U.S. patent application number 13/104461 was filed with the patent office on 2011-09-22 for method and system for purchase-based segmentation.
This patent application is currently assigned to Citicorp Credit Services, Inc.. Invention is credited to Noor A. Menai, Alan B. Newman, Mark E. TEMARES.
Application Number | 20110231227 13/104461 |
Document ID | / |
Family ID | 34392929 |
Filed Date | 2011-09-22 |
United States Patent
Application |
20110231227 |
Kind Code |
A1 |
TEMARES; Mark E. ; et
al. |
September 22, 2011 |
METHOD AND SYSTEM FOR PURCHASE-BASED SEGMENTATION
Abstract
A method and system for purchased-based segmentation of
potential customers employs the use of actual, observed purchases
instead of presumptions and correlations to improve the accuracy of
segmentation and involves collecting empirical data for a client on
actual purchasing behavior of a group of customers and applying
statistical modeling techniques to the empirical purchasing
behavior data to identify segments or clusters of the customers
that exhibit similar purchasing propensity characteristics.
Thereafter, the segments or clusters are further differentiated
from one another according to other factors having a tendency to
directly affect actual purchasing behavior of the customers within
the segments or clusters, and potential customers are then
identified according to a correlation with the segments or clusters
for customized marketing.
Inventors: |
TEMARES; Mark E.; (Garden
City, NY) ; Newman; Alan B.; (Huntington, NY)
; Menai; Noor A.; (Summit, NJ) |
Assignee: |
Citicorp Credit Services,
Inc.
|
Family ID: |
34392929 |
Appl. No.: |
13/104461 |
Filed: |
May 10, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12010185 |
Jan 22, 2008 |
7966226 |
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13104461 |
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10947589 |
Sep 22, 2004 |
7328169 |
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12010185 |
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60504432 |
Sep 22, 2003 |
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Current U.S.
Class: |
705/7.33 |
Current CPC
Class: |
G06Q 30/0255 20130101;
G06Q 30/0269 20130101; G06Q 30/0631 20130101; G06Q 30/0224
20130101; Y10S 707/99933 20130101; G06Q 30/0204 20130101; G06Q
30/0241 20130101; G06Q 30/0234 20130101; G06Q 30/02 20130101 |
Class at
Publication: |
705/7.33 |
International
Class: |
G06Q 10/00 20060101
G06Q010/00; G06Q 30/00 20060101 G06Q030/00 |
Claims
1. A method for purchased-based segmentation of customers,
comprising: collecting, using a computer having a processor and
memory, empirical data on purchasing behavior of a subset of a
group of customers, the empirical data comprising purchase
information associated with purchase behavior of the group of
customers; analyzing, using the computer, the empirical purchasing
behavior data to identify at least one segment or cluster that
includes the group of customers; analyzing, using the computer, the
empirical purchasing behavior data to identify at least one segment
or cluster for the subset of customers; analyzing, using the
computer, the empirical purchasing behavior data to identify at
least one segment or cluster for the group of customers that does
not include the subset of the group of customers; identifying,
using the computer, characteristics indicative of purchasing
behavior for the segment or cluster for the group of customers;
analyzing, using the computer, the subset of the group of customers
based upon the characteristics of the group of customers; and
identifying, using the computer, potential customers for according
to a correlation with the segments or clusters and the information
generated about the purchasing behavior of the group of customers
that does not include the subset of the group of customers.
2. The method of claim 1, further comprising collecting purchase
information from the use of payment devices.
3. The method of claim 2, wherein at least one payment device is
selected from the group consisting of credit cards, debit cards,
stored value cards, and radio frequency identification devices.
4. The method of claim 1, further comprising collecting purchase
information from the use of credentials issued to the group of
customers.
5. The method of claim 4, wherein at least one credential is
selected from the group consisting of warranty cards, rebate forms,
barcode scans, and proof of purchase data.
6. The method of claim 1, wherein the subset of the group of
customers comprises customers of a particular product or service
provider.
