U.S. patent application number 13/689443 was filed with the patent office on 2013-12-19 for system, method, software and data structure for independent prediction of attitudinal and message responsiveness, and preferences for communication media, channel, timing, frequency, and sequences of communications, using an integrated data repository.
This patent application is currently assigned to EXPERIAN MARKETING SOLUTIONS, INC.. The applicant listed for this patent is Experian Marketing Solutions, Inc.. Invention is credited to Marc Christian Fanelli, Patricia Kay Gormley, Kymberly Ann Kulle, Thomas G. Nocerino, Kaushik Sanyal.
Application Number | 20130339087 13/689443 |
Document ID | / |
Family ID | 35515147 |
Filed Date | 2013-12-19 |
United States Patent
Application |
20130339087 |
Kind Code |
A1 |
Fanelli; Marc Christian ; et
al. |
December 19, 2013 |
System, Method, Software and Data Structure for Independent
Prediction of Attitudinal and Message Responsiveness, and
Preferences For Communication Media, Channel, Timing, Frequency,
and Sequences of Communications, Using an Integrated Data
Repository
Abstract
The present invention provides a system, method, software and
data structure for independently predicting attitudinal and message
responsiveness, using a plurality of attitudinal or other
identification classifications and a plurality of message content
or version classifications, for a selected population of a
plurality of entities, such as individuals or households,
represented in a data repository. The plurality of predictive
attitudinal (or identification) classifications and plurality of
predictive message content (ore version) classifications have been
determined using a plurality of predictive models developed from a
sample population and applied to a reference population represented
in the data repository, such as attitudinal, behavioral, or
demographic models. For each predictive attitudinal (or
identification) classification, at least one predominant predictive
message content or version classification is independently
determined. The exemplary embodiments also provide, for each
predictive attitudinal classification, corresponding information
concerning predominant communication media (or channel) types,
predominant communication timing, predominant communication
frequency, and predominant communication sequencing.
Inventors: |
Fanelli; Marc Christian;
(Kinnelon, NJ) ; Gormley; Patricia Kay; (Lincoln,
NE) ; Kulle; Kymberly Ann; (Cincinnati, OH) ;
Nocerino; Thomas G.; (Bethlehem, PA) ; Sanyal;
Kaushik; (Jersey City, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Experian Marketing Solutions, Inc.; |
|
|
US |
|
|
Assignee: |
EXPERIAN MARKETING SOLUTIONS,
INC.
Schaumburg
IL
|
Family ID: |
35515147 |
Appl. No.: |
13/689443 |
Filed: |
November 29, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10881436 |
Jun 30, 2004 |
8346593 |
|
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13689443 |
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Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
G06Q 30/0204 20130101;
G06Q 30/02 20130101; G06Q 30/0203 20130101; G06Q 30/0202
20130101 |
Class at
Publication: |
705/7.31 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method of determining a plurality of predictive message
content classifications and a plurality of predictive attitudinal
classifications for a reference population represented in a data
repository, the method comprising: developing a plurality of
empirical attitudinal factors based on a factor analysis of an
attitudinal survey of a sample population; using each empirical
attitudinal factor of the plurality of empirical attitudinal
factors, scoring each participant of the attitudinal survey to
create a corresponding plurality of empirical attitudinal factor
scores; using a plurality of selected variables from the data
repository as independent variables, and using the corresponding
plurality of empirical attitudinal factor scores as dependent
variables, performing a regression analysis to create a plurality
of predictive attitudinal models; using each predictive attitudinal
model of the plurality of predictive attitudinal models, scoring a
plurality of entities forming the reference population represented
in the data repository, to create the plurality of predictive
message content classifications; and performing a cluster analysis
of the plurality of predictive message content classifications of
each entity of the plurality of entities forming the reference
population represented in the data repository to create the
plurality of predictive attitudinal classifications.
2. The method of claim 1, further comprising: validating the
plurality of predictive attitudinal models using a subset of the
sample population and corresponding results of the attitudinal
survey.
3. The method of claim 1, wherein the cluster analysis includes
determining a plurality of combinations of membership and
non-membership of the plurality of entities in each of the
plurality of predictive message content classifications.
4. The method of claim 1, wherein the cluster analysis includes
determining a plurality of combinations of probabilities of
membership of the plurality of entities in each of the plurality of
predictive message content classifications.
5. The method of claim 1, wherein the cluster analysis includes
determining a plurality of combinations of probabilities of the
plurality of entities exhibiting an attitude represented by each of
the plurality of predictive message content classifications.
6. The method of claim 1, wherein the regression analysis is a
logistic regression analysis.
7. The method of claim 1, further comprising: independently
determining a subset of the plurality of predictive attitudinal
classifications and a subset of the plurality of predictive message
content classifications for a selected population of a plurality of
entities represented in a data repository.
8. The method of claim 7, wherein the independent determination of
the subset of the plurality of predictive attitudinal
classifications and the subset of the plurality of predictive
message content classifications further comprises: for each entity
of the plurality of entities of the selected population, appending
from the data repository a corresponding predictive attitudinal
classification of the plurality of predictive attitudinal
classifications and a corresponding plurality of memberships in the
plurality of predictive message content classifications; for each
predictive attitudinal classification of the plurality of
predictive attitudinal classifications, determining a penetration
index of the selected population compared to the reference
population; and for each predictive attitudinal classification of
the plurality of predictive attitudinal classifications,
independently determining at least one predominant predictive
message content classification from the appended corresponding
plurality of memberships in the plurality of predictive message
content classifications of the plurality of entities of the
selected population having the corresponding predictive attitudinal
classification of the plurality of predictive attitudinal
classifications.
9. The method of claim 8, further comprising: for each entity of
the plurality of entities of the selected population, appending
from the data repository at least one corresponding predictive
communication media classification of a plurality of predictive
communication media classifications, the corresponding predictive
communication media classification having been determined from
information stored in the data repository; and for each predictive
attitudinal classification of the plurality of predictive
attitudinal classifications, independently determining at least one
predominant predictive communication media classification from the
appended plurality of predictive communication media
classifications of the plurality of individuals of the selected
population having the corresponding predictive attitudinal
classification of the plurality of predictive attitudinal
classifications.
10. The method of claim 9, wherein the plurality of predictive
communication media classifications comprises at least two of the
following: electronic mail (email), direct mail, telecommunication,
radio, television, internet, satellite, cable media, video media,
digital versatile disk media, compact disk media, print media, and
visual display media.
11. The method of claim 8, further comprising: for each entity of
the plurality of entities of the selected population, appending
from the data repository at least one corresponding predictive
communication timing classification of a plurality of predictive
communication timing classifications, the corresponding predictive
communication timing classification having been determined from
information stored in the data repository; and for each predictive
attitudinal classification of the plurality of predictive
attitudinal classifications, independently determining at least one
predominant predictive communication timing classification from the
appended plurality of predictive communication timing
classifications of the plurality of entities of the selected
population having the corresponding predictive attitudinal
classification of the plurality of predictive attitudinal
classifications.
12. The method of claim 11, wherein the plurality of predictive
communication timing classifications comprises at least two of the
following communication timing classifications: any time, morning,
afternoon, evening, night, weekday, and weekend.
13. The method of claim 8, further comprising: for each entity of
the plurality of entities of the selected population, appending
from the data repository at least one corresponding predictive
communication frequency classification of a plurality of predictive
communication frequency classifications, the corresponding
predictive communication frequency classification having been
determined from information stored in the data repository; and for
each predictive attitudinal classification of the plurality of
predictive attitudinal classifications, independently determining
at least one predominant predictive communication frequency
classification from the appended plurality of predictive
communication frequency classifications of the plurality of
entities of the selected population having the corresponding
predictive attitudinal classification of the plurality of
predictive attitudinal classifications.
14. The method of claim 13, wherein the plurality of predictive
communication frequency classifications comprises at least two of
the following frequency classifications: unlimited, none, daily,
weekly, biweekly, monthly, semi-monthly, bimonthly, annually, and
semi-annually.
15. The method of claim 8, further comprising: determining at least
one core attitudinal classifications by selecting, from the
plurality of predictive attitudinal classifications, at least one
predictive attitudinal classification having a comparatively
greater penetration index and having a comparatively greater
proportion of the reference population; determining at least one
niche attitudinal classifications by selecting, from the plurality
of predictive attitudinal classifications, at least one predictive
attitudinal classification having a comparatively greater
penetration index and having a comparatively lesser proportion of
the reference population; and determining at least one growth
attitudinal classifications by selecting, from the plurality of
predictive attitudinal classifications, at least one predictive
attitudinal classification having a comparatively lesser
penetration index and having a comparatively greater proportion of
the reference population.
16. The method of claim 8, wherein the independent determination of
at least one predominant predictive message content classification
further comprises: for each predictive attitudinal classification,
determining all of the appended corresponding plurality of
memberships in the plurality of predictive message content
classifications of the plurality of entities of the selected
population having the corresponding predictive attitudinal
classification; and for each predictive attitudinal classification,
selecting at least one predictive message content classifications,
of the plurality of predictive message content classifications,
corresponding to a comparatively greater number of entities of the
selected population.
17. A system for determining a plurality of predictive message
content classifications and a plurality of predictive attitudinal
classifications for a reference population, the system comprising:
a data repository storing a plurality of selected variables for
each entity of a plurality of entities forming a sample population
and forming a reference population; and a processor coupled to the
data repository, the processor configured to perform a factor
analysis of an attitudinal survey of the sample population to
determine a plurality of empirical attitudinal factors; to score
each participant of the attitudinal survey, using each empirical
attitudinal factor of the plurality of empirical attitudinal
factors, to create a corresponding plurality of empirical
attitudinal factor scores; to perform a regression analysis using
the plurality of selected variables from the data repository as
independent variables, and using the corresponding plurality of
empirical attitudinal factor scores as dependent variables, to
create a plurality of predictive attitudinal models; to score the
plurality of entities forming the reference population using each
predictive attitudinal model of the plurality of predictive
attitudinal models, to create the plurality of predictive message
content classifications; and to perform a cluster analysis of the
plurality of predictive message content classifications of each
entity of the plurality of entities forming the reference
population, to create the plurality of predictive attitudinal
classifications.
18. The system of claim 17, wherein the processor is further
configured to validate the plurality of predictive attitudinal
models using a subset of the sample population and corresponding
results of the attitudinal survey.
19. The system of claim 17, wherein the processor is further
configured to perform the cluster analysis by determining a
plurality of combinations of membership and non-membership of the
plurality of entities in each of the plurality of predictive
message content classifications.
20. The system of claim 17, wherein the processor is further
configured to perform the cluster analysis by determining a
plurality of combinations of probabilities of membership of the
plurality of entities in each of the plurality of predictive
message content classifications.
21. The system of claim 17, wherein the processor is further
configured to perform the cluster analysis by determining a
plurality of combinations of probabilities of the plurality of
entities exhibiting an attitude represented by each of the
plurality of predictive message content classifications.
22. The system of claim 17, wherein the processor is further
configured to perform the regression analysis as a logistic
regression analysis.
23. The system of claim 17, wherein the processor is further
configured to store, in the data repository, for each entity of the
plurality of entities of the reference population, a corresponding
predictive attitudinal classification of the plurality of
predictive attitudinal classifications and a corresponding
plurality of memberships in the plurality of predictive message
content classifications determined from a corresponding plurality
of scores from the plurality of predictive attitudinal models.
24. The system of claim 23, wherein the processor is further
configured to independently determine a subset of the plurality of
predictive attitudinal classifications and a subset of the
plurality of predictive message content classifications for the
selected population.
25. The system of claim 58, wherein the processor is further
configured to append from the data repository, for each entity of
the plurality of entities of the selected population, the
corresponding predictive attitudinal classification of the
plurality of predictive attitudinal classifications and the
corresponding plurality of memberships in the plurality of
predictive message content classifications; for each predictive
attitudinal classification of the plurality of predictive
attitudinal classifications, to determine a penetration index of
the selected population compared to the reference population; and
for each predictive attitudinal classification of the plurality of
predictive attitudinal classifications, to independently determine
at least one predominant predictive message content classification
from the appended corresponding plurality of memberships in the
plurality of predictive message content classifications of the
plurality of entities of the selected population having the
corresponding predictive attitudinal classification of the
plurality of predictive attitudinal classifications.
