Process and System for Integrating Information from Disparate Databases for Purposes of Predicting Consumer Behavior

Abstract

A process and system for integrating information stored in at least two disparate databases. The stored information includes consumer transactional information. According to the process and system, at least one qualitative variable which is common to each database is identified, and then transformed into one or more quantitative variables. The consumer transactional information in each database is then converted into converted information in terms of the quantitative variables. Thereafter, an integrated database is formed for predicting consumer behavior by combining the converted information from the disparate databases.

We claim: 
     
         1 . A computer-implemented method for predicting consumer behavior from information stored in a plurality of separate databases, the computer-implemented method comprising:
 using a processing device, accessing a first database of the plurality of separate databases, the first database comprising a first plurality of data variables associated with survey data obtained from a first plurality of consumers;   using a processing device, accessing a second database of the plurality of separate databases, the second database comprising a second plurality of data variables associated with transaction data of a second plurality of consumers, wherein the first plurality of data variables includes data variables that are not included in the second plurality of data variables, and wherein at least some consumers of the second plurality of consumers are not in the first plurality of consumers;   using a processing device, performing a cluster analysis across the first and second databases to form a plurality of clusters, at least one cluster of the plurality of clusters containing at least some individuals of the first and second databases that are not in both the first and second databases;   using a processing device, for a selected cluster of the plurality of clusters, associating a plurality of behavioral characteristics of the consumers of the first plurality of consumers associated with the selected cluster with the consumers of the second plurality of consumers associated with the selected cluster; and   using a processing device, predicting a behavior of one or more consumers of the second plurality of consumers associated with a selected cluster of the plurality of clusters based at least in part on the plurality of behavioral characteristics of the consumers of the first plurality of consumers associated with the selected cluster.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising:
 identifying at least one qualitative data variable which is in both the first plurality of data variables and the second plurality of data variables;   transforming the at least one qualitative data variable into a plurality of quantitative variables;   using a processing device, differentially weighting the plurality of quantitative variables;   using a processing device, converting at least some of the survey data and transaction data according to the according to the differentially weighted plurality of quantitative variables to form converted information for use in the cluster analysis.   
     
     
         3 . The computer-implemented method of  claim 2 , wherein the step of differentially weighting the plurality of quantitative variables further comprises:
 differentially weighting the plurality of quantitative variables to adjust for differences in numbers of transactions and in time periods encompassed.   
     
     
         4 . The computer-implemented method of  claim 2 , wherein the plurality of behavioral characteristics are not included in the qualitative and quantitative variables. 
     
     
         5 . The computer-implemented method of  claim 2 , wherein the at least one qualitative variable is a commercial entity and the plurality of quantitative variables comprises two or more of the quantitative variables selected from the group consisting of: mean number of transactions per person for the commercial entity, mean amount per transaction for the commercial entity, mean household income of consumers purchasing from the commercial entity, mean proportion of consumers for a particular geographic region of the commercial entity, mean proportion of consumers for a plurality of counties having different population sizes of a particular geographic region of the commercial entity, and combinations thereof. 
     
     
         6 . The computer-implemented method of  claim 2 , further comprising:
 using a processing device, determining at least one discriminating subset of the plurality of quantitative variables using a root mean squared standard statistic to create one or more statistical drivers; and   using a processing device, evaluating the pluralities of consumers represented in the plurality of separate databases using the one or more statistical drivers.   
     
     
         7 . The computer-implemented method of  claim 6 , wherein the determining step further comprises:
 identifying one or more industries which have discriminating consumers and grouping selected commercial entities into the at least one discriminating subset.   
     
     
         8 . The computer-implemented method of  claim 1 , further comprising:
 converting one or more clusters of the plurality of clusters into at least one super cluster; and   assigning the consumers of the first and second pluralities of consumers to a corresponding cluster or super cluster using data from each database of the plurality of separate databases.   
     
     
         9 . The computer-implemented method of  claim 1 , wherein the information stored in at least the second database further comprises instances of purchasing behavior by consumers. 
     
     
         10 . A computer system for predicting consumer behavior from information stored in a plurality of separate databases, the computer system comprising:
 one or more data storage devices to store a first database of the plurality of separate databases and a second database of the plurality of separate databases, the first database comprising a first plurality of data variables associated with survey data obtained from a first plurality of consumers; the second database comprising a second plurality of data variables associated with transactions of a second plurality of consumers, wherein the first plurality of data variables includes data variables that are not included in the second plurality of data variables, and wherein at least some consumers of the second plurality of consumers are not in the first plurality of consumers; and   one or more processing devices coupled to the one or more data storage devices, the one or more processing devices to perform a cluster analysis across the first and second databases to form a plurality of clusters, at least one cluster of the plurality of clusters containing at least some individuals of the first and second databases that are not in both the first and second databases; for a selected cluster of the plurality of clusters, associate a plurality of behavioral characteristics of the consumers of the first plurality of consumers associated with the selected cluster with the consumers of the second plurality of consumers associated with the selected cluster; and predict a behavior of one or more consumers of the second plurality of consumers associated with a selected cluster of the plurality of clusters based at least in part on the plurality of behavioral characteristics of the consumers of the first plurality of consumers associated with the selected cluster.   
     
