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
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.
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.