26. The system of claim 25, wherein the processor is further
configured to append from the data repository, for each entity of
the plurality of entities of the selected population, at least one
corresponding predictive communication media classification of a
plurality of predictive communication media classifications, the
corresponding predictive communication media classification having
been determined by the processor from information stored in the
data repository; and for each predictive attitudinal classification
of the plurality of predictive attitudinal classifications, to
independently determine at least one predominant predictive
communication media classification from the appended plurality of
predictive communication media classifications of the plurality of
individuals of the selected population having the corresponding
predictive attitudinal classification of the plurality of
predictive attitudinal classifications.
27. The system of claim 26, wherein the plurality of predictive
communication media classifications comprises at least two of the
following: electronic mail (email), direct mail, telecommunication,
radio, television, internet, video media, digital versatile disk
media, print media, and visual display media.
28. The system of claim 25, wherein the processor is further
configured to append from the data repository, for each entity of
the plurality of entities of the selected population, at least one
corresponding predictive communication timing classification of a
plurality of predictive communication timing classifications, the
corresponding predictive communication timing classification having
been determined from information stored in the data repository; and
for each predictive attitudinal classification of the plurality of
predictive attitudinal classifications, to independently determine
at least one predominant predictive communication timing
classification from the appended plurality of predictive
communication timing classifications of the plurality of entities
of the selected population having the corresponding predictive
attitudinal classification of the plurality of predictive
attitudinal classifications.
29. The system of claim 28, wherein the plurality of predictive
communication timing classifications comprises at least two of the
following communication timing classifications: any time, morning,
afternoon, evening, night, weekday, and weekend.
30. The system of claim 25, wherein the processor is further
configured to append from the data repository, for each entity of
the plurality of entities of the selected population, at least one
corresponding predictive communication frequency classification of
a plurality of predictive communication frequency classifications,
the corresponding predictive communication frequency classification
having been determined by the processor from information stored in
the data repository; and for each predictive attitudinal
classification of the plurality of predictive attitudinal
classifications, to independently determine at least one
predominant predictive communication frequency classification from
the appended plurality of predictive communication frequency
classifications of the plurality of entities of the selected
population having the corresponding predictive attitudinal
classification of the plurality of predictive attitudinal
classifications.
31. The system of claim 30, wherein the plurality of predictive
communication frequency classifications comprises at least two of
the following frequency classifications: unlimited, none, daily,
weekly, biweekly, monthly, semi-monthly, bimonthly, annually, and
semi-annually.
32. The system of claim 25, wherein the processor is further
configured to determine at least one core attitudinal
classifications by selecting, from the plurality of predictive
attitudinal classifications, at least one predictive attitudinal
classification having a comparatively greater penetration index and
having a comparatively greater proportion of the reference
population; to determine at least one niche attitudinal
classifications by selecting, from the plurality of predictive
attitudinal classifications, at least one predictive attitudinal
classification having a comparatively greater penetration index and
having a comparatively lesser proportion of the reference
population; and to determine at least one growth attitudinal
classifications by selecting, from the plurality of predictive
attitudinal classifications, at least one predictive attitudinal
classification having a comparatively lesser penetration index and
having a comparatively greater proportion of the reference
population.
33. The system of claim 25, wherein the processor is further
configured to independently determine the at least one predominant
predictive message content classification by determining, for each
predictive attitudinal classification, all of the appended
corresponding plurality of memberships in the plurality of
predictive message content classifications of the plurality of
entities of the selected population having the corresponding
predictive attitudinal classification; and by selecting, for each
predictive attitudinal classification, at least one predictive
message content classifications, of the plurality of predictive
message content classifications, corresponding to a comparatively
greater number of entities of the selected population.
Description
CROSS-REFERENCE TO A RELATED APPLICATION
[0001] This application is a divisional of and claims priority to
co-pending U.S. patent application Ser. No. 10/881,436, filed Jun.
30, 2004, entitled "System, Method and Software for Prediction of
Attitudinal and Message Responsiveness", inventors Marc Christian
Fanelli et al., commonly assigned herewith, the entire contents of
which are incorporated by reference herein with the same full force
and effect as if set forth in their entirety herein, with priority
claimed for all commonly disclosed subject matter.
FIELD OF THE INVENTION
[0002] The present invention relates, in general, to database
management systems and, more particularly, to a system, method and
software for independently predicting attitudinal and message
responsiveness, and preferences for communication media, channel,
timing, frequency, and sequences of communications, using an
integrated data repository.
BACKGROUND OF THE INVENTION
[0003] Business and consumer records are typically contained in
databases and other forms of data repositories. Typical databases
may contain records such as demographic data, customer data,
marketing data, name and address information, observed and
self-reported lifestyle and other behavioral data, consumer data,
public record information, realty and property tax information,
summarized automotive statistics, summarized financial data, census
data, and so on. Virtually any type of information may be contained
in any such database. One such highly inclusive database,
containing much of the above-mentioned types of data for
approximately 98% of U.S. individuals and living units
(households), is the Experian INSOURCE.RTM. database.
[0004] Various database applications have been directed to attempts
to utilize the wide array of information contained in such
databases for marketing and analytical purposes. For example,
demographic data may be appended to customer records, to identify
the demographic composition of a set of customers, followed by
marketing directed toward people having similar demographic
characteristics.
[0005] These database applications, in their various forms, attempt
to understand and access distinct customer and prospect groups, and
then send the right message to the right individual, household,
living unit or other target audience. Typically, all of the
individuals and/or households contained in the corresponding
database are segmented into groups which share distinct
demographic, lifestyle, and consumer behavior characteristics. In
other applications, following such segmentation, consumer attitudes
and motivations are assumed and attributed to those
individuals/households within each such segment or cluster. The
number of segments utilized varies widely by application.
[0006] In addition, in these various database marketing
applications, consumer attitudes and preferred marketing message
themes or types are generally assumed and assigned to a segment,
without any independent empirical research and analysis. As a
consequence, once a population is segmented, any further analysis
of the population based on preferred messaging themes does not, in
fact, add any additional, independent information, and merely
reiterates the underlying message theme assumptions of any given
segment.
[0007] The resulting data, moreover, may have a large degree of
uncertainty, may or may not be accurate, and may or may not be
actionable. For example, the attitudes, motivations and behaviors
attributed or assigned to each segment may not be accurate and may
not be based on factual, empirical research. Such attitudes,
motivations and behaviors may or may not actually reflect
representative attitudes found in a particular customer
database.
[0008] The diminished accuracy of current marketing methods is
further underscored by comparatively low response rates, such as
1-2% response from a target audience for direct mail marketing.
Other methods and systems are required to appropriately target and
motivate the remainder of the target audience, and to determine
potentially new and underdeveloped target audiences. In addition,
new methods and systems are required to maximize marketing returns,
by not overly saturating the target audience with excessive and
ineffective communications, and instead to appropriately
communicate with the target audience using the audience's preferred
methods and times of communication.
[0009] As a consequence, a need remains for a predictive
methodology and system, for accurate prediction of attitudes,
motivations and behaviors, which may be utilized for marketing
applications. Such a method and system should be empirically-based,
such as based on actual attitudinal, behavioral or demographic
research and other information from a population sample, and
further should provide accurate modeling to predict and extrapolate
such attitudinal or other information to a larger or entire
population. Such a method and system should provide information
concerning preferred message themes or message content
independently from any population grouping, segmentation or
clustering process. In addition, such a method and system should be
actionable, providing not only audience attitudinal information and
preferred message content, but also preferred communication channel
information or other preferred communication media, preferred
frequency of communication or other contact, and communication
timing information.
SUMMARY OF THE INVENTION
[0010] The present invention provides a system, method and software
for independently predicting a plurality of first, message content
classifications and a plurality of second, attitudinal
classifications, for a selected population of individuals,
households, living units or other groupings of people represented
in a data repository, such as a selected population of customers or
prospects represented in a database or data files. In addition, the
system, method and software of the invention, depending on the
selected embodiment, also determine preferences for communication
channel or other media forms, communication timing, frequency of
communication, and/or sequences of types of communication.
[0011] The illustrated, exemplary embodiments of the present
invention are empirically-based, using actual attitudinal research
and other information from a population sample. Other types of
research or data may also be utilized, such as transactional data,
demographic data, marketing research data, or other types of survey
information. With this empirical basis, the invention provides
accurate modeling to predict and extrapolate such attitudinal,
behavioral, demographic or other information to a larger reference
population, thereby providing for accurate prediction of attitudes,
motivations and behaviors, which may be utilized for marketing
applications, for example. The exemplary embodiments of the
invention further provide information concerning preferred message
themes or message content independently from any population
grouping, segmentation or clustering process. In addition, the
exemplary embodiments of the invention provide actionable results,
providing not only audience attitudinal information and preferred
message content, but also preferred communication channel
information, communication media, communication frequency, and
communication timing and sequencing information.
[0012] The power of the invention cannot be overstated. As
indicated above, prior art methods have focused on finding "who",
namely, those individuals or households to whom marketers should
direct their communications. None of these prior art methods
provide, independently of the selection of "who", determination of
the "what" of the communication, such as preferred content or
versions of marketing information. None of these prior art methods
provide independent information on the "when" of the communication,
such as the customer's or prospect's preferred time of day to
receive communications. None of these prior art methods provide
independent information on the "how" of the communication, such as
the customer's or prospect's preferred medium or channel for
communication, such as direct mail, telephone, electronic mail
(email), broadcast media, print media, and so on. Lastly, none of
these prior art methods provide independent information on the
frequency (how often) and sequencing (ordering) of the
communications, based on preferences, such as print media for a
first number of times, followed by direct mail for a second number
of times, followed by email, for example.
[0013] More specifically, in exemplary embodiments, the present
invention provides a method, system and software for independently
predicting both a plurality of first predictive classification,
referred to as message content classifications, and a plurality of
second predictive classifications, referred to as attitudinal or
other behavioral classifications, for a selected population of a
plurality of individuals, households, living units or other
groupings of persons, as "entities", represented in a data
repository. As used herein, any reference to "entity" or "entities"
should be understood to mean and include any individual, household,
living unit, group or potential grouping of one or more people,
whether related or unrelated, individually or collectively, however
defined or demarcated, such as a household, a living unit, a
geographic unit, or any other grouping of individuals for whom or
which data may be maintained, generally at a granular or atomic
level, in a database.
[0014] In the exemplary embodiments, empirical attitudinal research
and predictive attitudinal classifications are illustrated as
examples, and should be understood to mean and include other forms
of research and classifications, such as behavioral or demographic
classifications formed from corresponding empirical research, such
as corresponding behavioral or demographic survey research, for
example.
[0015] The various exemplary method, system and software
embodiments of the invention, perform the following: [0016] First,
for each entity (e.g., individual or household) of the plurality of
entities of the selected population, appending from the data
repository a corresponding predictive attitudinal classification of
a plurality of predictive attitudinal classifications, and a
corresponding plurality of predictive message content
classifications, with the corresponding predictive attitudinal
classification and corresponding plurality of predictive message
content classifications having been determined using a plurality of
predictive (attitudinal) models developed from a sample population
and applied to a reference population represented in the data
repository. [0017] Second, for each predictive attitudinal
classification of the plurality of predictive attitudinal
classifications, determining a penetration index of the selected
population compared to the reference population. [0018] Third, for
each predictive attitudinal classification of the plurality of
predictive attitudinal classifications, independently determining
at least one predominant predictive message content classification
from the appended plurality of predictive message content
classifications of the plurality of individuals of the selected
population having the corresponding predictive attitudinal
classification of the plurality of predictive attitudinal
classifications.
[0019] In addition, depending upon the selected embodiment, for
each entity (e.g., individual or household) of the plurality of
entities of the selected population, the various embodiments
optionally provide for appending from the data repository a
corresponding predictive communication media (or other channel)
classification of a plurality of predictive communication media
classifications, a corresponding predictive communication timing
classification of a plurality of predictive communication timing
classifications, a corresponding predictive frequency of
communication classification of a plurality of predictive
communication frequency classifications, and a corresponding
predictive sequence of communications of a plurality of predictive
communication sequence classifications, with these classifications
having been determined from information stored in the data
repository.
[0020] Typically, the plurality of predictive communication media
classifications comprises at least two of the following
communication media (equivalently referred to as communication
channels): electronic mail, internet, direct mail,
telecommunication, broadcast media (such as radio, television,
cable, satellite), video media, optical media (DVD, CD), print
media (such as newspapers, magazines), electronic media (such as
web sites and electronic forms of newspapers, magazines), and
public display media (such as signage, billboards, multimedia
displays). Depending upon the selected embodiment, the plurality of
communication media and channel classifications may be more or less
specific, such as further subdividing print and electronic media
channels into newspaper, weekly magazines, monthly magazines,
journals, business reports, and further into their print, internet,
email or electronic versions. In addition, various forms of
broadcast media may have any of a plurality of forms, such as
cable, satellite, television and radio frequency transmission,
internet, etc. Also typically, the plurality of predictive
communication timing classifications comprises at least two of the
following communication timing classifications: morning, afternoon,
evening, night, weekday, weekend, any time (no preference), and
none. The plurality of predictive communication frequency
classifications typically comprises at least two of the following
frequency of communication classifications: daily, weekly,
biweekly, monthly, semi-monthly, bimonthly, annually,
semi-annually, and none. Lastly, the plurality of communication
sequences are highly varied and may include, for example, print
communications, followed by electronic communications.