7. The method of claim 1, wherein the group of customers that does
not include the subset of the group of customers comprises
customers of a competitor product or service provider.
8. The method of claim 1, further comprising identifying, using the
computer, common and different characteristics indicative of
purchasing behavior between the subset of the group of customers
and the group of customers that does not include the subset of the
group of customers.
9. The method of claim 1, wherein identifying potential customers
for customized marketing further comprises identifying customers
who correlate with a particular segment or cluster.
10. The method of claim 9, wherein the particular segment or
cluster is the segment or cluster for the subset of the group of
customers.
11. The method of claim 9, wherein the particular segment or
cluster is the segment or cluster the group of customers that does
not include the subset of the group of customers.
12. The method of claim 1, wherein identifying potential customers
comprises indexing.
13. A machine for purchased-based segmentation of customers,
comprising: a computer having a processor and memory, the processor
being programmed for: collecting empirical data on purchasing
behavior of a subset of a group of customers, the empirical data
comprising purchase information associated with purchase behavior
of the group of customers; analyzing the empirical purchasing
behavior data to identify at least one segment or cluster that
includes the group of customers; analyzing the empirical purchasing
behavior data to identify at least one segment or cluster for the
subset of customers; analyzing the empirical purchasing behavior
data to identify at least one segment or cluster for the group of
customers that does not include the subset of the group of
customers; identifying characteristics indicative of purchasing
behavior for the segment or cluster for the group of customers;
analyzing the subset of the group of customers based upon the
characteristics of the group of customers; and identifying
potential customers for according to a correlation with the
segments or clusters and the information generated about the
purchasing behavior of the group of customers that does not include
the subset of the group of customers.
14. The machine of claim 13, further comprising collecting purchase
information from the use of payment devices.
15. The machine of claim 14, wherein at least one payment device is
selected from the group consisting of credit cards, debit cards,
stored value cards, and radio frequency identification devices.
16. The machine of claim 13, further comprising collecting purchase
information from the use of credentials issued to the group of
customers.
17. The machine of claim 16, wherein at least one credential is
selected from the group consisting of warranty cards, rebate forms,
barcode scans, and proof of purchase data.
18. The machine of claim 13, wherein the subset of the group of
customers comprises customers of a particular product or service
provider.
19. The machine of claim 13, wherein the group of customers that
does not include the subset of the group of customers comprises
customers of a competitor product or service provider.
20. The machine of claim 13, further comprising identifying common
and different characteristics indicative of purchasing behavior
between the subset of the group of customers and the group of
customers that does not include the subset of the group of
customers.
21. The machine of claim 13, wherein identifying potential
customers for customized marketing further comprises identifying
customers who correlate with a particular segment or cluster.
22. The machine of claim 21, wherein the particular segment or
cluster is the segment or cluster for the subset of the group of
customers.
23. The machine of claim 21, wherein the particular segment or
cluster is the segment or cluster the group of customers that does
not include the subset of the group of customers.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of pending application
Ser. No. 12/010,185, filed Jan. 22, 2008, entitled "METHOD AND
SYSTEM FOR PURCHASE-BASED SEGMENTATION," which is a continuation
application of U.S. application Ser. No. 10/947,589 filed Sep. 22,
2004, entitled "METHOD AND SYSTEM FOR PURCHASE-BASED SEGMENTATION,"
which claims the benefit of U.S. Provisional Application No.
60/504,432 filed Sep. 22, 2003, entitled "METHOD AND SYSTEM FOR
PURCHASE-BASED TARGETING" and incorporated herein by this
reference. Each and every document, including patents and
publications, cited herein is incorporated in its entirety as
though recited in full herein.
FIELD OF THE INVENTION
[0002] The present invention relates to methods and systems
associated with purchase-based segmentation and clustering used in
commercial transactions, and more particularly to methods and
systems for implementing purchase-based segmentation and clustering
programs which can be used to improve the success of various
commercial efforts. These may include direct marketing (e.g.,
direct-to-customer advertising, direct mail, couponing); creating
marketing-related services for retailers, product/service providers
and others based on actual purchase behavior of identified
customers and similar customers; providing a means of understanding
the actual behavior of a retailer or product/service provider's
customers versus the behavior of those same customers with
competitors, and versus the behavior of competitors' other
customers; merchandise planning; real estate planning; and other
applications.
