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 for independently predicting communication responsiveness of a selected population of a plurality of entities represented in a data repository, the method 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;
(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.
2 . The method of claim 1 , further comprising:
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.
3 . The method of claim 1 , further comprising:
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.
4 . The method of claim 1 , further comprising:
for each predictive identification classification of the plurality of predictive identification classifications, independently determining at least one predominant predictive communication frequency classification of a plurality of predictive communication frequency classifications.
5 . The method of claim 1 , further comprising:
for each predictive identification classification of the plurality of predictive identification classifications, independently determining at least one predominant predictive communication sequencing classification of a plurality of predictive communication sequencing classifications.
6 . The method of claim 1 , further comprising:
determining a penetration index for each predictive identification classification of the plurality of predictive identification classifications;
determining at least one core predictive identification classification by selecting, from the plurality of predictive identification classifications, at least one predictive identification classification having a comparatively greater penetration index and having a comparatively greater proportion of the selected population;
determining at least one niche predictive identification classification by selecting, from the plurality of predictive identification classifications, at least one predictive identification classification having a comparatively greater penetration index and having a comparatively lesser proportion of the reference population; and
determining at least one growth predictive identification classification by selecting, from the plurality of predictive identification classifications, at least one predictive identification classification having a comparatively lesser penetration index and having a comparatively greater proportion of the reference population.
7 . The method of claim 1 , wherein 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.
8 . A system for independently predicting communication responsiveness of a selected population of a plurality of entities, the system comprising:
a data repository storing, for each entity of a plurality of entities forming a reference population, a corresponding predictive identification classification of a plurality of predictive identification classifications and at least one corresponding predictive message version classification of a plurality of predictive message version classifications, wherein the plurality of predictive identification classifications designate the plurality of entities according to a selected property, and 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
a processor coupled to the data repository, the processor adapted to determine from the data repository, for the selected population, at least one predominant predictive identification classification of the plurality of predictive identification classifications, and for each predictive identification classification of the plurality of predictive identification classifications, to independently determine at least one predominant predictive message version classification from the plurality of predictive message version classifications.
9 . The system of claim 8 , wherein the processor is further adapted to independently determine, for each predictive identification classification of the plurality of predictive identification classifications, at least one predominant predictive communication media classification of a plurality of predictive communication media classifications.
10 . The system of claim 8 , wherein the processor is further adapted to independently determine, for each predictive identification classification of the plurality of predictive identification classifications, at least one predominant predictive communication timing classification of a plurality of predictive communication timing classifications.
11 . The system of claim 8 , wherein the processor is further adapted to independently determine, for each predictive identification classification of the plurality of predictive identification classifications, at least one predominant predictive communication frequency classification of a plurality of predictive communication frequency classifications.
12 . The system of claim 8 , wherein the processor is further adapted to independently determine, for each predictive identification classification of the plurality of predictive identification classifications, at least one predominant predictive communication sequencing classification of a plurality of predictive communication sequencing classifications.
13 . The system of claim 8 , wherein 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.
14 . A data structure for independently predicting communication responsiveness of a selected population of a plurality of entities represented in a data repository, the data structure comprising:
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.
15 . The data structure of claim 14 , further comprising:
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.
16 . The data structure of claim 14 , further comprising:
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.
17 . The data structure of claim 14 , further comprising:
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.
18 . The data structure of claim 14 , further comprising:
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.
19 . The data structure of claim 14 , further comprising:
a seventh field having a penetration index for each predictive identification classification of the plurality of predictive identification classifications.
20 . The data structure of claim 14 , wherein 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.
21 . The data structure of claim 14 , wherein the data structure is stored in a database.
22 . The data structure of claim 14 , wherein the data structure is embodied as an electronic transmission.
23 . The data structure of claim 14 , wherein the data structure is embodied in a tangible medium.