[0021] In the various embodiments, the plurality of predictive
message content classifications are or have been determined by:
[0022] first, developing a plurality of empirical attitudinal
factors based on a factor analysis of an attitudinal survey of a
sample population; [0023] second, using each empirical attitudinal
factor of the plurality of empirical attitudinal factors, scoring
each participant of the attitudinal survey to create a
corresponding plurality of empirical attitudinal factor scores;
[0024] third, using a plurality of selected variables from the data
repository as independent variables, and using the corresponding
plurality of empirical attitudinal factor scores as dependent
variables, performing a regression analysis to create the plurality
of predictive attitudinal models; [0025] fourth, using each
predictive attitudinal model of the plurality of predictive
attitudinal models, scoring the plurality of entities represented
in the data repository, as the reference population, to create the
plurality of predictive message content classifications; and [0026]
fifth, independently determining the plurality of predictive
attitudinal classifications by a cluster analysis of the plurality
of predictive message content classifications of each entity of the
plurality of entities represented in the data repository. As
indicated above, in lieu of or in addition to the attitudinal
research and predictive attitudinal classifications, other types of
research and corresponding classifications may also be formed, such
as behavioral, demographic, and transactional.
[0027] The invention also provides for determining core, niche and
growth attitudinal classifications, as follows: [0028] determining
one or more core attitudinal classifications by selecting, from the
plurality of predictive attitudinal classifications, at least one
predictive attitudinal classification having a comparatively
greater (e.g., average or above average) penetration index and
having a comparatively greater proportion of a selected population;
[0029] determining one or more niche attitudinal classifications by
selecting, from the plurality of predictive attitudinal
classifications, at least one predictive attitudinal classification
having a comparatively greater penetration index and having a
comparatively lesser proportion of the reference population; and
[0030] determining one or more growth attitudinal classifications
by selecting, from the plurality of predictive attitudinal
classifications, at least one predictive attitudinal classification
having a comparatively lesser (e.g., below average) penetration
index and having a comparatively greater proportion of the
reference population.
[0031] In yet another aspect of the invention, the exemplary
embodiments provide a method and system for independently
predicting communication responsiveness of a selected population of
a plurality of entities represented in a data repository. The
method comprises: (a) for each entity of the plurality of entities
of the selected population, appending from the data repository a
corresponding predictive identification classification of a
plurality of predictive identification classifications, wherein the
plurality of predictive identification classifications designate a
plurality of entities according to a selected property; (b) for
each entity of the plurality of entities of the selected population
in a corresponding predictive identification classification,
appending at least one corresponding predictive message version
classification of a plurality of predictive message version
classifications, the plurality of predictive identification
classifications and the plurality of predictive message version
classifications having been determined from a plurality of
predictive models developed from a sample population and applied to
a reference population represented in the data repository; and (c)
for each predictive identification classification of the plurality
of predictive identification classifications, independently
determining at least one predominant predictive message version
classification from the corresponding, appended predictive message
version classifications of the plurality of entities of the
selected population of the predictive identification
classification. The selected property is derived from at least one
of the following: attitudinal characteristics, behavioral
characteristics, demographic characteristics, geographic
characteristics, financial characteristics, or transactional
characteristics.
[0032] In yet another aspect of the invention, the exemplary
embodiments provide a data structure for independently predicting
communication responsiveness of a selected population of a
plurality of entities represented in a data repository. Such a data
structure may be stored in a database, transmitted electronically,
or stored in a tangible medium. The data structure comprises: a
first field having a plurality of predictive identification
classifications, wherein the plurality of predictive identification
classifications designate a plurality of entities according to a
selected property; and a second field having, for each predictive
identification classification of the first field, at least one
predominant predictive message version classification of a
plurality of predictive message version classifications, the
plurality of predictive identification classifications and the
plurality of predictive message version classifications having been
determined from a plurality of predictive models developed from a
sample population and applied to a reference population represented
in the data repository.
[0033] The data structure may also include a third field having,
for each predictive identification classification of the first
field, at least one predominant predictive communication media
classification of a plurality of predictive communication media
classifications; a fourth field having, for each predictive
identification classification of the first field, at least one
predominant predictive communication timing classification of a
plurality of predictive communication timing classifications; a
fifth field having, for each predictive identification
classification of the first field, at least one predominant
predictive communication frequency classification of a plurality of
predictive communication frequency classifications; a sixth field
having, for each predictive identification classification of the
first field, at least one predominant predictive communication
sequencing classification of a plurality of predictive
communication sequencing classifications; and a seventh field
having a penetration index for each predictive identification
classification of the plurality of predictive identification
classifications. As indicated above, the selected property is
derived from at least one of the following: attitudinal
characteristics, behavioral characteristics, demographic
characteristics, geographic characteristics, financial
characteristics, or transactional characteristics.
[0034] In yet another aspect of the invention, the exemplary
embodiments provide a method for independently predicting
communication media responsiveness of a selected population of a
plurality of entities represented in a data repository, comprising:
(a) for each entity of the plurality of entities of the selected
population, appending from the data repository a corresponding
predictive identification classification of a plurality of
predictive identification classifications, wherein the plurality of
predictive identification classifications designate a plurality of
entities according to a selected property; and (b) for each
predictive identification classification of the plurality of
predictive identification classifications, independently
determining at least one predominant predictive communication media
classification of a plurality of predictive communication media
classifications.
[0035] In other embodiments, instead of step (b) above, the
exemplary method provides for each predictive identification
classification of the plurality of predictive identification
classifications, independently determining at least one predominant
predictive communication timing classification of a plurality of
predictive communication timing classifications, or for
independently determining at least one predominant predictive
communication frequency classification of a plurality of predictive
communication frequency classifications.
[0036] These and additional embodiments are discussed in greater
detail below. Numerous other advantages and features of the present
invention will become readily apparent from the following detailed
description of the invention and the embodiments thereof, from the
claims and from the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0037] The objects, features and advantages of the present
invention will be more readily appreciated upon reference to the
following disclosure when considered in conjunction with the
accompanying drawings and examples which form a portion of the
specification, in which:
[0038] FIG. 1 is a block diagram illustrating first and second
exemplary system embodiments in accordance with the present
invention.
[0039] FIG. 2 is a block diagram illustrating an exemplary
integrated data repository in accordance with the present
invention.
[0040] FIG. 3 (divided into FIGS. 3A and 3B and collectively
referred to as FIG. 3), is a flow diagram illustrating an exemplary
method for determination of predictive attitudinal classifications
and predictive message content classifications using a data
repository in accordance with the present invention.
[0041] FIG. 4 (divided into FIGS. 4A, 4B and 4C and collectively
referred to as FIG. 4), is a flow diagram illustrating an exemplary
method of independently predicting a plurality of attitudinal
classifications, a plurality of message content classifications,
and other predictive information, of a selected population using a
data repository in accordance with the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0042] While the present invention is susceptible of embodiment in
many different forms, there are shown in the drawings and will be
described herein in detail specific examples and embodiments
thereof, with the understanding that the present disclosure is to
be considered as an exemplification of the principles of the
invention and is not intended to limit the invention to the
specific examples and embodiments illustrated.
[0043] As indicated above, the present invention provides a system,
method and software for independently predicting a plurality of
attitudinal classifications and a plurality of message content
classifications, for a selected population of individuals,
households or other living units ("entities") represented in a data
repository, such as a database. The embodiments of the present
invention provide a predictive methodology, system and software,
for accurate prediction of attitudes, motivations and behaviors,
which may be utilized for marketing, research, assessment, and
other applications. The embodiments of the invention are
empirically-based upon actual attitudinal research and other
information from a population sample, and provide accurate modeling
to predict and extrapolate such attitudinal information to a larger
reference population. The embodiments of the invention further
provide information concerning preferred message themes or message
content independently from any population grouping, segmentation or
clustering process. In addition, the embodiments of the invention
provide actionable results, providing not only audience attitudinal
information and preferred message content, but also preferred
communication and media channel information, communication
frequency, and communication timing and sequence information.
[0044] FIG. 1 is a block diagram illustrating first exemplary
system embodiment 110 and second exemplary system embodiment 150 in
accordance with the present invention. As illustrated in FIG. 1,
the first exemplary system embodiment 110 is a computer system
embodiment (e.g., a mainframe computer), comprising an input and
output (I/O) interface 105, one or more processors 115, and a
memory 120 storing a database (or data repository) 100A. The memory
120 may be external, such as an external magnetic disk, tape, or
optical drive. The second system 150, such as an open or network
system, comprises a data repository (or database) 100B (also
embodied in a form of memory, discussed below), a database
management server 140, and/or an application server 125. A "data
repository", "database", and "data warehouse", as used herein, are
considered interchangeable, and may be relational, object-oriented,
object-relational, or use files or flat files, or any combinations
of the above. Both database 100A and 100B are instantiations of a
database 100, discussed in greater detail below.
[0045] In the exemplary embodiments of system 150, the database
management server 140 and the application server 125 may be
implemented together, such as implemented within the application
server 125. Either or both of the database management server 140
and the application server 125 are connected or coupled (or
couplable) to the data repository (database) 100B, for full duplex
communication, such as for database queries, database file or
record transfers, database updates, and other forms of database
communication. In the second system embodiment 150, the database
management server 140 and/or the application server 125 perform the
methodology of the invention utilizing a correspondingly programmed
or configured processor as discussed below (not separately
illustrated), such as a processor 115 illustrated for system 110,
in conjunction with a database 100 (such as database 100B).
[0046] Typically, the databases 100A and 100B are ODBC-compliant
(Open Database Connectivity), although this is not required for the
present invention. The first system 110 and second system 150 may
also be coupled to or may be part of a local area network ("LAN")
130 or, not separately illustrated, a wide area network ("WAN"),
such as for full duplex communication with a plurality of computers
(or other terminals) 135, also for database queries, database file
or record transfers, database updates, and other forms of database
communication. The LAN 130 communication capability provides for
the first system 110 and second system 150 to be accessible for
local access to the databases 100A and 100B, such as for large file
transfers or other batch processing, discussed in greater detail
below. In addition, the first system 110 may also be directly
accessible (185), such as for loading of records (e.g., magnetic
tape records or other media) for batch processing.
[0047] The first system 110 and second system 150 may also be
included within or coupled to a larger data communication network
180, through network (or web) server 160, for full duplex
communication with remote devices, such as a remote Internet or
other network server 170 and remote computer (or other terminal)
175. Such remote communication capability provides for the first
system 110 and second system 150 to be accessible for on-line
functionality, discussed in greater detail below, such as for
web-based access, using any of the prior art protocols, such as
hypertext transfer protocol (HTTP) or other Internet Protocol
("IP") forms of communication for data, voice or multimedia.
[0048] The data repository (or database) 100, illustrated as
databases 100A and 100B, may be embodied in any number of forms,
including within any data storage medium, memory device or other
storage device, such as a magnetic hard drive, an optical drive, a
magnetic disk or tape drive, other machine-readable storage or
memory media such as a floppy disk, a CDROM, a CD-RW, DVD or other
optical memory, a memory integrated circuit ("IC"), or memory
portion of an integrated circuit (such as the resident memory
within a processor IC), including without limitation RAM, FLASH,
DRAM, SRAM, MRAM, FeRAM, ROM, EPROM or EPROM, or any other type of
memory, storage medium, or data storage apparatus or circuit, which
is known or which becomes known, depending upon the selected
embodiment.
[0049] In the first system 110, the I/O interface may be
implemented as known or may become known in the art. The first
system 110 and second system 150 further include one or more
processors, such as processor 115 illustrated for first system 110.
