Methods and apparatus for campaign mapping for total audience measurement

Abstract

Example methods and apparatus disclosed herein include campaign mapping for total audience measurement. An example apparatus includes processor circuitry to train a machine learning model to determine first and second estimated duplication factors for respective first and second reference media campaigns; and determine, using the machine learning model, third estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of media platforms for the query media campaign. The processor circuitry to select one of the first and second reference media campaigns based on a comparison of the third estimated duplication factors with each of the first and second estimated duplication factors; and determine fourth estimated duplication factors for the query media campaign based on (a) the respective first or second estimated duplication factors associated with the selected one of the first and second reference media campaigns and (b) the total exposure metrics.

What is claimed is: 
     
         1 . An apparatus comprising:
 memory;   instructions; and   processor circuitry to execute the instructions to:
 train a machine learning model to determine first and second estimated duplication factors for respective first and second reference media campaigns, the first and second estimated duplication factors corresponding to estimated measures of duplicated media exposure across different possible combinations of media platforms for the respective first and second reference media campaigns; 
 determine, using the machine learning model, third estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of the media platforms for the query media campaign, the query media campaign corresponding to a campaign for which duplication of media exposure across the different possible combinations of the media platforms is unknown; 
 select one of the first and second reference media campaigns based on a comparison of the third estimated duplication factors with each of the first and second estimated duplication factors; and 
 determine fourth estimated duplication factors for the query media campaign based on (a) the respective first or second estimated duplication factors associated with the selected one of the first and second reference media campaigns and (b) the total exposure metrics. 
   
     
     
         2 . The apparatus of  claim 1 , wherein the total exposure metrics are third total exposure metrics, and the machine learning model is trained using (a) first and second total exposure metrics associated with individual ones of the media platforms for the respective first and second reference media campaigns, and (b) first and second actual duplication factors for the respective first and second reference media campaigns, the first and second actual duplication factors different than the first and second estimated duplication factors. 
     
     
         3 . The apparatus of  claim 2 , wherein the processor circuitry is to:
 generate a first reference media estimated duplication factor for a first combination of the media platforms based on the first and second total exposure metrics and the first and second actual duplication factors; and   generate a second reference media estimated duplication factor for a second combination of the media platforms based on the first and second total exposure metrics and the first and second actual duplication factors, the second combination different than the first combination, both the first and second reference media estimated duplication factor associated with a combination of features common to both the first and second combinations of the media platforms.   
     
     
         4 . The apparatus of  claim 3 , wherein the features include at least one of demographics, a media campaign time step, a media platform reach, or a digital duplicated reach, the digital duplicated reach determined based on a combination of desktop reach and mobile reach. 
     
     
         5 . The apparatus of  claim 3 , wherein the processor circuitry is to:
 generate a first regression model based on the first reference media estimated duplication factor;   generate a second regression model based on the second reference media estimated duplication factor; and   train the first and second regression models using K-fold cross validation.   
     
     
         6 . The apparatus of  claim 3 , wherein the processor circuitry is to generate a query media estimated duplication factor based on the query media campaign and on the combination of the features common to both the first and second combinations of the media platforms. 
     
     
         7 . The apparatus of  claim 1 , wherein the processor circuitry is to select the one of the first and second reference media campaigns based on a Euclidean distance to the query media campaign, the Euclidean distance based on a KD tree. 
     
     
         8 . A non-transitory computer readable medium comprising instruction that, when executed, cause a machine to at least:
 train a machine learning model to determine first and second estimated duplication factors for respective first and second reference media campaigns, the first and second estimated duplication factors corresponding to estimated measures of duplicated media exposure across different possible combinations of media platforms for the respective first and second reference media campaigns;   determine, using the machine learning model, third estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of the media platforms for the query media campaign, the query media campaign corresponding to a campaign for which duplication of media exposure across the different possible combinations of the media platforms is unknown;   select one of the first and second reference media campaigns based on a comparison of the third estimated duplication factors with each of the first and second estimated duplication factors; and   determine fourth estimated duplication factors for the query media campaign based on (a) the respective first or second estimated duplication factors associated with the selected one of the first and second reference media campaigns and (b) the total exposure metrics.   
     
