Reallocation of electronic resources using a predictive model of attribution

Abstract

Systems and methods for predictive modeling of attribution are described. Systems and methods may include receiving one or more inputs; processing the one or more inputs using a general linear model; and providing predicted online and offline campaign impact.

1 . A computerized method of predictive modeling of attribution, the computerized method comprising the steps of:
 selecting a first set of control electronic mail recipients;   selecting a second set of treatment electronic mail recipients;   transmitting, by a computing server, a first electronic mail message to first remote computing devices associated with the first set of control electronic mail recipients using a data communication network;   transmitting, by the computing server, a second electronic mail message containing an element of a messaging campaign to second remote computing devices associated with the second treatment set of electronic mail recipients;   receiving, by the computing server in real time over the communication network, first data indicating interactions of the first set of control electronic mail recipients with the first electronic mail message and the second data indicating interactions of the second set of treatment electronic mail recipients with the second electronic mail message;   receiving, by the computing server, customer information including match information;   generating, by the computing server, campaign-related attribution data using the customer information and a difference between the first data and the second data in response to one or both of the first data and the second data satisfying the match information;   based on the difference between the first data and the second data, generating an incremental new customer rate of a campaign to which the campaign-related attribution data relates;   generating a product of the second set of treatment electronic mail recipients and the incremental new customer rate to identify a set of new customers generated by the campaign;   generating an incremental lift by comparing response rates from the first set of control electronic mail recipients and the second set of treatment electronic mail recipients;   acquiring, by the computing server, real time campaign data, audience data, and attribution data from one or more databases, and adding the campaign-related attribution data to the attribution data, the real time campaign data including rate of change of metric rates;   generating, by the computing server, a general linear model having one or more inputs from each of the real time campaign data, the audience data, and the attribution data as independent input variables in the general linear model; and   predicting in real time, by the computing server, an online and offline campaign impact of the messaging campaign using the general linear model with the campaign-related attribution data as part of the independent input variables to determine differences in performance between the first set of electronic mail recipients and the second set of electronic mail recipients, the predicted campaign impact including data relating to at least one or more of the incremental new customer rate, the set of new customers, and the incremental lift;   transmitting, by the computing server, a third electronic mail message based on the predicting.   
     
     
         2 . The method of  claim 1 , wherein selecting the first set of control electronic mail recipients and the second set of treatment electronic mail recipients includes using a sampling process such that attributes of the first set and the second set are proportional to each other. 
     
     
         3 . The method of  claim 1 , wherein the real time campaign data is further selected from the group consisting of: opens, clicks, landing page actions, complaints, unsubscribes, metrics rates, dates, times, and combinations thereof. 
     
     
         4 . The method of  claim 1 , wherein the audience profiles are selected from the group consisting of:
 demographics, geographic, online sales, offline sales, psychographic, purchase intent data, and combinations thereof.   
     
     
         5 . The method of  claim 1 , wherein the attribution data is selected from the group consisting of:
 advertiser customer data, treated prospects records, control prospects records, incremental customers, incremental customer rate, and combinations thereof.   
     
     
         6 . The method of  claim 1 , wherein the match information includes a time range for completion of an event with respect to the transmission of the second electronic mail message containing the element of the messaging campaign. 
     
     
         7 . The method of  claim 1 , wherein the method includes varying values of the real time campaign data, the audience data, and the attribution data that are input as independent input variables in the general linear model to determine weights indicating an importance of each of the real time campaign data, the audience data and the attribution data. 
     
     
         8 . The method of  claim 7 , wherein the general linear model determines influential factors. 
     
     
         9 . The method of  claim 1 , wherein the predicted online and offline campaign impact is determined on a weekly basis. 
     
     
         10 . A system for predictive modeling of online and offline attribution, the system comprising:
 one or more databases;   system memory comprising instructions; and   one or more processors to execute the instructions to perform operations comprising:   selecting a first set of control electronic mail recipients;   selecting a second set of treatment electronic mail recipients;   transmitting a first electronic mail message to first remote computing devices associated with the first set of control electronic mail recipients using a data communication network;   transmitting a second electronic mail message containing an element of a messaging campaign to second remote computing devices associated with the second set of treatment electronic mail recipients;   receiving, in real time over the communication network, first data indicating interactions of the first set of control electronic mail recipients with the first electronic mail message and the second data indicating interactions of the second set of treatment electronic mail recipients with the second electronic mail message;   receiving customer information including match information;   generating campaign-related attribution data using the customer information and a difference between the first data and the second data in response to one or both of the first data and the second data satisfying the match information;   acquiring real time campaign data, audience data, and attribution data from the one or more databases, and adding the campaign-related attribution data to the attribution data, the real time campaign data including rate of change of metric rates;   generating a general linear model having one or more inputs from each of the real time campaign data, the audience data, and the attribution data as independent input variables in the general linear model; and   predicting in real time an online and offline campaign impact of the messaging campaign using the general linear model with the campaign-related attribution data as part to the input independent input variables to determine differences in performance between the first set of electronic mail recipients and the second set of electronic mail recipients;   transmitting, by the computing server, a third electronic mail message based on the predicting.   
     
     
         11 . The system of  claim 10 , wherein selecting the first set of control electronic mail recipients and the second set of treatment electronic mail recipients includes using a sampling process such that attributes of the first set and the second set are proportional to each other. 
     
     
         12 . The system of  claim 10 , wherein the real time campaign data is further selected from the group consisting of: opens, clicks, landing page actions, complaints, unsubscribes, metrics rates, dates, times, and combinations thereof. 
     
     
         13 . The system of  claim 10 , wherein the audience profiles are selected from the group consisting of: demographics, geographic, online sales, offline sales, psychographic, purchase intent data, and combinations thereof. 
     
     
         14 . The system of  claim 10 , wherein the attribution data is selected from the group consisting of: advertiser customer data, treated prospects records, control prospects records, incremental customers, incremental customer rate, and combinations thereof. 
     
     
         15 . The system of  claim 10 , wherein the match information includes a time range for completion of an event with respect to the transmission of the second electronic mail message containing the element of the messaging campaign. 
     
     
         16 . The system of  claim 15 , wherein the operations include varying values of the real time campaign data, the audience data, and the attribution data that are input as independent input variables in the general linear model to determine weights indicating an importance of each of the real time campaign data, the audience data and the attribution data. 
     
     
         17 . The system of  claim 16 , wherein the general linear model determines influential factors. 
     
     
         18 . The system of  claim 10 , wherein the predicted online and offline campaign impact is determined on a weekly basis.