As the term processor is used herein, these implementations may
include use of a single integrated circuit ("IC"), or may include
use of a plurality of integrated circuits or other components
connected, arranged or grouped together, such as microprocessors,
digital signal processors ("DSPs"), custom ICs, application
specific integrated circuits ("ASICs"), field programmable gate
arrays ("FPGAs"), adaptive computing ICs, associated memory (such
as RAM and ROM), and other ICs and components. As a consequence, as
used herein, the term processor should be understood to
equivalently mean and include a single IC, or arrangement of custom
ICs, ASICs, processors, microprocessors, controllers, FPGAs,
adaptive computing ICs, or some other grouping of integrated
circuits which perform the functions discussed below, with
associated memory, such as microprocessor memory or additional RAM,
DRAM, SRAM, MRAM, ROM, EPROM or EPROM. A processor (such as
processor 115), with its associated memory, may be adapted or
configured (via programming, FPGA interconnection, or hard-wiring)
to perform the methodology of the invention, as discussed above and
as further discussed below. For example, the methodology may be
programmed and stored, in a processor with its associated memory
(and/or memory 120) and other equivalent components, as a set of
program instructions (or equivalent configuration or other program)
for subsequent execution when the processor is operative (i.e.,
powered on and functioning). Equivalently, when the first system
110 and second system 150 may implemented in whole or part as
FPGAs, custom ICs and/or ASICs, the FPGAs, custom ICs or ASICs also
may be designed, configured and/or hard-wired to implement the
methodology of the invention. For example, the first system 110 and
second system 150 may implemented as an arrangement of
microprocessors, DSPs and/or ASICs, collectively referred to as a
"processor", which are respectively programmed, designed, adapted
or configured to implement the methodology of the invention, in
conjunction with a database 100.
[0050] The application server 125, database management server 140,
and the system 110 may be implemented using any form of server,
computer or other computational device as known or may become known
in the art, such as a server or other computing device having a
processor, microprocessor, controller, digital signal processor
("DSP"), adaptive computing circuit, or other integrated circuit
programmed or configured to perform the methodology of the present
invention, such as a processor 115, as discussed in greater detail
below.
[0051] FIG. 2 is a block diagram illustrating an exemplary data
repository (or database) 100 in accordance with the present
invention. As mentioned above, "data repository" as used herein, is
considered interchangeable with "database", and may be relational,
object-oriented, or object-relational, or utilize any other
database structure, in accordance with a selected embodiment. The
database 100 may be integrated, namely, that the information
resides within a singular, co-located or otherwise centralized
database structure or schema, or may be a distributed database,
with information distributed between and among a plurality of
databases, some of which may be remotely located from the other
databases. From another point of view, the database 100 may be
considered integrated in that a plurality of different tables or
types of tables, objects or relations are included within the
database 100, such as including an attitudinal classification table
240 with the other illustrated tables discussed below. While
generally not included within the database 100 (as potentially
private client data), optionally one or more copies of a selected
population data (file, table or database) 270, such as a client
customer databases, client customer flat files, or client master
databases, may also be utilized. (Use of any type of data
repository, whether an integrated database, a non-integrated
database, or any otherwise distributed or non-distributed database
structures or schemas, are within the scope of the present
invention. While referred to as tables, it should be understood
that the tables illustrated in the database 100 of FIG. 2 are to be
construed broadly, to mean and include relations, objects, object
relations, multidimensional relations, cubes, flat files, or other
similar or equivalent database constructs.) In addition, while a
plurality of relations (or connections) between and among the
various tables are illustrated in FIG. 2, it should be understood
that in any selected embodiment, a greater or fewer number of
relations, connections, cross-references, keys, or indices may be
utilized, all within the scope of the present invention.
[0052] The database 100 generally includes, for example, a name
table 205, an address table 210, a lifestyle and behavioral data
table 215, and a demographic data table 220, and depending upon the
selected embodiment, may also include a public record table 225, a
census data table 230, a summarized financial data table 235, and
other information tables 265. In various embodiments, the name
table 205 and address table 210 may be combined as a single table.
In the exemplary embodiments, the database 100 further includes an
attitudinal (or behavioral) classification table 240, a message
theme table 245, a communication media (or channel) table 250, a
communication timing table 255, a communication frequency table
260, a communication sequence table 285, a response data table 275,
and a transactional data table 280, as discussed in greater detail
below. While illustrated as separate tables or relations, it should
be understood that the information contained in such tables may be
contained or distributed between or among any number of tables or
relations, depending upon any applicable or selected schema or
other database 100 structure, in any number of equivalent ways, any
and all of which being within the scope of the present invention.
The data repository 100 is generally included within a first system
110 and/or second system 150, and respectively accessed through the
I/O 105 and processor 115, or an application server 125 or database
management server 140, discussed above.
[0053] The name table 205 contains all individual, consumer,
household, living unit, group or other entity names, in various
forms, variations, abbreviations, and so on, and is also utilized
for searching and matching processes, as discussed below. The
address table 210 contains all addresses of individuals,
households, living units, groups or other entities which will be
utilized for searching and matching processes, as discussed below.
In the exemplary embodiment, the lifestyle and behavioral data
table 215 contains lifestyle and behavioral information for
individuals, households, living units, or other groups or
consumers, as "entities", such as purchase behavior, activity data,
and any self-reported data. The demographic data table 220 contains
demographic information and possibly geodemographic information for
consumers and other individuals, households, living units, or other
entities, such as age, gender, race, religion, household
composition, income levels, career choice, etc. The public record
table 225 contains information available in public records, such as
vehicle ownership records, driving records, property ownership
records, public proceeding records, secured transaction records,
and so on. The census data table 230 contains census information,
typically available through a government agency. The summarized
financial data table 235, when included in database 100, typically
includes summaries of financial information generally for
individuals or households in a given geographic region (e.g., by
postal code), and could possibly also include bank account
information, investment information, securities information, and
credit or other private information, when available and to the
extent allowable under any applicable regulations or laws.
[0054] The transactional data table 280 typically contains
information concerning purchase history or other transaction
history of the various entities. The response data table 275
contains information typically related to transactional data, such
as for purchases made in response to a particular communications,
such as in response to a catalogue, direct mail, or a magazine
advertisement. The transactional data table 280 and the response
data table 275, for example, may be based upon data from particular
clients or groups of clients. The information contained in the
lifestyle and behavioral data table 215, the demographic data table
220, the public record table 225, the census data table 230, and
the summarized financial data table 235, other information tables
265, and any other available tables, depending upon the selected
embodiment, are utilized in the creation of independent variables
for the predictive attitudinal (or behavioral) modeling discussed
below. The selected population data 270, which may be in any of
various forms such as a table, a file, a flat file, a relation, a
database, or another data schema, such as a copy of a selected
customer database, contains names or names and addresses of
individuals, households, living units, other groups or entities,
such as customers or any other selected or designated population,
and is utilized to provide predictive attitudinal marketing
information for the selected population, as discussed in greater
detail below. Also optionally included within database 100 are
other information tables 265, such as for other demographic
information, credit information, and fraud information, when
available or authorized.
[0055] The attitudinal classification table 240, the message theme
table 245, the communication channel table 250, the communication
timing table 255, the communication frequency table 260, and the
communication sequence table 285, are generally created, populated
or segregated based upon the predictive attitudinal (or behavioral)
modeling discussed below with reference to FIG. 3.
[0056] Depending upon the selected database 100 embodiment, a table
or index (relation or look-up table) 200 of identifiers or
identifications ("IDs") of a plurality of individuals, consumers,
households, living units, other groups or entities, may be included
within the database 100. The identifiers are typically persistent,
with every entity assigned at least one ID. In the exemplary
embodiments, the ID table 200 also provides relations, links or
cross-references to a plurality of other relations or tables, such
as, for example, the name table 205, the address table 210, the
lifestyle and behavioral data table 215, the demographic data table
220, the public record table 225, the census data table 230, the
summarized financial data table 235, the attitudinal classification
table 240, the message theme table 245, the communication channel
table 250, the communication timing table 255, the communication
frequency table 260, the communication sequence table 285, and the
other information tables 265. The ID table 200 may also provide
relations, links or cross-references to selected population data
(file, table or database) 270, depending upon the selected
embodiment and the form of the data. The ID table 200 may be
utilized in the searching and matching processes discussed below,
and for other database applications, such as updating.
[0057] FIG. 3 is a flow diagram illustrating an exemplary method
for determination of first predictive (attitudinal) classifications
and second predictive (message content) classifications using a
data repository in accordance with the present invention. As
indicated above, in accordance with the exemplary embodiments, such
first predictive classifications are attitudinal and derived using
empirical attitudinal research; in other embodiments, the first
predictive classifications may be behavioral, demographic, or any
combination thereof, and derived from corresponding empirical
research, such as behavioral or demographic survey research. The
second predictive classifications are determined independently, and
in the exemplary embodiments, provide message content or message
theme classifications, which are subsequently utilized to determine
the "what" of a marketing or educational communication, for
example.
[0058] The method begins, start step 300, with results of one or
more attitudinal (or behavioral) surveys of individuals, as
consumers or as members of a living unit (household). As mentioned
above, as used herein, "entity" or "entities" should be understood
to mean and include any individual person, household, living unit,
group or potential grouping of one or more people, whether related
or unrelated, individually or collectively, such as a single
individual, a household, a living unit, a geographic unit, or any
other grouping of individuals for which data may be maintained,
generally at a granular or atomic level in a database. For example,
various databases may be maintained in which information is stored
and available at an individual level, for individual persons and,
in many cases, also maintained at a less granular level of living
units (households), while in other cases, also within the scope of
the present invention, information may be stored and available in a
database only at a household level, with the predicted attitudinal
and messaging classifications then pertaining to corresponding
households (as a larger grouping of one or more individuals). In
other cases, an individual residing at a first location may also be
considered to be part of a living unit at a second location, such
as a student residing in a college dormitory being considered part
of a family household residing at a different location. All such
variations are within the scope of the present invention and, for
ease of reference, any references to an entity or entities means
and includes any individual, person, or grouping or collection of
persons, however such a grouping may be defined or demarcated.
[0059] In the exemplary embodiments, an empirical modeling process
is performed in which selected questions from a survey are utilized
for obtaining information pertinent to consumer purchasing,
behaviors, and attitudes (which also may be considered to include
various behavioral and demographic components). Survey questions,
data and results are available from a wide variety of vendors,
publications, and other sources. Survey questions may also be
determined based upon the goals of the modeling and classification
processes. The survey may be conducted and results obtained in any
of various forms, such as via telephone, written survey, email
survey, mail survey, internet survey, personal interview, etc. The
selected questions from the survey are then subjected to a factor
analysis, step 305, to statistically determine which questions are
highly related to or among other survey questions, to what degree,
and to isolate the significant questions and determine
corresponding factors, step 510, with a selected group of highly
related survey questions forming a selected, corresponding factor.
This factor analysis may also be an iterative process in selected
embodiments. The resulting plurality of empirical attitudinal
factors identify attitudes, behaviors and motivations of the survey
participants, and identify the significant corresponding survey
questions. The plurality of empirical attitudinal factors which are
selected depend upon the selected purposes of the classifications
discussed below, such as marketing analyses, for example, and are
also dependent upon the selected purposes of the survey (as are the
resulting attitudinal and message content classifications,
discussed below), and the cultural, sociological, demographic, and
other characteristics of the sample population of the survey.
[0060] In an exemplary embodiment, a plurality of empirical
attitudinal factors were developed as a result of the factor
analysis and empirical attitudinal model determinations of steps
305 and 310. Exemplary factors for marketing purposes include, for
example, consumer brand loyalty, impulse buying behavior, incentive
driven behavior, and so on. Innumerable other factors and
corresponding questions will be apparent to those of skill in the
art, for other behavioral or demographic modeling, for example.
Corresponding exemplary statements are illustrated below, to which
a sample population was asked to agree or disagree, on a varying
scale, from highly agree, somewhat agree, neutral, somewhat
disagree, and highly disagree, resulting in an equivalent question
format. The sample questions are exemplary, for purposes of
illustration only, and any resulting set of attitudinal, behavioral
or demographic models (discussed below) will be empirically
determined based upon the purpose of the research survey, the
selected survey questions and results, the survey population and
culture, followed by factor analysis. Additional survey questions
may also be utilized to expand the attitudinal factors utilized.
Exemplary statements, utilized in a representative survey of the
present invention, include, for example:
[0061] If a product is made by a company I trust, I'll buy it even
if it is slightly more expensive.
[0062] I am willing to pay more for a product that is
environmentally safe.
[0063] I like to shop around before making a purchase.
[0064] I'm always one of the first of my friends to try new
products or services.
[0065] I prefer products that offer the latest in new
technology.
[0066] If I really want something I will buy it on credit rather
than wait.
[0067] I'd rather receive a sample of a product than a price-off
coupon.
