System and method for creating segmentation of a population

Abstract

A system including a processor may filter a population to identify a group having members, and deliver, over an electronic network, a survey to members of the group to determine value preferences and value gaps. The survey may be made according to a set of paired comparisons or other related techniques. The system may generate vectors according to the determined value preferences and value gaps for members of the group, which may have lengths equal to numbers of determined values for the members. The system may cluster members of the group into segments by calculating patterns of differences between generated vectors. Results of this process enable the creation of a predictive model to estimate segment membership, which can be used in the generation of a promotion for display corresponding to a clustered segment, and deliver the promotion to a client device associated for display.

What is claimed is: 
     
         1 . A system for creating a segmentation of a population, the system comprising:
 a memory storing instructions; and   a processor configured to execute the instructions to:
 filter a population to identify a group having members; 
 deliver, over an electronic network, a survey to a first set of members to determine value preferences and value gaps for the members; 
 receive, over an electronic network, results of the survey; 
 generate vectors according to the determined value preferences and value gaps, the vectors having lengths equal to numbers of determined values for the members; 
 cluster the first set of members into multiple segments by calculating differences between the vectors; 
 create a predictive model to estimate segment membership of a second set of the members using the results of the survey; 
 generate a promotion for display corresponding to a clustered segment using the predictive model; and 
 deliver the promotion to a client device associated for display. 
   
     
     
         2 . The system of  claim 1 , wherein the processor is further configured to execute the instructions to:
 compare at least one vector to a predetermined threshold to determine if the value preference and value gap of the at least one vector are both positive; and   cluster into a segment at least two vectors whose value preferences and value gaps are both positive.   
     
     
         3 . The system of  claim 1 , wherein the processor is further configured to execute the instructions to:
 generate a latent variable model to determine an interval nature and ranking of the value preferences and value gaps.   
     
     
         4 . The system of  claim 3 , wherein the processor is further configured to execute the instructions to:
 generate a Bradley-Terry-Luce (BTL) model to analyze relative strengths of the value preferences and value gaps.   
     
     
         5 . The system of  claim 1 , wherein the processor is further configured to execute the instructions to:
 cluster the first set of members into multiple consumer segments according to at least one of demographics, channel preferences, product experience, media usage, attitudes, behaviors, or psychographics.   
     
     
         6 . The system of  claim 1 , wherein filtering the population further comprises filtering the members based on at least one of age, gender, socio-economic status, or demographics. 
     
     
         7 . The system of  claim 1 , wherein the survey is directed to a set of paired comparisons, and the processor is further configured to execute the instructions to analyze relative strengths of the value preferences and value comparisons based on the set of paired comparisons. 
     
     
         8 . A method for creating segmentation of a population, comprising a memory and a processors configured to execute instructions, the method comprising:
 filtering a population to identify a group having members;   delivering, over an electronic network, a survey to a first set the members to determine value preferences and value gaps for the members;   receiving, over an electronic network, results of the survey;   generating vectors according to the determined value preferences and value gaps, the vectors having lengths equal to numbers of determined values for each member;   clustering the first set of members into multiple segments by calculating differences between the vectors;   creating a predictive model to estimate segment membership of a second set of the members using the results of the survey;   generating a promotion for display corresponding to a clustered segment using the predictive model; and   delivering the promotion to a client device associated for display.   
     
     
         9 . The method according to  claim 8 , the method further comprising:
 comparing at least one vector to a predetermined threshold to determine if the value preference and value gap of the at least one vector are both positive; and   cluster into a segment at least two vectors whose value preferences and value gaps are both positive.   
     
     
         10 . The method according to  claim 8 , the method further comprising:
 generating a latent variable model to determine an interval nature and ranking of the value preferences and value gaps.   
     
     
         11 . The method according to  claim 10 , the method further comprising:
 generating a Bradley-Terry-Luce (BTL) model to analyze relative strengths of the value preferences and value gaps.   
     
     
         12 . The method according to  claim 8 , the method further comprising:
 clustering the first set of members into multiple consumer segments according to at least one of demographics, channel preferences, product experience, media usage, attitudes, behaviors, or psychographics.   
     
     
         13 . The method according to  claim 8 , wherein filtering the population further comprises filtering the members based on at least one of age, gender, socio-economic status, or demographics. 
     
     
         14 . The method according to  claim 8 , wherein the survey is directed to a set of paired comparisons, and the method further comprises analyzing relative strengths of the value preferences and value comparisons based on the set of paired comparisons. 
     
     
         15 . A non-transitory computer-readable medium storing instructions executable a processor to perform a method for creating a segmentation of a population, the method comprising:
 filtering a population to identify a group having members;   delivering, over an electronic network, a survey to a first set of the members to determine value preferences and value gaps for the members;   receiving, over an electronic network, results of the survey;   generating vectors according to the determined value preferences and value gaps, the vectors having lengths equal to numbers of determined values for the members;   clustering the first set of members into multiple segments by calculating differences between the vectors;   creating a predictive model to estimate segment membership of a second set of members of the group using the results of the survey;   generating a promotion for display corresponding to a clustered segment using the predictive model; and   delivering the promotion to a client device associated for display.   
     
     
         16 . The non-transitory computer readable medium according to  claim 15 , further comprising:
 comparing at least one vector to a predetermined threshold to determine if the value preference and value gap of the at least one vector are both positive; and   cluster into a segment at least two vectors whose value preferences and value gaps are both positive.   
     
     
         17 . The non-transitory computer readable medium according to  claim 15 , further comprising:
 generating a latent variable model to determine an interval nature and ranking of the value preferences and value gaps.   
     
     
         18 . The non-transitory computer readable medium according to  claim 17 , further comprising:
 generating a Bradley-Terry-Luce (BTL) model to analyze relative strengths of the value preferences and value gaps.   
     
     
         19 . The non-transitory computer readable medium according to  claim 15 , further comprising:
 clustering the first set of members into multiple consumer segments according to at least one of demographics, channel preferences, product experience, media usage, attitudes, behaviors, or   psychographics.   
     
     
         20 . The non-transitory computer readable medium according to  claim 15 , wherein filtering the population further comprises filtering the members based on at least one of age, gender, socio-economic status, or demographics.