BACKGROUND OF THE INVENTION
[0003] A key essence and aim of segmentation and clustering is to
maximize the return on marketing investments by directing marketing
efforts towards those more likely to respond favorably, and
reducing marketing efforts to those less likely to respond
favorably.
[0004] To maximize the effectiveness of segmentation and clustering
methodologies, the marketer must have a means of differentiating
those more likely to respond. Many techniques exist for
differentiation, including techniques related to geographic factors
(e.g. determine those living near existing customers), demographic
factors (e.g. determine those with high incomes, or those with
children in the household), and psychographic/lifestyle factors
(e.g. determine those who have active lifestyles, those who engage
in crafting, or those who attend church regularly).
[0005] Once the differentiating factors are identified, the
marketer's next challenge is to determine, given the communication
medium selected, how best to reach the desired prospects with a
minimum of waste. This can include purchasing advertising time on
certain television stations or during certain programs watched
disproportionately by the desired prospects, purchasing mailing
lists of subscribers to magazines that serve the desired customers'
interests, concentrating advertising in local newspapers in
selected geographies, and many other means. It could also include
adjusting the positioning/messaging of the product or service being
marketed to align better with the behaviors and attitudes expressed
by the desired prospects, selecting merchandise and store locations
more likely to appeal to the desired prospects, and other
applications.
[0006] Among the methods available to marketers to identify likely
purchasers are those methods known as "clustering". These methods
assign households or individuals to one of a number of discrete
segments or clusters based on a statistical "best fit" methodology
that takes into account a number of the factors above.
[0007] In all of the above methods described, any success achieved
by the marketer is a function of presumptions and correlations. For
example, a sporting goods marketer may achieve better results by
mailing to subscribers of Sports Illustrated than by mailing to
subscribers of Time, because readers of Sports Illustrated are more
likely to participate in sports (that is, there may be a better
correlation between reading Sports Illustrated and sports
participation than there is with reading Time). However, results
are relative, and the actual response may be small. Many readers of
Sports Illustrated are spectators, not participants. Many others
are participants, but not necessarily users of the sorts of
products sold by the sporting goods marketer.
SUMMARY OF THE INVENTION
[0008] It is a feature and advantage of the present invention to
provide a methodology and system for purchased-based segmentation
of potential customers which employs the use of actual, observed
transactions, rather than presumptions and correlations, to improve
the accuracy of segmenting and reaching prospects.
[0009] It is another feature and advantage of the present invention
to provide a methodology and system for purchased-based
segmentation of potential customers which employs the use of
actual, observed purchases to improve the accuracy of segmentation
when the purchasers are known to the provider as a means of
facilitating marketing to the actual purchasers of a product or
category.
[0010] It is an additional feature and advantage of the present
invention to provide a methodology and system for purchased-based
segmentation of potential customers which employs the use of
actual, observed purchases to improve the accuracy of segmentation
when the specific purchasers or their transactions are not known to
the provider, or when key data about them are not available, as a
means of creating more accurate and effective marketing via
correlation with other behaviors and by enhancing existing
methodologies.
[0011] It still another feature and advantage of the present
invention to provide a methodology and system for purchased-based
segmentation of potential customers that provides insights into the
actual observed behavior of segmented customers at competitors and
into the actual observed behavior of competitors' customers.
[0012] To achieve the stated and other features, advantages and
objects, embodiments of the present invention utilize, for example,
computer hardware, operating systems, programming languages,
software applications, and other technology to provide methods and
systems for purchased-based segmentation of customers in which
empirical data is collected by a service provider for a client on
actual purchasing behavior of a group of customers. The data can be
collected, for example, directly or indirectly as a byproduct of
use of payment devices, such as credit cards, debit cards, stored
value cards, and/or radio frequency identification devices,
provided to the customers by the client or a third party.