[0068] Following development of the plurality of empirical
attitudinal factors of steps 305 and 310, all of the survey
participants, as a sample population, are scored across each of the
empirical attitudinal factors, step 315. For example, various
survey participants may have indicated various levels of agreement
(or disagreement) with the survey questions of a particular
attitudinal factor, and as such, would be scored high (or low) for
that particular attitudinal factor. The scoring process may also be
computed in probabilistic terms, as a probability of exhibiting a
particular attitude. As a consequence, in the exemplary embodiment,
each survey participant was scored across the plurality of
empirical attitudinal factors. Also for example, various survey
participants may have similar scores across a plurality of
empirical attitudinal factors, such as high scores for the same
first factor, and low scores for the same second factor.
[0069] Following scoring of the survey participants across each of
the empirical (attitudinal) factors in step 315, records in the
database 100 pertaining to the survey participants, as individuals
(or entities such as living units), are searched, and the survey
participants are matched with their corresponding records within
the database 100, step 320, generally utilizing the name table 205
and address table 210. For all entities such as individuals,
households, living units, or other groups having matching records
(i.e., matching individuals/households), demographic, lifestyle,
behavioral, and other variables (from the database 100) are
appended or linked to each (matching) survey participant, step 325,
such as variables from the lifestyle and behavioral data table 215,
the demographic data table 220, the public record table 225, the
census data table 230, the summarized financial data table 235, the
transactional data table 280, and the response data table 275, for
example. In the exemplary embodiment, the Experian INSOURCE.RTM.
database, as previously described, was utilized as the database
100. Using the appended database variables as independent
variables, and using the empirical attitudinal factor scores as
dependent variables, a predictive attitudinal model (or,
equivalently, an attitudinally predictive model) is developed for
each empirical attitudinal factor, step 330, thereby generating a
corresponding plurality of predictive attitudinal models, one for
each empirical attitudinal factor. In the exemplary embodiment, a
logistic regression analysis is performed, to identify the database
variables (as independent variables) which are statistically
significant predictors of the attitude of the corresponding
empirical attitudinal factor. Other statistical methods, such as
multiple linear regression analysis, other forms of regression
analysis, and other forms of modeling and statistical analysis, are
also considered equivalent and within the scope of the present
invention. Selection of given database variables is also a function
of the availability of such variables within the database 100,
namely, any given database may or may not include variables
available in other databases. Not separately illustrated in step
330, the plurality of attitudinally predictive models may also be
validated, such as by using a "holdout" (or separate) sample from
the survey results.
[0070] Using the plurality of predictive attitudinal models of step
330, all or most database entities (or members), namely, all or
most individuals, consumers, households, living units, or other
groups contained in the database (i.e., contained in the database
by having representative information in the database 100), are
scored (or otherwise evaluated) across each of the plurality of
predictive attitudinal models, step 335. Having been
scored/evaluated, these entities (e.g., individuals or households)
then form a reference population, utilized for comparative purposes
discussed below. The results from each such entity (individual or
group) being scored or evaluated based on each of the predictive
attitudinal models, in step 335, then form or represent (or
otherwise generate or determine) a corresponding plurality of
predictive message content classifications, also referred to as
predictive message theme classifications, for each such entity
(individual or group) represented in the database 100. More
specifically, using the plurality of predictive attitudinal models,
each entity (individual, household, living unit, or other group)
represented in the database 100 is predicted to have a
corresponding probability of belonging in or to a classification
associated with a particular attitude/behavior/demographic of
interest, in which entities (individuals, households, living units,
or other groups) exhibiting that attitude/behavior/demographic are
generally responsive, receptive, attentive, conducive to or
motivated by messages or other communications having a particular
content or theme. Such content or themes, for example, to which
individuals or groups may be receptive, may be matched, correlated
or derived from various information sources, such as information
contained in the lifestyle and behavioral data table 215, the
response data table 275, and the transactional data table 280. As a
consequence, the results from the evaluations using the predictive
attitude/behavior/demographic models provide or form the
corresponding predictive message content classifications for each
entity (individual, household, living unit, or other group) of the
reference population.
[0071] These classifications are referred to as predictive message
content classifications (or message theme classifications) because,
as discussed below, they are utilized to predict the message
content or message themes to which individuals, households, living
units, or other groups within that classification are likely to be
receptive or responsive. As indicated above, the actual result of
the scoring may be a probability of exhibiting the attitude in
question, or may be a number or percentile which can be
equivalently translated into such a probability. In the exemplary
embodiment, the probability scores were further classified into
nine tiers, which were then further utilized to create dichotomous
variables in order to classify the entity (individual or group) as
either exhibiting the attitude of interest or not exhibiting the
attitude of interest. For each such entity (individual or group),
the results of step 335, namely, the scores for each of the
predictive attitudinal models and/or each of the resulting
predictive message content classifications, are stored in the
database, step 340, such as in message theme table or relation
245.
[0072] For example, using the scores or evaluations from the
plurality of predictive attitudinal models for individual (or
group) "A", records may be stored indicating that "A" has a high
probability level of belonging to predictive message content
classifications "X", "Y", and "Z", and a low probability level of
belonging to each of the remaining predictive message content
classifications. Alternatively and equivalently, records for
individual (or group) "A" may be stored indicating that "A" has
certain scores from the evaluations under each of the predictive
attitudinal models, and belongs to predictive message content
classifications "X", "Y", and "Z" (with dichotomous variables of
"1"), and does not belong to each of the remaining predictive
message content classifications (with dichotomous variables of
"0"). As a consequence, depending upon the selected embodiment, all
or most entities represented in the database 100 have associated
scores for each of the plurality of predictive attitudinal models
and, correspondingly, a membership (or no membership), or a degree
or probability of membership, in each of the corresponding
plurality of predictive message content (or theme)
classifications.
[0073] It should be noted that in the exemplary embodiments, the
empirical attitudinal factors and predictive message content
classifications have a one-to-one correspondence, and may be very
similar. In other embodiments, there may be more or fewer
predictive message content classifications compared to empirical
attitudinal factors. As indicated above, the empirical attitudinal
factors are based on the factor analysis of the survey questions
from the sample population and are utilized to develop the
predictive attitude/behavior/demographic models incorporating the
database variables. The predictive attitude/behavior/demographic
models derived from the empirical analysis of the sample population
are then extended into the database population, as the reference
population. The results from this predictive modeling are then
matched or correlated with other database information to create the
corresponding predictive message content classifications. In
addition, given the appended database variables, various
demographic, lifestyle and behavioral characteristics may also be
included in or as part of the descriptions of the predictive
message content (or theme) classifications.
[0074] In the exemplary embodiments, a plurality of representative,
predictive message content (or message theme) classifications were
developed as a result of the analysis of step 335. Several examples
of predictive message content classifications are illustrated
below, with corresponding, exemplary message content guidelines. It
will be understood in the art that these predictive message content
classifications are exemplary and for purposes of illustration and
not limitation, and that any resulting set of predictive message
content classifications will be empirically determined based upon
the selected survey purposes; the selected survey questions and
results; the survey or sample population, demographics,
socioeconomics and culture; the plurality of predictive attitudinal
models; and the extrapolated database population.
Exemplary Predictive Message Content (or Theme)
Classifications:
[0075] First Exemplary Predictive Message Content (or Theme)
Classification:
[0076] Exemplary message content guidelines include rewarding and
complimenting for being the first to take advantage of new products
and services, highlighting new or cutting edge products or offers,
and demonstrating the prestige of the product/service offered.
[0077] Second Exemplary Predictive Message Content (or Theme)
Classification:
[0078] Exemplary message content guidelines include communicating
the strength and quality of a brand, the importance of
relationships and customer service, emphasizing the quality of a
product, emphasizing the number of years in business, and integrity
and quality awards.
[0079] Third Exemplary Predictive Message Content (or Theme)
Classification:
[0080] Exemplary message content guidelines include a family focus,
bonuses, presenting how a product/offer is better than a
competitive product/service, price comparison, and value
features.
[0081] Fourth Exemplary Predictive Message Content (or Theme)
Classification:
[0082] Exemplary message content guidelines include appealing to
altruism, activism, and appreciation for our ecology, the use of
natural ingredients, and emphasis on quality with details.
[0083] Fifth Exemplary Predictive Message Content (or Theme)
Classification:
[0084] Exemplary message content guidelines include use of
celebrity endorsements and testimonials to emphasize image and
style, and use of incentive gifts.
[0085] Sixth Exemplary Predictive Message Content (or Theme)
Classification:
[0086] Exemplary message content guidelines include demonstrating a
fair value using a straightforward, logical approach, a masculine
emphasis, and use of peer/user comparisons and testimonials.
[0087] Referring again to FIG. 3, following scoring of all or most
entities (individuals or groups) in the database 100 using the
plurality of predictive attitudinal models, concomitant assignment
of membership (or non-membership) of the entities (individuals or
groups) to the corresponding plurality of predictive message
content classifications in step 335, and storing the resulting
information in the database 100 of step 340, the exemplary method
of the invention performs a cluster or grouping analysis of all
such database members (entities) using either (or both) the
corresponding scores from each of the plurality of predictive
attitudinal models or the resulting assigned membership(s) (or
probability of membership(s)) in the predictive message content
classifications, step 345. This cluster or grouping analysis of
step 345 not only utilizes the plurality of predictive message
content classifications (and/or predictive attitudinal models), but
also utilizes combinations of the various predictive message
content classifications (or, equivalently, scores from the
corresponding predictive attitudinal models). Any form of cluster
or grouping analysis may be utilized, as known or may become known
in the field. The result of this cluster or grouping analysis is a
plurality of predictive attitudinal classifications. For example,
those entities that belong or are assigned to the same two
predictive message content classifications, or equivalently those
entities that scored high in the same two corresponding predictive
attitudinal models, may be clustered or grouped together into a
first predictive attitudinal classification. Also for example,
those entities who belong in one predictive message content
classifications and who do not belong in another predictive message
content classification may be clustered or grouped together into a
second predictive attitudinal classification. In addition, clusters
may exist for those who belong in only one predictive message
content classification.
[0088] For example, for the plurality of predictive attitudinal
classifications described below: a first exemplary cluster
exhibited both a "trend following" attitude and an "impulsive"
attitude, but not an "incentive driven" attitude; and a second
exemplary cluster exhibited an "environmentally conscious"
attitude, a "brand loyal" attitude, and a "buy American" attitude,
but not a "price conscious" attitude.
[0089] As a result of the cluster (segmentation or grouping)
analysis of step 345, a plurality of predictive attitudinal
classifications are developed, generally having a greater number of
classifications than the plurality of predictive message content
classifications, and providing higher granularity or discrimination
among the various attitudes/behaviors/demographics exhibited among
the database reference population. In the exemplary embodiments,
using membership or non-membership in the plurality of predictive
message content classifications (based on probability scores from
the predictive attitudinal models), clusters were identified where
the mean value of the dichotomous variable was 0.70 or higher,
indicating a segment that had at least one strong loading.
[0090] Following the cluster analysis, in step 350, each entity
(individual or group) represented in the database is assigned to a
predominant predictive attitudinal (behavioral or demographic)
classification, of the plurality of predictive attitudinal
(behavioral or demographic) classifications, based upon his, her or
its highest probability of exhibiting the attitude(s) (behaviors or
demographics) of interest of the corresponding classification. This
assignment may be determined equivalently by the entity's scores
from the predictive attitudinal models and/or the correspondingly
determined memberships in one or more predictive message content
classifications. For example, individuals or groups predicted to
exhibit only a single attitude of interest would be assigned to
that corresponding predictive attitudinal classification (or
cluster), while those exhibiting more than one attitude of interest
would be assigned to a corresponding predictive attitudinal
classification, as a cluster of those particular of attitudes. In
the exemplary embodiment, those entities (using dichotomous
variables or "all or none" scores for the predictive attitudinal
models) not assigned as described above are then re-clustered to
identify an optimal segment or cluster, which may not meet stricter
scoring requirements, but nonetheless indicate a predominant,
predictive attitudinal classification. Also in the exemplary
embodiments, an entity is assigned to one and only one predictive
attitudinal classification; in other embodiments, multiple
predictive attitudinal/behavioral/demographic classifications may
be assigned.
