Advertising cannibalization management

Abstract

A system and method for advertising cannibalization management are provided. In example embodiments, historical data comprising advertisement revenue, advertisement parameters, and a cannibalization metric are accessed. The cannibalization metric is indicative of sales loss associated with an advertisement presentation. A value for at least one of the advertisement parameters that, when used, causes a desired advertisement revenue with respect to a bounded cannibalization metric is determined by analyzing the historical data. An advertisement is presented, in real time, on a user interface of a client device using the determined value.

What is claimed is: 
     
         1 . A system comprising:
 a data module to access historical data that comprises advertisement revenue, advertisement parameters, and a cannibalization metric that is indicative of sales loss associated with an advertisement presentation;   an analysis module, implemented by at least one hardware processor of a machine, to determine a value for at least one of the advertisement parameters by an analysis of the historical data, use of the determined value causes a desired advertisement revenue with respect to a bounded cannibalization metric; and   a presentation module to cause presentation, in real time, of an advertisement on a user interface of a client device using the determined value.   
     
     
         2 . The system of  claim 1 , wherein the analysis module is further to:
 identify a cannibalization covariate from among candidate covariates that include the advertisement parameters;   generate a covariate model that models the cannibalization covariate with respect to the advertisement parameters;   generate a cannibalization model that models the cannibalization metric with respect to the cannibalization covariate;   generate a revenue model that models the advertisement revenue with respect to the advertisement parameters; and   determine the value for at least one of the advertisement parameters using the revenue model in conjunction with the cannibalization model and the covariate model.   
     
     
         3 . The system of  claim 1 , wherein the cannibalization metric comprises at least one of a purchase per user per week metric (PPW) and a gross revenue per user per week metric (GPW). 
     
     
         4 . The system of  claim 1 , wherein the advertisement parameters include at least one of an advertisement placement, impressions, clicks, an advertiser, and an advertisement type. 
     
     
         5 . A method comprising:
 accessing historical data comprising advertisement revenue, advertisement parameters, and a cannibalization metric that is indicative of sales loss associated with an advertisement presentation;   determining a value for at least one of the advertisement parameters by analyzing the historical data, use of the determined value causes a desired advertisement revenue with respect to a bounded cannibalization metric; and   causing presentation, in real time, of an advertisement on a user interface of a client device using the determined value.   
     
     
         6 . The method of  claim 5 , wherein the analyzing the historical data further comprises:
 identifying a cannibalization covariate from among candidate covariates that include the advertisement parameters;   generating a covariate model that models the cannibalization covariate with respect to the advertisement parameters;   generating a cannibalization model that models the cannibalization metric with respect to the cannibalization covariate;   generating a revenue model that models the advertisement revenue with respect to the advertisement parameters; and   determining the value for at least one of the advertisement parameters using the revenue model in conjunction with the cannibalization model and the covariate model.   
     
     
         7 . The method of  claim 6 , wherein the identifying the cannibalization covariate further comprises:
 measuring a cannibalization value by comparing cannibalization of a control group of users shown advertisements and a treatment group of users not shown advertisements; and   identifying the cannibalization covariate from among the candidate covariates according to a correlation between respective candidate covariates and the measured cannibalization value, the cannibalization covariate being a highest correlated covariate among the candidate covariates.   
     
     
         8 . The method of  claim 7 , wherein the candidate covariates include at least one of clicks, impressions, and page views. 
     
     
         9 . The method of  claim 5 , wherein the cannibalization metric comprises at least one of a purchase per user per week metric (PPW) and a gross revenue per user per week metric (GPW). 
     
     
         10 . The method of  claim 5 , wherein the advertisement parameters include at least one of an advertisement placement, impressions, clicks, an advertiser, and an advertisement type. 
     
     
         11 . The method of  claim 5 , further comprising:
 determining a lower limit for the bounded cannibalization metric according to a minimum advertisement revenue specified by an operator; and   determining an upper limit for the bounded cannibalization metric according to a maximum cannibalization cost specified by the operator.   
     
     
         12 . The method of  claim 5 , further comprising:
 accessing current data comprising the advertisement revenue, the advertisement parameters, and the cannibalization metric for a time period of a duration;   determining a change amount for at least one of the advertisement parameters by analyzing the historical data in conjunction with the current data; and   causing presentation, in real time, of the advertisement on the user interface of the client device using the determined change amount for another time period of the duration.   
     
     
         13 . A machine-readable medium having no transitory signals and storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:
 accessing historical data comprising advertisement revenue, advertisement parameters, and a cannibalization metric that is indicative of sales loss associated with an advertisement presentation;   determining a value for at least one of the advertisement parameters by analyzing the historical data, use of the determined value causes a desired advertisement revenue with respect to a bounded cannibalization metric; and   causing presentation, in real time, of an advertisement on a user interface of a client device using the determined value.   
     
     
         14 . The machine-readable medium of  claim 13 , wherein the analyzing the historical data further comprises:
 identifying a cannibalization covariate from among the candidate covariates that include the advertisement parameters;   generating a covariate model that models the cannibalization covariate with respect to the advertisement parameters;   generating a cannibalization model that models the cannibalization metric with respect to the cannibalization covariate;   generating a revenue model that models the advertisement revenue with respect to the advertisement parameters; and   determining the value for at least one of the advertisement parameters using the revenue model in conjunction with the cannibalization model and the covariate model.   
     
     
         15 . The machine-readable medium of  claim 14 , wherein the operations further comprise the identifying the cannibalization covariate by:
 measuring a cannibalization value by comparing cannibalization of a control group of users shown advertisements and a treatment group of users not shown advertisements; and   identifying the cannibalization covariate from among the candidate covariates according to a correlation between respective candidate covariates and the measured cannibalization value, the cannibalization covariate being a highest correlated covariate among the candidate covariates.   
     
     
         16 . The machine-readable medium of  claim 15 , wherein the candidate covariates include at least one of clicks, impressions, and page views. 
     
     
         17 . The machine-readable medium of  claim 13 , wherein the cannibalization metric comprises at least one of a purchase per user per week metric (PPW) and a gross revenue per user per week metric (GPW). 
     
     
         18 . The machine-readable medium of  claim 13 , wherein the advertisement parameters include at least one of an advertisement placement, impressions, clicks, an advertiser, and an advertisement type. 
     
     
         19 . The machine-readable medium of  claim 13 , wherein the operations further comprise:
 determining a lower limit for the bounded cannibalization metric according to a minimum advertisement revenue specified by an operator; and   determining an upper limit for the bounded cannibalization metric according to a maximum cannibalization cost specified by the operator.   
     
     
         20 . The machine-readable medium of  claim 13 , further comprising:
 accessing current data comprising the advertisement revenue, the advertisement parameters, and the cannibalization metric for a time period of a duration;   determining a change amount for at least one of the advertisement parameters by analyzing the historical data in conjunction with the current data; and   causing presentation, in real time, of the advertisement on the user interface of the client device using the determined change amount for another time period of the duration.