Alternatively, the data can be collected, for example, directly or
indirectly as a byproduct of use of benefit credentials, such as
warranty cards, rebate forms, barcode scans, and proof of purchase
data, provided to the customers by the client or a third party.
[0013] In an embodiment of the invention, statistical modeling
techniques are applied to the empirical purchasing behavior data to
identify segments or clusters of the customers that exhibit similar
purchasing propensity characteristics in terms, for example, of
likelihood of future purchases from the client and/or from a third
party. The segments or clusters are further differentiated from one
another according to other factors, such as geodemographic and
psychographic/lifestyle factors, having a tendency to directly
affect actual purchasing behavior of the customers within the
segments or clusters. Based on a correlation with the segments or
clusters, for example, via indexing, potential customers, who may
be customers of the client and/or customers of third parties, can
then be identified for customized marketing.
[0014] Additional objects, advantages and novel features of the
invention will be set forth in part in the description which
follows, and in part will become more apparent to those skilled in
the art upon examination of the following, or may be learned from
practice of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 is a schematic diagram that illustrates an example of
key components and relationships between key components of the
process of purchased-based segmentation of potential customers
utilizing data regarding actual, observed purchases and statistical
modeling and clustering techniques for embodiments of the
invention.
[0016] FIG. 2 is flow diagram that illustrates an example of the
process of purchased-based segmentation of potential customers
utilizing data regarding actual, observed purchases and statistical
modeling and clustering techniques for embodiments of the
invention.
DETAILED DESCRIPTION
[0017] Referring now in detail to embodiments of the present
invention, examples of which are illustrated in the accompanying
drawings, each example is provided by way of explanation of the
invention, not as a limitation of the invention. It will be
apparent to those skilled in the art that various modifications and
variations can be made in the present invention without departing
from the scope or spirit of the invention. For instance, features
illustrated or described as part of one embodiment can be used on
another embodiment to yield a still further embodiment. Thus, it is
intended that the present invention cover such modifications and
variations that come within the scope of the invention.
[0018] In preferred embodiments, the invention uses various methods
to collect data about the actual purchases of individuals,
households, and/or businesses ("prospects"); uses statistical
modeling techniques to create segments or clusters based on the
actual purchases, provides the information on how, or the actual
means, to reach these prospects when the actual prospects are known
to the user of the invention (e.g. customers of the user, or names
and addresses acquired through third parties); and uses actual
purchases of others to enhance methodologies that provide
predictions of segment/cluster membership or specific behavior
("index values" and/or scores) for prospects for whom actual
purchase information is not available.
[0019] In preferred embodiments, the invention can use similar data
collection and segmentation methods, and then provides information
and insights on how actual purchases of customers of a specific
client (e.g. a retailer or product/service provider) compare to
purchases by those same customers at competitors or at
non-competitors, how the behavior of that client's customers may or
may not differ from the behavior of non-customers, and/or how the
customers themselves may differ.
[0020] Methods of data acquisition include those methods through
which data is acquired directly by the user, and those which
collect data from others who possess it. Among the former are:
issuance of payment devices (e.g. credit and debit cards, RFID
devices, etc.), where purchase data can be a byproduct of
facilitating the purchase transaction; issuance of a "benefit
credential" (e.g. a loyalty program or "frequent shopper"
identifier), where, again, data collection is a byproduct of
facilitating other customer benefits; or by collecting information
directly from the actual customers themselves through various means
(e.g. warranty cards, rebate forms requiring proof of purchase,
scanning of barcodes received in the house, premiums and prizes
requiring proof of purchase or collection of multiple proofs,
etc.).
[0021] Among the latter are collection of customer and/or purchase
information directly or indirectly from other issuers of payment
devices or benefit credentials, collection of customer and/or
purchase information directly or indirectly from one or more
merchants, and collection of customer and/or purchase information
from other third party sources who use various means to obtain that
information.