     
         11 . The computer-implemented method of  claim 10 , wherein the one or more processing devices further is to identify at least one qualitative data variable which is in both the first plurality of data variables and the second plurality of data variables; transform the at least one qualitative data variable into a plurality of quantitative variables; convert at least some of the survey data and transaction data according to the plurality of quantitative variables to form converted information; using the converted information, to differentially weight the plurality of quantitative variables and convert at least some of the survey data and transaction data according to the differentially weighted plurality of quantitative variables to form converted information for use in the cluster analysis. 
     
     
         12 . The computer system of  claim 11 , wherein the one or more processing devices further is to differentially weight the plurality of quantitative variables to adjust for differences in numbers of transactions and in time periods encompassed. 
     
     
         13 . The computer-implemented method of  claim 11 , wherein the plurality of behavioral characteristics are not included in the qualitative and quantitative variables. 
     
     
         14 . The computer system of  claim 11 , wherein the at least one qualitative variable is a commercial entity and the plurality of quantitative variables comprises two or more of the quantitative variables selected from the group consisting of: mean number of transactions per person for the commercial entity, mean amount per transaction for the commercial entity, mean household income of consumers purchasing from the commercial entity, mean proportion of consumers for a particular geographic region of the commercial entity, mean proportion of consumers for a plurality of counties having different population sizes of a particular geographic region of the commercial entity, and combinations thereof. 
     
     
         15 . The computer system of  claim 11 , wherein the one or more processing devices further is to determine at least one discriminating subset of the plurality of quantitative variables using a root mean squared standard statistic to create one or more statistical drivers; and evaluate the pluralities of consumers represented in the plurality of separate databases using the one or more statistical drivers. 
     
     
         16 . The computer system of  claim 15 , wherein the one or more processing devices further is to identify one or more industries which have discriminating consumers and group selected commercial entities into the at least one discriminating subset. 
     
     
         17 . The computer system of  claim 11 , wherein the one or more processing devices further is to perform a principal components analysis on the plurality of quantitative variables using the information stored in each separate database of the plurality of separate and disparate databases to create a plurality of statistical drivers, and to convert the information stored in the plurality of separate databases according to the plurality of statistical drivers to create the converted information. 
     
     
         18 . The computer system of  claim 11 , wherein the one or more processing devices further is to validate the plurality of clusters as a discriminatory behavioral model for predicting consumer behavior and determine whether the plurality of clusters provides corresponding discrimination on a plurality of other variables which are not statistical drivers in the plurality of separate databases. 
     
     
         19 . The computer system of  claim 10 , wherein the information stored in the plurality of separate databases further comprises one or more of types of information selected from the group consisting of: behavioral and attitudinal information; consumer transactional information; media consumption information; instances of purchasing behavior by consumers; survey information; self-reported behavioral information; and combinations thereof. 
     
     
         20 . A non-transient computer readable medium storing software code for execution on a computing system comprising one or more processors to cause the computing system to perform a method for predicting consumer behavior from information stored in a plurality of separate databases, the computer-implemented method comprising:
 using a processing device, accessing a first database of the plurality of separate databases, the first database comprising a first plurality of data variables associated with survey data obtained from a first plurality of consumers;   using a processing device, accessing a second database of the plurality of separate databases, the second database comprising a second plurality of data variables associated with transaction data of a second plurality of consumers, wherein the first plurality of data variables includes data variables that are not included in the second plurality of data variables, and wherein at least some consumers of the second plurality of consumers are not in the first plurality of consumers;   using a processing device, performing a cluster analysis across the first and second databases to form a plurality of clusters, at least one cluster of the plurality of clusters containing at least some individuals of the first and second databases that are not in both the first and second databases;   using a processing device, for a selected cluster of the plurality of clusters, associating a plurality of behavioral characteristics of the consumers of the first plurality of consumers associated with the selected cluster with the consumers of the second plurality of consumers associated with the selected cluster; and   using a processing device, predicting a behavior of one or more consumers of the second plurality of consumers associated with a selected cluster of the plurality of clusters based at least in part on the plurality of behavioral characteristics of the consumers of the first plurality of consumers associated with the selected cluster.