Method and system for media initialization via data sharing

Abstract

A method, apparatus, and computer-readable medium estimate media performance on advertising space inventory. The method selects at least one media cell that shares one or more common attributes with a target media cell. The method subsequently estimates mean revenue per impression (RPI) of the selected media cell, and then defines an initial estimate of a RPI of the target media cell based on the estimated RPI of the selected cell. The method computes the RPI of the target media cell by combining the initial RPI estimate for the target media cell with performance data associated with the target media cell.

1 - 34 . (canceled) 
     
     
         35 . A computer-implemented method, comprising the following operations performed by at least one processor:
 obtaining an approximation of an error associated with a mean revenue per impression of a first media cell;   determining whether the error approximation exceeds a threshold value associated with a revenue model of the first media cell;   when the error approximation does not exceed the threshold value, calculating a mean revenue per impression of the first media cell based on a distribution of revenue per impression for the first media cell; and   predicting a revenue per impression of a second media cell based on the calculated mean revenue per impression of the first media cell.   
     
     
         36 . The method of  claim 35 , wherein:
 the first media cell corresponding to an advertisement disposed on a first portion of advertising space inventory; and   the second media cell corresponds to a disposition of the advertisement on a second portion of the advertising space inventory.   
     
     
         37 . The method of  claim 35 , wherein the first and second media cells share one or more common attributes, the common attributes comprising at least one of a common tract of advertising space inventory, a common revenue model, a common slot size, a common segmentation model, a common media, or a common industry campaign. 
     
     
         38 . The method of  claim 35 , wherein the obtaining comprises generating the error approximation based on a number of impressions associated with the first media cell. 
     
     
         39 . The method of  claim 38 , wherein the generating comprises:
 calculating a factor indicative of a revenue model of the first media cell and a geographic segmentation of the first media cell; and   computing the error approximation based on at least one of the number of impressions associated with the first media cell or a ratio of (i) a cost per unit of media associated with the first media cell and (ii) the calculated factor.   
     
     
         40 . The method of  claim 35 , further comprising determining the distribution of revenue per impression for the first media cell based on at least one of a cost per unit of media associated with the first media cell, the number of impressions associated with the first media cell, or a number of events associated with the first media cell. 
     
     
         41 . The method of  claim 35 , further comprising calculating the mean revenue per impression of the first media cell based on a relationship between a plurality of attributes of one or more additional media cells and an estimated mean revenue per impression of the one or more additional media cells. 
     
     
         42 . The method of  claim 41 , wherein the plurality of attributes comprise at least one of a website, a slot size, or a segmentation model associated with each additional media cell. 
     
     
         43 . The method of  claim 35 , wherein the predicting comprises computing a weighted average of (i) the estimated mean revenue per impression of the first media cell and (ii) measurements of revenue per impression for the second media cell. 
     
     
         44 . The method of  claim 35 , wherein the calculating comprises adjusting the mean revenue per impression of the first media cell to account for one or more of audience leadback and advertiser leadback. 
     
     
         45 . An apparatus, comprising:
 a storage device; and   at least one processor coupled to the storage device, wherein the storage device stores a program for controlling the at least one processor, and wherein the at least one processor, being operative with the program, is configured to:
 obtain an approximation of an error associated with a mean revenue per impression of a first media cell; 
 determine whether the error approximation exceeds a threshold value associated with a revenue model of the first media cell; 
 when the error approximation does not exceed the threshold value, calculate a mean revenue per impression of the first media cell based on a distribution of revenue per impression for the first media cell; and 
 predict a revenue per impression of a second media cell based on the calculated mean revenue per impression of the first media cell. 
   
     
     
         46 . The apparatus of  claim 45 , wherein:
 the first media cell corresponding to an advertisement disposed on a first portion of advertising space inventory; and   the second media cell corresponds to a disposition of the advertisement on a second portion of the advertising space inventory.   
     
     
         47 . The apparatus of  claim 45 , wherein the first and second media cells share one or more common attributes, the common attributes comprising at least one of a common tract of advertising space inventory, a common revenue model, a common slot size, a common segmentation model, a common media, or a common industry campaign. 
     
     
         48 . The apparatus of  claim 45 , wherein the at least one processor is further configured to generate the error approximation based on a number of impressions associated with the first media cell. 
     
     
         49 . The apparatus of  claim 48 , wherein the at least one processor is further configured to:
 calculate a factor indicative of a revenue model of the first media cell and a geographic segmentation of the first media cell; and   compute the error approximation based on at least one of the number of impressions associated with the first media cell or a ratio of (i) a cost per unit of media associated with the first media cell and (ii) the calculated factor.   
     
     
         50 . The apparatus of  claim 45 , wherein the at least one processor is further configured to determine the distribution of revenue per impression for the first media cell based on at least one of a cost per unit of media associated with the first media cell, the number of impressions associated with the first media cell, or a number of events associated with the first media cell. 
     
     
         51 . The apparatus of  claim 45 , wherein:
 the at least one processor is further configured to calculate, when the error approximation exceeds the threshold value, the mean revenue per impression of the first media cell based on a relationship between a plurality of attributes of one or more additional media cells and an estimated mean revenue per impression of the one or more additional media cells; and   the plurality of attributes comprise at least one of a website, a slot size, or a segmentation model associated with each additional media cell.   
     
     
         52 . The apparatus of  claim 45 , wherein the at least one processor is further configured to predict the revenue per impression of the second media cell based on a weighted average of (i) the estimated mean revenue per impression of the first media cell and (ii) measurements of revenue per impression for the second media cell. 
     
     
         53 . The apparatus of  claim 45 , wherein the at least one processor is further configured to adjust the mean revenue per impression of the first media cell to account for one or more of audience leadback and advertiser leadback. 
     
     
         54 . A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method comprising:
 obtaining an approximation of an error associated with a mean revenue per impression of a first media cell;   determining whether the error approximation exceeds a threshold value associated with a revenue model of the first media cell;   when the error approximation does not exceed the threshold value, calculating a mean revenue per impression of the first media cell based on a distribution of revenue per impression for the first media cell; and   predicting a revenue per impression of a second media cell based on the calculated mean revenue per impression of the first media cell.