[0022] In embodiments of the present invention, for each retailer
or product/service provider who is interested in obtaining better
information, or for categories of retailers or products/services,
statistical modeling and/or clustering methods are used to create
segments or clusters of relatively more likely and relatively less
likely purchasers, with varying degrees in between. Key statistical
drivers of the segments or clusters will be actual purchases at the
specific retailer or of the product/service providers' products,
and similar purchases in related stores or categories. Other
factors which appear to drive the actual purchases will also be
used to create further differentiation among , and maximum
homogeneity within, segments or clusters, including purchases at
other stores or categories , methods of purchase, and
geodemographic and psychographic/lifestyle factors (e.g., a heavy
shopper in the category who does not live near any outlets of a
retailer should be significantly less likely to shop at that
retailer, even though actual purchases in the category would, on
their own, indicate otherwise).
[0023] The segments or clusters, which may be expressed as names,
ordinal numbers, indices, or statistically-based scores, can then
be used by retailers or product/service providers in various ways,
including, in embodiments of the present invention: by choosing
mass media more likely to reach individuals/households in the
highest-potential segments or clusters, by observing and
understanding differences in their own customers' behaviors and
competitive customers' behaviors from segment or cluster to segment
or cluster and adjusting offers, products, product
mixes/merchandising, store formats and locations, and other means
of doing business and attracting and serving customers
accordingly.
[0024] In embodiments of the present invention, the user or its
retailer and product/service provider clients may also combine the
information created from segments or clusters with its own and
other data to market to known individuals/households in the highest
potential segments or clusters, and to identify other individuals
likely to be in the highest potential segments or clusters, but for
whom specific purchase information is not known to the user. This
can be performed through the techniques of indexing and/or scoring.
Embodiments of this invention include indexing to specific
individual factors or sets of factors (e.g. customers in a specific
high potential segment or cluster have an index of 250 for home
ownership, that is, they are 2.5 times as likely to own a home than
average. This could indicate that targeting homeowners would be an
efficient means of reaching unknown potential customers), or
indexing to existing clustering methodologies using other,
non-purchase-driven clustering techniques (e.g. customers in a
specific high potential segment or cluster have an index of 600 in
another clustering methodology's "cluster twelve").
[0025] The advantage to marketers of indexing to other factors or
clusters is that, for certain means of marketing, the relationship
of the marketing channel to the other factors or clusters is
already established and known. For example, there are not, at
present, indices of television viewers by show for the segments or
clusters created in the embodiments of the invention described
here, nor, as many of the segments or clusters created will be
custom for particular clients, are there likely to be. However,
indices of television viewers by show are widely available for many
specific geodemographic and psychographic/lifestyle variables, and
for at least two clustering methodologies provided by commercial
companies (Personicx.RTM. and PRIZM.RTM.). By providing an index or
similar overlay measure to existing factors or clusters, then,
marketers can use these known factors and clusters to select media.
Using the numbers in the example above, marketers would seek to
advertise on television shows disproportionately appealing to
homeowners (for example, home improvement shows), or to those in
the other clustering methodology's cluster twelve.
[0026] In embodiments of this invention related to understanding
customers of competitors or non-competitors versus their own
customers, retailers and product/service providers can use segments
or clusters in several ways. When segments or clusters are created
for an overall category, retailers and product/service providers
can compare the relative presence of their and others' customers in
each segment or cluster, and use the characteristics of the segment
or cluster to generate insights about the nature of their own and
competitors' or non-competitors' clients. When segments or clusters
are created separately for the customers of the retailer or
product/service provider and for customers of the competitors or
non-competitors, the characteristics of overlapping and
non-overlapping segments or clusters can be identified and
compared. In either case, as with all of the above, the ability of
the segmentation or clustering in embodiments of the present
invention to be based on actual purchase behavior or actual
purchase transactions provides a significant advantage in improving
the results of marketing and research efforts, leading to increased
efficiency in marketing, sales, and other business functions.