[0091] As is the case with the scores from the plurality of
predictive attitudinal models and the corresponding assignments to
the plurality of predictive message content classifications, such
assignments of predictive attitudinal classifications are also
stored in the database 100, step 355, such as in attitudinal
classification table or relation 240. As a consequence, all or most
entities represented in the database 100, in accordance with the
present invention, have a plurality of records stored in the
database 100, namely: (1) either or both the associated scores
(results) for each of the plurality of predictive attitudinal
models and/or, correspondingly and equivalently, a membership (or
no membership) or a degree or probability of membership in the
corresponding plurality of predictive message content (or theme)
classifications (message theme table 245); and (2) an assignment
into a predominant, predictive attitudinal classification
(attitudinal classification table 240). Following step 355, the
method of determination of predictive attitudinal classifications
and predictive message content classifications using a data
repository, in accordance with the present invention, may end,
return step 360.
[0092] In the exemplary embodiment, a plurality of predictive
attitudinal classifications were developed as a result of the
analysis of step 345, and representative examples are illustrated
immediately below. For each such exemplary predictive attitudinal
classification, corresponding marketing strategies, lifestyle and
interests, demographics, behaviors and attitudes, and socioeconomic
indicators are illustrated, generally derived from corresponding
database variables and other information available in a database
100, as well as syndicated survey research. It will be understood
in the art that these predictive attitudinal classifications and
their marketing names are exemplary and for purposes of
illustration and not limitation, and that any resulting set of
predictive attitudinal classifications will be empirically
determined based upon the selected survey questions and results;
the survey population, demographics, socioeconomics and culture;
the plurality of predictive attitudinal models; the extrapolated
database population; and the selected cluster analysis.
Exemplary Predictive Attitudinal (or Behavioral)
Classifications:
[0093] First Exemplary Predictive Attitudinal (or Behavioral)
Classification:
[0094] Individuals and households in this first predictive
attitudinal classification stay true to themselves and the brands
that they prefer. They are selective with their purchases, and look
for well-established products and services that have demonstrated
quality and value. Individuals and households in this first
predictive attitudinal classification are responsive to brand
extensions and use coupons on the products that they already have
an affinity toward. From the database 100, their lifestyle and
interests include enjoyment of reading and visits to bookstores;
television viewing and preferring informative programming and movie
classics; investing wisely and often; maintaining an exercise and
fitness regimen; and participation in activities such as golf,
tennis, fishing, and occasional gambling. Also from the database
100, their demographics include being established mid-lifers;
married, divorced or single; any children are grown and have left
home; they typically own their own homes, and have established
residences, usually in larger, affluent cities. The behaviors and
attitudes of the individuals and households in this first
predictive attitudinal classification include being ardent catalog
shoppers; having a preference for outdoor lifestyle companies;
shopping at upscale retail stores; preferring "the real thing" to
generic products; visiting the grocery store frequently with a
likelihood of using coupons to save on preferred brands; and
enjoyment of domestic and overseas travel. Their socioeconomic
indicators include a high income; an above average home value;
established credit experience with a well-maintained, stable credit
history; an undergraduate degree and some graduate studies;
occupations including finance, accounting, engineering and real
estate; and they drive luxury vehicles.
[0095] Second Exemplary Predictive Attitudinal (or Behavioral)
Classification:
[0096] Individuals and households in this second predictive
attitudinal classification represent a highly affluent, successful
and stable consumer market, containing established old-wealth and
the nouveau riche. Their investments and dividends are as
impressive as their incomes. They aspire to own and use the finest
quality brands and services, and they are willing to pay the extra
dollar for the privilege of living this lifestyle. They enjoy
traveling quite extensively, so incentives that provided added
benefit in this area are preferable. The active lifestyles they
lead drive them to utilize all modes of convenient communication.
The lifestyle and interests of individuals and households in this
second predictive attitudinal classification include a love to
travel domestically and overseas, preferring cruises and tours;
shopping at mid-level to upscale stores; diverse sports interests
and may be avid golfers; socially involved as club members, theatre
and concert-goers, and with environmental causes. Their
demographics include a wide age range, from young to mature adults;
largest concentration is established and mid-life adults, who are
typically married; their children range in age from grade school to
high school; they typically own their own homes, having
well-established residences, usually in comfortable and prosperous
neighborhoods, in major and mid-size cities, and in urban city
settings. The behaviors and attitudes of the individuals and
households in this second predictive attitudinal classification
include "working to live" rather than "living to work"; they are
active, affluent, have an influential lifestyle and are financially
astute; they make time for family and individual interests, and
want the "good life" for their family; they are technology- and
internet-savvy, with frequent web accessing. The socioeconomic
indicators include a high income; an above average home value;
extensive, established and good credit experience; they have an
undergraduate degree with some graduate studies; their occupations
include finance, engineering, healthcare, counseling,
computer/technology and marketing; they are more likely to lease
vehicles than to buy; drive new and used import cars and light
trucks, and are drawn to near-luxury, luxury, specialty and SUV
models.
[0097] Third Exemplary Predictive Attitudinal (or Behavioral)
Classification:
[0098] Individuals and households in this third predictive
attitudinal classification are dedicated sports fans that enjoy a
wide variety of outdoor pursuits--from do-it-yourself home
improvement projects to scuba diving, and they enjoy their
lifestyle. Their independence may make it a challenge to establish
relationships with these customers, and they prefer product samples
to coupons to provide immediate proof of the product's quality and
immediate savings. Their lifestyle and interests include being
outdoor enthusiasts; they have no preference for brand name goods
over generic brands; they are dedicated sports fans, they enjoy
working on mechanics, home improvement, boating, motorcycles, scuba
diving and video games. The demographics of individuals and
households in this third predictive attitudinal classification
include mainly being young adults, who are single or divorced, with
low indications of children present in the household; they
typically rent instead of own residences, and live in apartments
rather than single-family homes, with a wide variety of residential
settings, often transient or in rural towns. Individuals and
households in this third predictive attitudinal classification have
behaviors and attitudes such as liking things to be simple and
straightforward, "rough and rugged", and self-determining.
Socioeconomic indicators of individuals and households in this
third predictive attitudinal classification include a below average
income; a slightly below average home value; a newer credit
experience with average extension; varied education levels;
typically employed in service- and consumer-oriented industries
and/or may be students; they typically drive used, domestic
vehicles, and models include small to mid-size cars and small- and
full-size pickup trucks.
[0099] Fourth Exemplary Predictive Attitudinal (or Behavioral)
Classification:
[0100] Individuals and households in this fourth predictive
attitudinal classification are conservative, content with the
status quo and not easily swayed. They focus on "hearth and home"
for comfort and entertainment, avidly donate to the causes they
support, and enjoy timeless activities such as leisure sports,
musical performances, gardening, and reading. As consumers, they
are motivated to spend money on their families, homes and hobbies
but are careful to spend it well, making them highly responsive to
coupons and discount offers. Their lifestyle and interests include
family-oriented, domestic activities such as home improvement
projects, gardening, cooking and entertaining. They are typically
passionate donors that support causes such as religious, political
and health issues. They are also devoted book and magazine lovers
and sports enthusiasts. The demographics of the individuals and
households in this fourth predictive attitudinal classification are
that they are mainly seniors and retirees, typically married, whose
children have left home (empty nesters). They typically own their
own homes, usually multi-dwelling units rather than single-family
homes, and prefer to live in rural towns and small city
communities. The behaviors and attitudes of the individuals and
households in this fourth predictive attitudinal classification
include a relaxed living attitude, with a healthy standard of
living, deriving significant pleasure from daily activities with
family and friends. They make the most of their spending and
utilize coupons. They like to keep up on interests in music, trivia
and collectibles. Socioeconomic indicators for this classification
include a low income, with an average to below average home value;
stable, consistent and capable credit experience; they are
typically high school graduates with some college; and primarily
are retired. They typically drive domestic used vehicles that
include mid-range cars and pick-up trucks
[0101] The plurality of predictive attitudinal classifications and
plurality of predictive message content classifications, with
additional information available in a database 100 as discussed
below, become extraordinarily powerful tools when applied to a
selected population, such as a group of individuals represented in
a customer database, a prospect database, a client database, a
membership database, an association database, and so on. In the
exemplary embodiment, the additional information available in the
database 100 includes, for all or most of the represented (or
matched) individuals, households, living units or other entities:
their preferred methods of communication and/or communication media
(communication media table 250), their preferred times (time of
day) of communications (communication timing table 255), their
preferred frequencies of communication (communication frequency
table 260), and their preferred sequences of communication
(communication sequence table 285). This additional information may
be determined in a wide variety of ways, including self-reported
preferences and behaviors, third-party reported preferences and
behaviors (such as transactions, purchases, and activities),
observed preferences and behaviors, and inferred preferences and
behaviors based on modeled data.
[0102] FIG. 4 is a flow diagram illustrating an exemplary method of
independently predicting a plurality of attitudinal classifications
and a plurality of message content classifications of a selected
population in accordance with the present invention. In addition,
depending upon the selected embodiment, predicted communication
and/or media channels, predicted communication timing, predicted
communication frequency, and predicted communication sequencing,
may also be provided as part of the method illustrated with
reference to FIG. 4.
[0103] Referring to FIG. 4, the method begins, start step 400, with
data about or concerning a selected population, such as name and/or
name and address information from a customer database or file, a
customer prospect file or list, or any other identifying data or
information of or for a group of individuals, households, living
units, groups or other entities, for any selected purpose. The
database 100 is then searched and the selected population data is
matched with the records of the database 100, step 405, such as
matched with the records of the INSOURCE.RTM. database. For all
records where a match is found in step 405, the method appends,
references or links, to each (matched) entity of the selected
population, their corresponding (i.e., predominant) predictive
attitudinal classification, and their corresponding predictive
message content classification(s), step 410. As discussed above,
for each such entity, their corresponding predictive attitudinal
classification is generally their predominant attitudinal
classification of the plurality of predictive attitudinal
classifications, and their corresponding predictive message content
classifications are generally their memberships or probabilities of
membership in each of the plurality of predictive message content
classifications. In the exemplary embodiments, optionally as part
of step 410, the method also appends, references or links the
entity's associated information concerning predicted communication
and media channels, predicted communication timing, predicted
communication frequency, and predicted communication sequence.
[0104] There are a wide variety of alternatives or defaults for
non-matching entities of step 405, including variations depending
upon degrees or levels of matching. Exemplary alternatives include,
for non-matching individuals or groups, appending and utilizing the
average, most common or mode classifications for a particular
geographic region, such as a postal code area. Another alternative
includes excluding those non-matching individuals or groups from
the remainder of the method and, equivalently, the selected
population may be considered to be comprised of the matching
entities from step 405. Those of skill in the art will recognize
that the matching step 405 and the appending step 410 may be
performed in a plurality of ways, including use of conditional
loops or iterations, with each iteration corresponding to the
matching and appending for a given entity, and with iterations
continuing until all entities have been matched (or found to not
match) and corresponding data appended.
[0105] The method then determines the distribution of the selected
population across or within each of the predictive attitudinal
classifications, to form a corresponding plurality of selected
population distributions, step 415. Each selected population
distribution is compared to a reference distribution for each of
the predictive attitudinal classifications, step 420. Typically, a
reference or baseline distribution is or may be the distribution,
across or within each of the predictive attitudinal
classifications, of the larger, often national or regional
population represented in the database 100, referred to above as
the reference population. For example, for a selected population,
such as the purchasers of a particular automobile brand, when
compared to a larger regional or national population on a
proportional or percentage basis, that selected population may be
comparatively or relatively over-represented in certain predictive
attitudinal classifications, and that selected population may be
comparatively or relatively under-represented in other predictive
attitudinal classifications.
[0106] Based on these comparisons of the distribution of the
selected population with a reference distribution, for each
predictive attitudinal classification of the plurality of
predictive attitudinal classifications, a "penetration" or
comparative index (or rate) is determined, step 425, with a
comparatively greater or higher penetration index indicative of a
higher proportional concentration of entities of the selected
population within a given predictive attitudinal classification
compared to the reference distribution, and with a comparatively
lower or lesser penetration index indicative of a lower
proportional concentration of entities of the selected population
within a given predictive attitudinal classification compared to
the reference distribution. For example, a 15% distribution of the
selected population for the predictive attitudinal classification
of "Q", when compared to an 8% distribution for the reference
population for this same "Q" predictive attitudinal classification,
indicates a comparatively higher (or above average) penetration
index or rate (a ratio of 1.875) of the selected population in this
classification. Similarly, an 11% distribution of the selected
population for the predictive attitudinal classification of "P",
when compared to a 16% distribution for the reference population
for this same "P" predictive attitudinal classification, indicates
a comparatively lower (or below average) penetration index or rate
(a ratio of 0.6875) of the selected population in this
classification.
