorithms instead of Apriori may be used to
build association rules based on attribute data.
[0042] Once the demographic model and the past purchasing pattern
model are built, each customer is assigned to a certain cluster in
each model (330). For example, consider five customers, a
demographic cluster model that includes at least two clusters, and
a purchasing pattern cluster model that includes at least three
clusters. An example cluster assignment is presented in Table 6
below:
TABLE-US-00006 TABLE 6 Demographic Cluster Purchase Cluster
Customer Model Pattern Model Acme Cluster 2 Cluster 2 Bromide
Cluster 1 Cluster 2 Calcium Cluster 2 Cluster 2 Density Cluster 1
Cluster 3 Ether Cluster 1 Cluster 1
[0043] Using the clustering models, a user can query the models to
return a customer reference (340). To initiate a query, the user
can select a sales prospect (i.e., a certain product that has been
recommended to a certain customer) and request a list of potential
references with the best references at the top of the list. For
example, assume a sales representative is considering a
recommendation of a "Business Intelligence" software application to
Customer "Acme" (i.e., the prospective customer). Embodiments uses
cluster membership to determine which customers are most similar to
the prospective customer.
[0044] Based on the cluster assignments of Table 6 above, customer
reference generator 120 calculates a similarity score in reference
to Customer Acme to sort by (350). For example, if Customer
"Calcium" were in the same cluster with Acme in both models, the
similarity score would be "2." Sharing cluster membership with Acme
in only one model would mean a score of "1," and sharing no cluster
memberships with Acme would mean a score of "0." An example
similarity score breakdown for the customers from Table 6 is
presented in Table 7 below:
TABLE-US-00007 TABLE 7 Demographic Purchase Cluster Similarity
Customer Cluster Model Pattern Model Score Acme Cluster 2 Cluster 2
N/A Bromide Cluster 1 Cluster 2 1 Calcium Cluster 2 Cluster 2 2
Density Cluster 1 Cluster 3 0 Ether Cluster 1 Cluster 1 0
[0045] Customer reference generator 120 then presents the user with
an ordered and ranked list of references based on the similarity
score (360). The user can then use this list for customer
references for selling to Acme. An example list is presented in
Table 8 below:
TABLE-US-00008 TABLE 8 Customer Score Calcium 2 Bromide 1 Density 0
Ether 0
[0046] One embodiment uses the hybrid prediction model disclosed
above (e.g., using both a demographic cluster model and past
purchases cluster model) to predict the buying patterns of a new
customer who has not previously purchased any products. System 10
first identifies customers that are similar to the new customer by
matching similar demographic attributes. The past purchasing
patterns of these similar customers are then used as the basis for
product recommendations. In one embodiment, since the new customer
does not own any products, the entire set of product
recommendations for the similar customers are used as recommended
products to the new customer.
[0047] FIG. 4 illustrates an example graphical output user
interface 400 of the customer reference module 120 in a dashboard
view, in accordance with an embodiment. In one embodiment, UI 400
is generated in response to a query for a specific prospect (i.e.,
a combination of a customer and a specific product). A graphical
portion 405, shows attributes such as a projected revenue and
probability of a sales close for a customer 410 (in this example,
"BSI2000INC"); a products list 420 listing recommended products for
a sales call; a purchase history 430 for the particular customer,
including in this example a graph of money spent on products over
the past sixteen quarters; and a references list 440 that lists
customers of similar needs, demographics, and purchasing patterns
that can be used as references for a sales call. The list of
references is in descending order of desirability. Further,
dashboard user interface 400 includes a projected revenue filter
slider 450 for filtering results in graph view 405 based on the
projected revenue of the sale if completed; and a purchase
probability slider 460 for filtering results in graph view 405
based on the probability that the sale will be completed.
Therefore, specific filters when searching for prospects can be
used to display, for example, prospects with an expected revenue
greater than $100,000; which products a specific customer is most
likely to purchase; or which prospects are expected to generate the
most revenue for a specific product.
[0048] One embodiment allows a user to provide feedback for each
reference of list 440 based on, for example, past experience with
that reference. A user can select a feedback icon 442 for a
reference. In response, a "customer referenceability" box will pop
up. FIG. 5 is an example user interface that illustrates a customer
referenceability box 502 that pops up in response to a user
selecting a feedback icon for reference "Purchasesoft". Box 502
allows a user to specify if the account/reference (i.e.,
Purchasesoft) is referenceable and if the product is referenceable
at the account. This information will be available to other users
that generate Purchasesoft as a potential reference in the
future.
[0049] Accordingly, a hybrid prediction model combining demographic
data and purchasing pattern data is disclosed. The model may be
used to understand a prospective customer's demographic, financial
and commercial profile and the customer's buying patterns. Based on
similarity to other customers in demographic and purchasing pattern
data, customer references are generated that can assist in making a
sale to the prospective customer.
[0050] Some embodiments of the invention have been described as
computer-implemented processes. It is important to note, however,
that those skilled in the art will appreciate that the mechanisms
of the invention are capable of being distributed as a program
product in a variety of forms. The foregoing description of example
embodiments is provided for the purpose of illustrating the
principles of the invention, and not in limitation thereof, since
the scope of the invention is defined solely by the appended
claims.
* * * * *