Maximizing advertising performance

Abstract

A target value of a performance metric associated with an advertising schedule can be obtained at an automated media content scheduling system. An upper boundary value indicating a value of the performance metric at which corrective action to decrease a predicted value of the performance metric is to be taken can be determined. A lower boundary value indicating a value of the performance metric at which corrective action to increase the predicted value of the performance metric is to be taken can also be determined. Information indicating the predicted value of the performance metric can be received at the automated media content scheduling system. The advertising schedule can be continually adjusted to maintain the predicted value of the performance metric between the upper boundary value and the lower boundary value.

What is claimed is: 
     
         1 . A method for implementation in an automated media content scheduling system including a memory and processor programmed to implement the method, the method comprising:
 obtaining, at the automated media content scheduling system, a target value of a performance metric associated with an advertising schedule;   determining, at the automated media content scheduling system, an upper boundary value indicating a value of the performance metric at which corrective action to decrease a predicted value of the performance metric is to be taken;   determining, at the automated media content scheduling system, a lower boundary value indicating a value of the performance metric at which corrective action to increase the predicted value of the performance metric is to be taken;   receiving, at the automated media content scheduling system, information indicating the predicted value of the performance metric; and   continually adjusting an advertising schedule to maintain the predicted value of the performance metric between the upper boundary value and the lower boundary value.   
     
     
         2 . The method of  claim 1 , wherein at least one of the upper boundary value and the lower boundary value is an outlier boundary. 
     
     
         3 . The method of  claim 1 , wherein at least one of the upper boundary value and the lower boundary value is a guidance boundary. 
     
     
         4 . The method of  claim 1 , wherein the performance metric is a gross rating point (GRP) metric. 
     
     
         5 . The method of  claim 4 , further comprising:
 generating a plurality of predicted gross rating point (GRP) values for an advertisement by filtering historic data using multiple different sets of parameters; and   continually adjusting the advertising schedule to maintain at least one of the predicted GRP values between the upper boundary value and the lower boundary value.   
     
     
         6 . The method of  claim 5 , further comprising:
 generating a predicted GRP trend line based on the plurality of predicted GRP values; and   continually adjusting the advertising schedule to maintain the predicted GRP trend line between the upper boundary value and the lower boundary value.   
     
     
         7 . The method of  claim 1 , further comprising:
 determining a relative rating of a plurality of spot breaks, within a particular market, based on a predicted value of the performance metric associated with individual spot breaks; and   continually adjusting the advertising schedule by moving an advertisement from a first spot break to a second spot break based on the relative rating of the first spot break and the second spot break.   
     
     
         8 . An automated media content scheduling system comprising:
 a processor;   a memory coupled to the processor;   a program of instructions stored in the memory and configured to be executed by the processor, the program of instructions including:   at least one instruction to obtain a target value of a performance metric associated with an advertising schedule;   at least one instruction to determine an upper boundary value indicating a value of the performance metric at which corrective action to decrease a predicted value of the performance metric is to be taken;   at least one instruction to determine a lower boundary value indicating a value of the performance metric at which corrective action to increase the predicted value of the performance metric is to be taken;   at least one instruction to receive information indicating the predicted value of the performance metric; and   at least one instruction to continually adjust an advertising schedule to maintain the predicted value of the performance metric between the upper boundary value and the lower boundary value.   
     
     
         9 . The automated media content scheduling system of  claim 8 , wherein at least one of the upper boundary value and the lower boundary value is an outlier boundary. 
     
     
         10 . The automated media content scheduling system of  claim 8 , wherein at least one of the upper boundary value and the lower boundary value is a guidance boundary. 
     
     
         11 . The automated media content scheduling system of  claim 8 , wherein the performance metric is a gross rating point (GRP) metric. 
     
     
         12 . The automated media content scheduling system of  claim 11 , further comprising:
 at least one instruction to generate a plurality of predicted gross rating point (GRP) values for an advertisement by filtering historic data using multiple different sets of parameters; and   at least one instruction to continually adjust the advertising schedule to maintain at least one of the predicted GRP values between the upper boundary value and the lower boundary value.   
     
     
         13 . The automated media content scheduling system of  claim 12 , further comprising:
 at least one instruction to generate a predicted GRP trend line based on the plurality of predicted GRP values; and   at least one instruction to continually adjust the advertising schedule to maintain the predicted GRP trend line between the upper boundary value and the lower boundary value.   
     
     
         14 . The automated media content scheduling system of  claim 8 , further comprising:
 at least one instruction to determine a relative rating of a plurality of spot breaks, within a particular market, based on a predicted value of the performance metric associated with individual spot breaks; and   at least one instruction to continually adjust the advertising schedule by moving an advertisement from a first spot break to a second spot break based on the relative rating of the first spot break and the second spot break.   
     
     
         15 . A non-transitory computer readable medium tangibly embodying a program of instructions configured to be executed by a processor, the program of instructions including:
 at least one instruction to obtain a target value of a performance metric associated with an advertising schedule;   at least one instruction to determine an upper boundary value indicating a value of the performance metric at which corrective action to decrease a predicted value of the performance metric is to be taken;   at least one instruction to determine a lower boundary value indicating a value of the performance metric at which corrective action to increase the predicted value of the performance metric is to be taken;   at least one instruction to receive information indicating the predicted value of the performance metric; and   at least one instruction to continually adjust an advertising schedule to maintain the predicted value of the performance metric between the upper boundary value and the lower boundary value.   
     
     
         16 . The non-transitory computer readable medium of  claim 15 , wherein at least one of the upper boundary value and the lower boundary value is an outlier boundary. 
     
     
         17 . The non-transitory computer readable medium of  claim 15 , wherein at least one of the upper boundary value and the lower boundary value is a guidance boundary. 
     
     
         18 . The non-transitory computer readable medium of  claim 15 , further comprising:
 at least one instruction to generate a plurality of predicted values of the performance metric for an advertisement by filtering historic data using multiple different sets of parameters; and   at least one instruction to continually adjust the advertising schedule to maintain at least one of the plurality of predicted values of the performance metric between the upper boundary value and the lower boundary value.   
     
     
         19 . The non-transitory computer readable medium of  claim 18 , further comprising:
 at least one instruction to generate a trend line based on the plurality of predicted values of the performance metric; and   at least one instruction to continually adjust the advertising schedule to maintain the trend line between the upper boundary value and the lower boundary value.   
     
     
         20 . The non-transitory computer readable medium of  claim 15 , further comprising:
 at least one instruction to determine a relative rating of a plurality of spot breaks, within a particular market, based on a predicted value of the performance metric associated with individual spot breaks; and   at least one instruction to continually adjust the advertising schedule by moving an advertisement from a first spot break to a second spot break based on the relative rating of the first spot break and the second spot break.