[0107] In addition, for each predictive attitudinal classification,
the reference distribution may be normalized to a particular value,
such as 100 or 1.0, e.g., a reference distribution of 11% in a
first predictive attitudinal classification may be normalized to
100 and a reference distribution of 7% in a second predictive
attitudinal classification may also be normalized to 100. Also for
example, for the selected population, and for a given predictive
attitudinal classification, a penetration index of 150 or 1.5 may
be utilized to indicate that the selected population has
proportionally (or percentage-wise) 50% (or 1.5 times) more
individuals (households, living units or other groups) in that
given predictive attitudinal classification compared to the larger
reference population, such as a national or regional population. As
illustrated above with the various percentage distributions for the
"Q" and "R" predictive attitudinal classifications, these
comparisons are performed on a proportional or percentage basis,
rather than a comparison of pure or gross numbers, as the selected
population generally concerns a considerably smaller total number
of individuals (or groups) compared to the reference population
represented in the database 100.
[0108] As a result of step 425, penetration indices or rates are
determined for each predictive attitudinal classification of the
plurality of predictive attitudinal classifications, comparing the
proportion or distribution of the selected population in that
classification to the proportion or distribution of the reference
population in that classification. The plurality of predictive
attitudinal classifications are then evaluated by their penetration
indices and, depending upon the selected embodiment, are also
evaluated based upon the relative (or proportional) reference and
selected population sizes within each predictive attitudinal
classification, step 430. Using the penetration indices and
relative or comparative reference and selected population sizes of
each predictive attitudinal classification, three additional levels
of attitudinal classifications are determined, namely, core
attitudinal classifications, niche attitudinal classifications, and
growth attitudinal classifications (steps 435, 440, 445). While
core determinations are usually determined first (to avoid
potential confusion with niche determinations, as based upon
proportions of the selected population in addition to penetration
indices), the other determinations may be performed in any order.
In other variations, depending upon the selected evaluation
algorithm, other determination orders for core, niche and growth
attitudinal classifications may be available.
[0109] More specifically, in step 435, one or more core attitudinal
classifications are determined by selecting, from the plurality of
predictive attitudinal classifications, at least one predictive
attitudinal classification having a comparatively greater (e.g.,
average or above) penetration index and having a comparatively
greater proportion of the selected population. These core
attitudinal classifications represent predictive attitudinal
classifications having the largest percentage of the selected
population, such as customers, and corresponding, significant
market share. With respect to a selected population of customers of
a particular brand, the core attitudinal classifications represent
significant brand appeal to population segments exhibiting
corresponding behavioral characteristics.
[0110] In step 440, one or more niche attitudinal classifications
are determined by selecting, from the plurality of predictive
attitudinal classifications, at least one predictive attitudinal
classification having a comparatively greater (e.g., average or
above) penetration index and having a comparatively lesser
proportion of the reference population. These niche attitudinal
classifications represent predictive attitudinal classifications
having a high penetration rate (and corresponding market share),
but a relatively small percentage of the reference population, such
as a small percentage of a prospect population.
[0111] In step 445, one or more growth attitudinal classifications
are determined by selecting, from the plurality of predictive
attitudinal classifications, at least one predictive attitudinal
classification having a comparatively lesser (e.g., below average)
penetration index and having a comparatively greater proportion of
the reference population. These growth attitudinal classifications
represent predictive attitudinal classifications having some
penetration success, and with the comparatively large percentages
of the reference population, such as prospective customers,
indicate significant opportunities to increase penetration and add
new customers from an otherwise underrepresented group.
[0112] To this point in the method of the present invention,
considerable attitudinal and behavioral information has been
provided, which may be utilized for a wide variety of purposes.
Based on empirical modeling, actual attitudes and behaviors of
segments of a selected population may be predicted, using the
plurality of predictive attitudinal classifications. Depending upon
selected purposes of the embodiment, additional information may be
provided, such as the actual attitudes and behaviors of individuals
or groups in the predictive attitudinal classifications, including
the core, niche and growth classifications.
[0113] Additional information is also independently provided in
accordance with the present invention. While a selected population
has been predictively classified as exhibiting certain attitudes
and behaviors, as "who" segments (such as who among the population
are significant customers or prospects), an additional, independent
and more fine-grained level of information is also provided, based
upon the plurality of predictive message content classifications,
providing independent "what" segments (such as what content will be
most effective). More specifically, the actual members of the
selected population, although assigned to a predictive attitudinal
classification as a predominant classification, may also exhibit
other or different attitudes and behaviors, represented by a
probability or membership in one or more predictive message content
classifications, in addition to those of the predominant predictive
attitudinal classification. As consequence, in step 450, for each
of the plurality of predictive attitudinal classifications, the
method also independently determines one or more predictive message
content classifications, based on the predictive message content
classifications of the actual entities (individuals or groups) of
the selected population assigned to that selected predictive
attitudinal classification. For each predictive attitudinal
classification, the plurality of predictive message content
classifications may also be ranked, such as by comparative or
relative penetration, proportion or distribution of a given
predictive message content classification for that predictive
attitudinal classification, step 455.
[0114] This independent determination of predictive message content
classifications based upon the actual, selected population (step
450 and optional ranking step 455) within each predictive
attitudinal classification, may be used to produce (or effectively
results in) an information matrix or data structure, consisting of
the plurality of predictive attitudinal classifications (e.g., as
rows) and the plurality of predictive message content
classifications (e.g., as columns), both of which may be further
ranked or ordered according to relative distribution, penetration
and/or population size. As a result, not only may a selected
population be predictively classified or segmented attitudinally
and behaviorally, using the plurality of predictive attitudinal
classifications, they may also be independently and predictively
classified based on content or theme receptivity, using the
plurality of predictive message content classifications.
Communication channel, media, timing, frequency, and sequencing
information may also be included in such a matrix, e.g., as
columns, and is discussed in greater detail below, as the various
fields of a data structure of the present invention.
[0115] In the exemplary embodiments, with the availability of
channel, media, timing, frequency, and sequencing information in
the database 100, the method continues with step 460, in which the
predominant communication channel and/or media preferences are
determined for each predictive attitudinal classification of the
plurality of predictive attitudinal classifications, based upon the
preferred communication channels and/or preferred media types of
the entities (individuals or groups) of the selected population
assigned to the given predictive attitudinal classification, such
as email, internet, direct mail, telecommunication, radio
(broadcast, cable and satellite), television (network (broadcast),
cable or satellite), video (or DVD) media, print media, electronic
media, visual or other public display media, and depending upon the
selected embodiment, the plurality of communication and media
channel classifications may be more or less specific, such as
further subdividing print and electronic media channels into
newspaper, weekly magazines, monthly magazines, journals, business
reports, and further into their print, internet, email or
electronic versions. For example, predominant communication
channels for a first predictive attitudinal classification may be,
in preferred order, direct mail followed by radio followed by
email, while predominant communication channels for a second
predictive attitudinal classification may be, also in preferred
order, television followed by telecommunication followed by direct
mail.
[0116] In step 465, the predominant timing (time of day)
preferences for communications are determined for each predictive
attitudinal classification of the plurality of predictive
attitudinal classifications, also based upon the communication
timing preferences of the entities (individuals or groups) of the
selected population assigned to the given predictive attitudinal
classification. For example, predominant timing preferences for a
first predictive attitudinal classification may be, in preferred
order, weekends followed by evening, while predominant timing
preferences for a second predictive attitudinal classification may
be, also in preferred order, mornings followed by afternoons. The
timing preferences may be further qualified based upon media and
communication channels, such as predominant timing preferences
being evenings for television, and weekends for
telecommunications.
[0117] In step 470, optionally, the predominant frequency
preferences for communications are determined for each predictive
attitudinal classification of the plurality of predictive
attitudinal classifications, also based upon the frequency of
communication preferences of the entities (individuals or groups)
of the selected population assigned to the given predictive
attitudinal classification. For example, predominant frequency
preferences for a first predictive attitudinal classification may
be, in preferred order, monthly followed by semi-annually, while
predominant timing preferences for a second predictive attitudinal
classification may be, also in preferred order, weekly followed by
bi-weekly. The predominant frequency of communications also may be
further qualified based on either or both timing preferences and
media and communication channels, such as no frequency preference
(unlimited) for television communications, and zero frequency (no
communication) for telecommunication channels (e.g., telephone
call, faxes).
[0118] In step 475, optionally, the predominant sequencing
preferences for communications are determined for each predictive
attitudinal classification of the plurality of predictive
attitudinal classifications, also based upon the sequencing of
communication preferences of the entities (individuals or groups)
of the selected population assigned to the given predictive
attitudinal classification. This information may also be
incorporated into the matrix discussed above and the data structure
discussed below.
[0119] Lastly, this collection of information is output and, in the
illustrated exemplary embodiment, also stored in a database, step
480. In the exemplary embodiment, as indicated, a matrix or data
structure of information is provided in step 480, indicating the
following: [0120] (1) the plurality of predictive attitudinal
classifications, ordered by core, niche and growth attitudinal
classifications (and corresponding penetration indices); [0121] (2)
for each predictive attitudinal classification of the plurality of
predictive attitudinal classifications, the predominant predictive
message content classifications of the selected population assigned
to that given predictive attitudinal classification; [0122] (3) for
each predictive attitudinal classification of the plurality of
predictive attitudinal classifications, the predominant
communication and/or media channel classifications of the selected
population assigned to that given predictive attitudinal
classification; [0123] (4) for each predictive attitudinal
classification of the plurality of predictive attitudinal
classifications, the predominant communication timing
classifications of the selected population assigned to that given
predictive attitudinal classification; [0124] (5) as an additional
option in the exemplary embodiments, for each predictive
attitudinal classification of the plurality of predictive
attitudinal classifications, the predominant frequency of
communication classifications of the selected population assigned
to that given predictive attitudinal classification; [0125] (6) as
an additional option in the exemplary embodiments, for each
predictive attitudinal classification of the plurality of
predictive attitudinal classifications, the predominant sequencing
of communication classifications of the selected population
assigned to that given predictive attitudinal classification.
Following the output of information in step 480, the method may
end, return step 485.
[0126] As indicated above, the system of the present invention
generally comprises a memory storing a data repository (or
database) 100 and a processor, such as a processor 115 included
within a mainframe computer of system 110 or within either (or
both) a database management server 140 or an application server 125
of system 150. The processor is programmed to perform the
methodology of the present invention. As a consequence, the system
and method of the present invention may be embodied as software
which provides such programming.
[0127] More generally, the system, methods and programs of the
present invention may be embodied in any number of forms, such as
within any type of computer, within a workstation, within an
application server such as application server 125, within a
database management server 140, within a computer network, within
an adaptive computing device, or within any other form of computing
or other system used to create or contain source code. Such source
code further may be compiled into some form of instructions or
object code (including assembly language instructions or
configuration information). The software or source code of the
present invention may be embodied as any type of source code, such
as SQL and its variations (e.g., SQL 99 or proprietary versions of
SQL), C, C++, Java, or any other type of programming language which
performs the functionality discussed above. As a consequence, a
"construct" or "program construct", as used herein, means and
refers to any programming language, of any kind, with any syntax or
signatures, which provides or can be interpreted to provide the
associated functionality or methodology (when instantiated or
loaded into a server or other computing device).
[0128] The software or other code of the present invention, such as
any resulting or compiled bit file (object code or configuration
bit sequence), may be embodied within any tangible storage medium,
such as within a memory or storage device for use by a computer, a
workstation, any other machine-readable medium or form, or any
other storage form or medium for use in a computing system. Such
storage medium, memory or other storage devices may be any type of
memory device, memory integrated circuit ("IC"), or memory portion
of an integrated circuit (such as the resident memory within a
processor IC), including without limitation RAM, FLASH, DRAM, SRAM,
MRAM, FeRAM, ROM, EPROM or EPROM, or any other type of memory,
storage medium, or data storage apparatus or circuit, depending
upon the selected embodiment. For example, without limitation, a
tangible medium storing computer readable software, or other
machine-readable medium, may include a floppy disk, a CDROM, a
CD-RW, a magnetic hard drive, an optical drive, a quantum computing
storage medium or device, a transmitted electromagnetic signal
(e.g., used in internet downloading), or any other type of data
storage apparatus or medium.
[0129] The results, information and other data provided by the
system, methods and programs of the present invention also may be
embodied as a data structure and stored or provided in any number
of forms and media, such as a data structure stored within any type
of computer, within a workstation, within an application server
such as application server 125, within a database management server
140, within a computer network, within a database 100, within an
adaptive computing device, or within any form of memory, storage
device, or machine-readable media, as discussed above. In
accordance with the present invention, such a data structure is
comprised of at least two fields of a plurality of fields, as
follows.
