Media spend optimization using a cross-channel predictive model

Abstract

A method, system, and computer program product for advertising portfolio management. The method form processes steps for determining effectiveness of marketing stimulations in a plurality of marketing channels included in a marketing campaign. The method commences upon receiving data comprising a plurality of marketing stimulations and respective measured responses, then determining from the marketing stimulations and the respective measured responses, a set of cross-channel weights to apply to the respective measured responses, where the cross-channel weights are indicative of the influence that a particular stimulation applied to a first channel has on the measure responses of other channels. The cross-channel weights are used in calculating the effectiveness of a particular marketing stimulation over an entire marketing campaign. The marketing campaign can comprise stimulations quantified as a number of direct mail pieces, a number or frequency of TV spots, a number of web impressions, a number of coupons printed, etc.

What is claimed is: 
     
         1 . A computer-implemented method for determining effectiveness of marketing stimulations in a plurality of marketing channels, the computer-implemented method comprising:
 receiving data comprising a plurality of marketing stimulations and respective measured responses;   determining, from the marketing stimulations and the respective measured responses, cross-channel weights to apply to the respective measured responses; and   calculating an effectiveness value of a particular one of the marketing stimulations using the cross-channel weights.   
     
     
         2 . The method of  claim 1 , wherein the marketing stimulations comprise at least one of, an advertising spend, a number of direct mail pieces, a number of TV spots, a number of radio spots, a number of web impressions, and a number of coupons printed. 
     
     
         3 . The method of  claim 1 , further comprising processing the marketing stimulations and respective measured responses to form a learning model. 
     
     
         4 . The method of  claim 3 , further comprising using the learning model to predict a portion of a response in a second channel resulting from a stimulus in a first channel. 
     
     
         5 . The method of  claim 4  wherein using the learning model to predict a portion of a response in a second channel resulting from a stimulus in a first channel comprises running a plurality of simulations. 
     
     
         6 . The method of  claim 5  wherein individual ones of the plurality of simulations comprise varying the stimulus in a first channel and observing the response in the second channel. 
     
     
         7 . The method of  claim 5 , further comprising outputting a simulated model. 
     
     
         8 . The method of  claim 7 , further comprising using the simulated model to generate one or more reports based on a user scenario. 
     
     
         9 . The method of  claim 1 , further comprising determining a portion of aggregate response that is not attributed to aggregate stimulus. 
     
     
         10 . A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process, the process comprising:
 receiving data comprising a plurality of marketing stimulations and respective measured responses;   determining, from the marketing stimulations and the respective measured responses, cross-channel weights to apply to the respective measured responses; and   calculating an effectiveness value of a particular one of the marketing stimulations using the cross-channel weights.   
     
     
         11 . The computer program product of  claim 10 , wherein the marketing stimulations comprise at least one of, an advertising spend, a number of direct mail pieces, a number of TV spots, a number of radio spots, a number of web impressions, and a number of coupons printed. 
     
     
         12 . The computer program product of  claim 10 , further comprising instructions for processing the marketing stimulations and respective measured responses to form a learning model. 
     
     
         13 . The computer program product of  claim 12 , further comprising instructions for using the learning model to predict a portion of a response in a second channel resulting from a stimulus in a first channel. 
     
     
         14 . The computer program product of  claim 13  wherein using the learning model to predict a portion of a response in a second channel resulting from a stimulus in a first channel comprises running a plurality of simulations. 
     
     
         15 . The computer program product of  claim 14  wherein individual ones of the plurality of simulations comprise varying the stimulus in a first channel and observing the response in the second channel. 
     
     
         16 . The computer program product of  claim 15 , further comprising instructions for outputting a simulated model. 
     
     
         17 . The computer program product of  claim 16 , further comprising instructions for using the simulated model to generate one or more reports based on a user scenario. 
     
     
         18 . The computer program product of  claim 10 , further comprising determining a portion of aggregate response that is not attributed to aggregate stimulus. 
     
     
         19 . A computer system comprising:
 a computer processor to execute a set of program code instructions; and   a memory to hold the program code instructions, in which the program code instructions comprises program code to perform,   receiving data comprising a plurality of marketing stimulations and respective measured responses;   determining, from the marketing stimulations and the respective measured responses, cross-channel weights to apply to the respective measured responses; and   calculating an effectiveness value of a particular one of the marketing stimulations using the cross-channel weights.   
     
     
         20 . The computer system of  claim 19 , wherein the marketing stimulations comprise at least one of, an advertising spend, a number of direct mail pieces, a number of TV spots, a number of radio spots, a number of web impressions, and a number of coupons printed.