     
         9 . The non-transitory computer readable medium of  claim 8 , wherein the total exposure metrics are third total exposure metrics, and the machine learning model is trained using (a) first and second total exposure metrics associated with individual ones of the media platforms for the respective first and second reference media campaigns, and (b) first and second actual duplication factors for the respective first and second reference media campaigns, the first and second actual duplication factors different than the first and second estimated duplication factors. 
     
     
         10 . The non-transitory computer readable medium of  claim 9 , wherein the instructions cause the machine to:
 generate a first reference media estimated duplication factor for a first combination of the media platforms based on the first and second total exposure metrics and the first and second actual duplication factors; and   generate a second reference media estimated duplication factor for a second combination of the media platforms based on the first and second total exposure metrics and the first and second actual duplication factors, the second combination different than the first combination, both the first and second reference media estimated duplication factor associated with a combination of features common to both the first and second combinations of the media platforms.   
     
     
         11 . The non-transitory computer readable medium of  claim 10 , wherein the features include at least one of demographics, a media campaign time step, a media platform reach, or a digital duplicated reach, the digital duplicated reach determined based on a combination of desktop reach and mobile reach. 
     
     
         12 . The non-transitory computer readable medium of  claim 10 , wherein the instructions cause the machine to:
 generate a first regression model based on the first reference media estimated duplication factor;   generate a second regression model based on the second reference media estimated duplication factor; and   train the first and second regression models using K-fold cross validation.   
     
     
         13 . The non-transitory computer readable medium of  claim 10 , wherein the instructions cause the machine to generate a query media estimated duplication factor based on the query media campaign and on the combination of the features common to both the first and second combinations of the media platforms. 
     
     
         14 . The non-transitory computer readable medium of  claim 8 , wherein the instructions cause the machine to select the one of the first and second reference media campaigns based on a Euclidean distance to the query media campaign, the Euclidean distance based on a KD tree. 
     
     
         15 . An apparatus comprising:
 memory; and   means for determining duplication factors for media campaigns, the means for determining to:
 train a machine learning model to determine first and second estimated duplication factors for respective first and second reference media campaigns, the first and second estimated duplication factors corresponding to estimated measures of duplicated media exposure across different possible combinations of media platforms for the respective first and second reference media campaigns; 
 determine, using the machine learning model, third estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of the media platforms for the query media campaign, the query media campaign corresponding to a campaign for which duplication of media exposure across the different possible combinations of the media platforms is unknown; 
 select one of the first and second reference media campaigns based on a comparison of the third estimated duplication factors with each of the first and second estimated duplication factors; and 
 determine fourth estimated duplication factors for the query media campaign based on (a) the respective first or second estimated duplication factors associated with the selected one of the first and second reference media campaigns and (b) the total exposure metrics. 
   
     
     
         16 . The apparatus of  claim 15 , wherein the total exposure metrics are third total exposure metrics, and the machine learning model is trained using (a) first and second total exposure metrics associated with individual ones of the media platforms for the respective first and second reference media campaigns, and (b) first and second actual duplication factors for the respective first and second reference media campaigns, the first and second actual duplication factors different than the first and second estimated duplication factors. 
     
     
         17 . The apparatus of  claim 16 , wherein the means for determining is to:
 generate a first reference media estimated duplication factor for a first combination of the media platforms based on the first and second total exposure metrics and the first and second actual duplication factors; and   generate a second reference media estimated duplication factor for a second combination of the media platforms based on the first and second total exposure metrics and the first and second actual duplication factors, the second combination different than the first combination, both the first and second reference media estimated duplication factor associated with a combination of features common to both the first and second combinations of the media platforms.   
     
     
         18 . The apparatus of  claim 17 , wherein the features include at least one of demographics, a media campaign time step, a media platform reach, or a digital duplicated reach, the digital duplicated reach determined based on a combination of desktop reach and mobile reach. 
     
     
         19 . The apparatus of  claim 17 , wherein the means for determining is to generate a query media estimated duplication factor based on the query media campaign and on the combination of the features common to both the first and second combinations of the media platforms. 
     
     
         20 . The apparatus of  claim 15 , wherein the means for determining is to select the one of the first and second reference media campaigns based on a Euclidean distance to the query media campaign, the Euclidean distance based on a KD tree.