[0130] A first field of the plurality of fields provides or stores
information, such as codes or designations, pertaining to a first
plurality of classifications which provide identification of
persons according to a selected property. For example, the
plurality of predictive attitudinal classifications identify
persons (in either or both the reference population or the selected
population), according to an attitudinal (selected) property. In
other circumstances, this identification of "who" may be based on
other selected properties, such as behavioral characteristics,
demographic characteristics, geographic characteristics, financial
characteristics, transactional characteristics, etc., such as
identification of persons who engage in certain activities, who
live in certain types of households, who live in a certain region
or postal code area, who have incomes greater than a certain
amount, who purchase particular goods of a particular monetary
amount, and so on.
[0131] Optionally, depending upon the selected embodiment,
additional fields related to this first field or which are
subfields of this first field provide or store additional
information pertaining to, for example, the percentage of the
selected population or the reference population within each
classification of the first plurality of classifications, or the
corresponding penetration indices for each classification of the
first plurality of classifications, or both, such as the
corresponding penetration indices for the plurality of predictive
attitudinal classifications. Other information in these additional
fields or subfields may also specify a size of a prospect
population, for marketing applications, for example.
[0132] Also optionally, depending on the selected embodiment, the
first field may also include additional fields or subfields based
on other relevant or related properties. For example, this first
field may be further divided into categories such as core, niche
and growth classifications, as discussed above.
[0133] A second field of the plurality of fields provides or stores
information, such as codes or designations, pertaining to a second
plurality of classifications, in which the second plurality of
classifications provides information pertaining to a corresponding
plurality of message versions, message content, or message themes.
This second field providing a designation or code for the "what" of
a communication will typically have one or two forms (or both),
such as containing general information concerning types of
messages, as in the plurality of predictive message content
classifications described above, or containing more particular
information, such as specific content versions correspondingly
tailored to the plurality of predictive message content
classifications. For an example of the latter case, this second
field may include at least one designation or code for a particular
version (of a plurality of content versions) for use in a direct
mail to the entities identified in the first field (via the
plurality of predictive attitudinal classifications), with other
versions transmitted to other entities of the other classifications
of the first plurality of classifications.
[0134] A third field of the plurality of fields provides or stores
information, such as codes or designations, pertaining to a third
plurality of classifications which provide media/channel
information, such as the media and channel preferences which
correspond to the preferences of the individuals identified in the
first field. For example, this third field may include designations
or codes (providing the "how" of a communication) corresponding to
communication media (channels), such as for electronic mail,
internet, direct mail, telecommunication, broadcast media (such as
radio, television, cable, satellite), video media, optical media
(DVD, CD), print media (newspaper, weekly magazines, monthly
magazines, journals, business reports), electronic media (such as
web sites and electronic forms of newspapers, magazines), and
public display media (such as signage, billboards, multimedia
displays).
[0135] A fourth field of the plurality of fields provides or stores
information, such as codes or designations, pertaining to a fourth
plurality of classifications which provide communication timing
information. For example, this fourth field may include
designations or codes (providing the "when" of a communication)
corresponding to communication timing classifications such as
morning, afternoon, evening, night, weekday, weekend, any time (no
preference), and none.
[0136] A fifth field of the plurality of fields provides or stores
information, such as codes or designations, pertaining to a fifth
plurality of classifications which provide frequency of
communication information. For example, this fifth field may
include designations or codes corresponding to predictive
communication frequency classifications, such as daily, weekly,
biweekly, monthly, semi-monthly, bimonthly, annually,
semi-annually, and none.
[0137] A sixth field of the plurality of fields provides or stores
information, such as codes or designations, pertaining to a sixth
plurality of classifications which provide communication sequencing
information. For example, this fourth field may include
designations or codes corresponding to particular sequences of
communications, such as direct mail, followed by electronic media,
followed by email. As indicated above, there are innumerable such
combinations available.
[0138] A wide variety of selections of which fields are included in
the data structure and the ordering of these various selected
fields are available, as will be apparent to those of skill in the
art, and are within the scope of the invention. In addition, this
data structure embodiment may be housed, embodied, or stored in
myriad orders and locations, such as different memory locations as
directed by a DMA engine or memory address generator, for example.
The data structure of the present invention may also be embodied,
stored, distributed or communicated in a wide variety of forms,
such as electronically (e.g., internet, wireless, email, storage
disk), or through various print media, for example, such as in the
form of a market research report.
[0139] In summary, the present invention provides a method, system
and software for independently predicting a plurality of
attitudinal classifications and a plurality of message content
classifications, for a selected population of a plurality of
entities (such as individuals or households) represented in a data
repository. The method, system and software embodiments of the
invention, in operation, each perform the following: [0140] First,
for each entity of the plurality of entities of the selected
population, appending from the data repository a corresponding
predictive attitudinal classification of a plurality of predictive
attitudinal classifications and a corresponding plurality of
predictive message content classifications, with the corresponding
predictive attitudinal classification and corresponding plurality
of predictive message content classifications having been
determined using a plurality of predictive attitudinal models
developed from a sample population and applied to a reference
population represented in the data repository. [0141] Second, for
each predictive attitudinal classification of the plurality of
predictive attitudinal classifications, determining a penetration
index of the selected population compared to the reference
population; and [0142] Third, for each predictive attitudinal
classification of the plurality of predictive attitudinal
classifications, independently determining at least one predominant
predictive message content classification from the appended
plurality of predictive message content classifications of the
plurality of entities of the selected population having the
corresponding predictive attitudinal classification of the
plurality of predictive attitudinal classifications.
[0143] Typically, the independent determination of at least one
predominant predictive message content classification comprises:
for each predictive attitudinal classification, determining all of
the appended plurality of predictive message content
classifications of the plurality of entities of the selected
population having the corresponding predictive attitudinal
classification; and selecting one or more predictive message
content classifications corresponding to a comparatively greater
number of entities of the selected population.
[0144] In addition, depending upon the selected embodiment, for
each entity of the plurality of entities of the selected
population, the various embodiments provide for appending from the
data repository at least one corresponding predictive communication
media/channel classification of a plurality of predictive
communication media classifications, with the corresponding
predictive communication media classification having been
determined from information stored in the data repository. For each
predictive attitudinal classification of the plurality of
predictive attitudinal classifications, the various embodiments
provide for independently determining at least one predominant
predictive communication media classification from the appended
plurality of predictive communication media classifications of the
plurality of entities of the selected population having the
corresponding predictive attitudinal classification of the
plurality of predictive attitudinal classifications. Typically, the
plurality of predictive communication media classifications
comprises at least two of the following communication media:
electronic mail (email), direct mail, telecommunication, radio,
television, video or DVD (digital versatile disk) media, print
media, and visual or public display media. Depending upon the
selected embodiment, the plurality of communication and media
channel classifications may be more or less specific, such as
further subdividing print and electronic media channels into
newspaper, weekly magazines, monthly magazines, journals, business
reports, and further into their print, internet, email or
electronic versions, and such as further subdividing broadcast
media such as radio and television into network, cable, satellite,
etc.
[0145] Similarly, depending upon the selected embodiment, for each
entity of the plurality of entities of the selected population, the
various embodiments provide for appending from the data repository
at least one corresponding predictive communication timing
classification of a plurality of predictive communication timing
classifications, the corresponding predictive communication timing
classification having been determined from information stored in
the data repository. For each predictive attitudinal classification
of the plurality of predictive attitudinal classifications, the
various embodiments provide for independently determining at least
one predominant predictive communication timing classification from
the appended plurality of predictive communication timing
classifications of the plurality of entities of the selected
population having the corresponding predictive attitudinal
classification of the plurality of predictive attitudinal
classifications. Also typically, the plurality of predictive
communication timing classifications comprises at least two of the
following communication timing classifications: any time, morning,
afternoon, evening, night, weekday, and weekend.
[0146] Also similarly, depending upon the selected embodiment, for
each entity of the plurality of entities of the selected
population, the various embodiments provide for appending from the
data repository at least one corresponding predictive communication
frequency classification of a plurality of predictive communication
frequency classifications, the corresponding predictive
communication frequency classification having been determined from
information stored in the data repository. For each predictive
attitudinal classification of the plurality of predictive
attitudinal classifications, the various embodiments provide for
independently determining at least one predominant predictive
communication frequency classification from the appended plurality
of predictive communication frequency classifications of the
plurality of entities of the selected population having the
corresponding predictive attitudinal classification of the
plurality of predictive attitudinal classifications. The plurality
of predictive communication frequency classifications typically
comprises at least two of the following frequency classifications:
daily, weekly, biweekly, monthly, semi-monthly, bimonthly,
annually, semi-annually, unlimited, and none.
[0147] Also similarly, depending upon the selected embodiment, for
each entity of the plurality of entities of the selected
population, the various embodiments provide for appending from the
data repository at least one corresponding predictive communication
sequencing classification of a plurality of predictive
communication sequencing classifications, the corresponding
predictive communication sequencing classification having been
determined from information stored in the data repository. For each
predictive attitudinal classification of the plurality of
predictive attitudinal classifications, the various embodiments
provide for independently determining at least one predominant
predictive communication sequencing classification from the
appended plurality of predictive communication sequencing
classifications of the plurality of entities of the selected
population having the corresponding predictive attitudinal
classification of the plurality of predictive attitudinal
classifications.
[0148] As part of the present invention, the various embodiments
provide for determining the selected population of the plurality of
entities by matching a listing of a plurality of customers to the
reference population represented in the data repository.
Alternatively, non-matching entities of the selected population may
simply be considered eliminated from the processes involving the
selected population. Exclusion of entities of the selected
population from these processes may also be dependent upon a level
or degree of match to the entities of the data repository 100, such
as matching to an individual, a household, or merely a geographic
or postal code area.
[0149] Also in summary, in the various embodiments, the plurality
of predictive message content classifications are determined by:
developing a plurality of empirical attitudinal factors based on a
factor analysis of an attitudinal survey of the sample population;
using each empirical attitudinal factor of the plurality of
empirical attitudinal factors, scoring each participant of the
attitudinal survey to create a corresponding plurality of empirical
attitudinal factor scores; using a plurality of selected variables
from the data repository as independent variables, and using the
corresponding plurality of empirical attitudinal factor scores as
dependent variables, performing a regression analysis to create the
plurality of predictive attitudinal models; and using each
predictive attitudinal model of the plurality of predictive
attitudinal models, scoring the plurality of entities represented
in the data repository, as the reference population, to create the
plurality of predictive message content classifications. The
plurality of predictive attitudinal classifications are determined
by a cluster analysis of the plurality of predictive message
content classifications of each entity of the plurality of entities
represented in the data repository.
[0150] The invention also provides for determining core, niche and
growth attitudinal classifications, as follows: determining one or
more core attitudinal classifications by selecting, from the
plurality of predictive attitudinal classifications, at least one
predictive attitudinal classification having a comparatively
greater (e.g., average or above average) penetration index and
having a comparatively greater proportion of the selected
population; determining one or more niche attitudinal
classifications by selecting, from the plurality of predictive
attitudinal classifications, at least one predictive attitudinal
classification having a comparatively greater penetration index and
having a comparatively lesser proportion of the reference
population; and determining one or more growth attitudinal
classifications by selecting, from the plurality of predictive
attitudinal classifications, at least one predictive attitudinal
classification having a comparatively lesser (e.g., below average)
penetration index and having a comparatively greater proportion of
the reference population.
[0151] Numerous advantages of the present invention are readily
apparent. The embodiments of the present invention provide a
predictive methodology, system and software, for accurate
prediction of attitudes, motivations and behaviors, which may be
utilized for marketing, research, assessment, and other
applications. The embodiments of the invention are
empirically-based upon actual attitudinal research and other
information from a population sample, and provide accurate modeling
to predict and extrapolate such attitudinal information to a larger
reference population. In addition to identifying to "whom" a
communication should be directed, the embodiments of the invention
further provide information concerning the "what" of the
communication, such as the preferred message themes or message
content, independently from any population grouping, segmentation
or clustering process. In addition, the embodiments of the
invention provide actionable results, providing not only audience
attitudinal information and preferred message content, but also
preferred communication channel or media information, communication
frequency, communication timing information, and communication
sequencing information.
[0152] From the foregoing, it will be observed that numerous
variations and modifications may be effected without departing from
the spirit and scope of the novel concept of the invention. It is
to be understood that no limitation with respect to the specific
methods and apparatus illustrated herein is intended or should be
inferred. It is, of course, intended to cover by the appended
claims all such modifications as fall within the scope of the
claims.
* * * * *