[0027] FIG. 1 is a schematic diagram that illustrates an example of
key components and relationships between key components of the
process of purchased-based segmentation of potential customers
utilizing data regarding actual, observed purchases and statistical
modeling and clustering techniques for embodiments of the
invention. Referring to FIG. 1, there is a large data warehouse
(10) of purchases. Using those purchases, if, for example, there is
an interest in looking for customers who had a high likelihood of
eating at casual dining restaurants, clusters of purchasers who
show casual dining restaurants behavior can be developed. Some of
those clusters might be, as examples, customers who go to casual
dining restaurants during the week but not on weekends; customers
who go on weekends but not during the week; customers who go very
frequently; people who go infrequently; non-customers (those who
never go); customers who go to casual dining restaurants and also
to white-tablecloth restaurants; customers who go to casual dining
restaurants and quick service restaurants but not to
white-tablecloth; and so on. The characteristics of both restaurant
purchase behavior and non-restaurant purchase behavior that creates
the greater differentiation among segments for various types of
purchasing behavior (restaurants 12, car rentals 14, department
stores 16, clothing, etc.) are identified. It should be understood
that the differentiating behaviors could be within or outside the
category.
[0028] Several things can be done with the segments or clusters.
Marketing to the people in those specific segments or clusters can
be facilitated and customized either by a particular restaurant, by
a category, or more broadly. Marketing to the customer base can
therefore be facilitated. In the present embodiment, the customer
base refers to the base of people for whom there is knowledge.
[0029] Additionally, "birds of a feather" can be determined, that
is, potential customers who appear to be just like the customers in
the desired segments or clusters but are not exhibiting the
behaviors. These may be potential customers who are in the customer
base but who make their purchases via methods which cannot be
observed in the data, or may be potential customers who are not in
the customer base. Furthermore, the segments or clusters that are
developed can be compared to existing known segmentation or
clustering schemes and incidences of high overlap may be determined
in particular to other segmentation or clustering schemes.
[0030] In the embodiment shown in FIG. 1, in the restaurant cluster
12 and Personicx cluster "one" (18), the person in restaurant
cluster "eight" (20) is 3.3 times as likely to occur in Personicx
cluster "one" (18) and only about 60 percent as likely to occur in
Personicx cluster "six" (22). The advantage of knowing that is that
certain clustering schemes have already been mapped to external
sources. For example, for almost every magazine, the readership has
already been indexed to Personicx clusters. Therefore, media buyers
already know in which magazines to advertise to reach prospects in
the desired Personicx clusters. Further, in reference to restaurant
cluster "eight" (20), indexing can be done directly between all the
media and this cluster, or instead, as illustrated, the restaurant
cluster determined in the illustrated embodiment can be mapped over
to other existing customer schemes, e.g., Personicx. This cluster
can similarly be correlated with geographic or demographic
information to identify prospective customers.
[0031] FIG. 2 is flow diagram that illustrates an example of the
process of purchased-based segmentation of potential customers
utilizing data regarding actual, observed purchases and statistical
modeling and clustering techniques for embodiments of the
invention. Referring to FIG. 2, at S1, a service provider collects
empirical data for a client on actual purchasing behavior of a
group of customers. At S2, statistical modeling techniques are
applied to the empirical purchasing behavior data collected by the
service provider in order to identify clusters of the customers
that exhibit similar purchasing propensity characteristics. At S3,
the clusters are further differentiated according to other factors
that have a tendency to directly affect actual purchasing behavior
of the customers within the clusters, and at S4, potential
customers for customized marketing are identified according to a
correlation with the clusters.
[0032] Although some embodiments use credit card purchase data,
there are many other sources of data that can be used to create the
segments or clusters. For example, debit card data or data using
the merchants' benefit credentials; or by purchasing from others
that have the data or allying with others that have it on a
partnership basis to obtain the data they have. For example, credit
card issuers have data; ACNielsen has data that is obtained
directly from stores; issuers of ID devices and other credentials
have data; issuers of debit cards have data; and the stores
themselves have data.
[0033] Embodiments of the present invention have now been generally
described in a non-limiting manner. It will be appreciated that
these examples are merely illustrative of the present invention.
Many variations and modifications will be apparent to those of
ordinary skill in the art.
* * * * *