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 comprising:
 receiving, using a computer, historical data that comprises stimuli data that represent a plurality of stimuli deployed through a plurality of channels to a plurality of users, the stimuli data comprising the stimuli and a time for deployment of the stimuli, the historical data further comprising response data that measures responses to the stimuli by the users and times the responses occurred;   generating, in a computer, from the stimuli data, a plurality of “n” time series stimuli vectors, SV n , that characterizes the stimuli with a plurality of values taken over a historical time period for each of the “n” stimuli vectors;   generating, in a computer, from the response data, a plurality of “n” time series response vectors, RV n , one for each of the corresponding “n” time series stimuli vectors, SV n , that characterizes the responses to the stimuli with a plurality of values over the historical time period;   generating a cross-channel predictive model, using machine-learning techniques in a computer, by identifying, for each of the “n” time series stimuli vectors, SV i , time and value based correlations between SV i  and each of the time series response vectors, RV j , wherein “i” and “j” comprise integers from 1 to “n” and “i” is not equal to “j”, and by generating a plurality of cross-channel contributions that specify, for the time series stimuli vectors, SV n , a weight that quantifies the contribution of the time series stimuli vectors, SV i , to the time series response vectors, RV j , and   executing, in a computer, using the cross-channel predictive model, a simulation to change at least one of the values of the stimuli, SV i , associated with a first channel, and to predict, using at least one of the weights for the cross-channel contributions for one or more time series response vectors, RV j , associated with a second channel, at least one cross-channel response, wherein the first and second channels comprise different channels.   
     
     
         2 . The computer-implemented method as set forth in  claim 1 , wherein generating a cross-channel predictive model, using machine-learning techniques in a computer further comprises training a set of exogenous variables into the cross-channel predictive model to attenuate effects independent from the stimuli. 
     
     
         3 . The computer-implemented method as set forth in  claim 1 , further comprising:
 receiving, in a computer, a range of values to vary the stimuli in at least one of the channels; and   executing, in the computer, a simulation, using the cross-channel predictive model, that sweeps across the range of values as the stimuli and produces additional weights for the cross-channel contributions.   
     
     
         4 . The computer-implemented method as set forth in  claim 1 , further comprises generating a report that quantifies the return on investment for at least one of the channels that accounts for the cross-channel contributions. 
     
     
         5 . The computer-implemented method as set forth in  claim 1 , wherein generating a cross-channel predictive model, using machine-learning techniques in a computer, further comprises:
 filtering the weights so as to eliminate small cross-channel weights, statistically insignificant cross-channel weights and statistically outlying cross-channel weights.   
     
     
         6 . The computer-implemented method as set forth in  claim 1 , wherein generating a plurality of “n” link series stimuli vectors and a plurality of “n” time series response vectors comprises introducing at least one delay between the “n” time series stimuli vectors and the “n” time series response vectors. 
     
     
         7 . The computer-implemented method as set forth in  claim 1 , wherein generating a cross-channel predictive model, using machine-learning techniques in a computer, comprises generating the cross-channel predictive model in accordance with the expression: 
       
         
           
             
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         8 . The computer-implemented method as set forth in  claim 1 , wherein generating a cross-channel predictive model comprises generating a cross-channel predictive model with only a portion of the stimuli data. 
     
     
         9 . 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, using a computer, historical data that comprises stimuli data that represent a plurality of stimuli deployed through a plurality of channels to a plurality of users, the stimuli data comprising the stimuli and a time for deployment of the stimuli, the historical data further comprising response data that measures responses to the stimuli by the users and times the responses occurred;   generating, in a computer, from the stimuli data, a plurality of “n” time series stimuli vectors, SV n , that characterizes the stimuli with a plurality of values taken over a historical time period for each of the “n” stimuli vectors;   generating, in a computer, from the response data, a plurality of “n” time series response vectors, RV n , one for each of the corresponding “n” time series stimuli vectors, SV n , that characterizes the responses to the stimuli with a plurality of values over the historical time period;   generating a cross-channel predictive model, using machine-learning techniques in a computer, by identifying, for each of the “n” time series stimuli vectors, SV i , time and value based correlations between SV i  and each of the time series response vectors, RV j , wherein “i” and “j” comprise integers from 1 to “n” and “i” is not equal to “j”, and by generating a plurality of cross-channel contributions that specify, for the time series stimuli vectors, SV n , a weight that quantifies the contribution of the time series stimuli vectors, SV i , to the time series response vectors, RV j ; and   executing, in a computer, using the cross-channel predictive model, a simulation to change at least one of the values of the stimuli, SV i , associated with a first channel, and to predict, using at least one of the weights for the cross-channel contributions for one or more time series response vectors, RV j , associated with a second channel, at least one cross-channel response, wherein the first and second channels comprise different channels.   
     
     
         10 . The computer program product as set forth in  claim 9 , wherein generating a cross-channel predictive model, using machine-learning techniques in a computer further comprises training a set of exogenous variables into the cross-channel predictive model to attenuate effects independent from the stimuli. 
     
     
         11 . The computer program product as set forth in  claim 9 , further comprising:
 receiving, in a computer, a range of values to vary the stimuli in at least one of the channels; and   executing, in the computer, a simulation, using the cross-channel predictive model, that sweeps across the range of values as the stimuli and produces additional weights for the cross-channel contributions.   
     
     
         12 . The computer program product as set forth in  claim 9 , further comprises generating a report that quantifies the return on investment for at least one of the channels that accounts for the cross-channel contributions. 
     
     
         13 . The computer program product as set forth in  claim 9 , wherein generating a cross-channel predictive model, using machine-learning techniques in a computer, further comprises:
 filtering the weights so as to eliminate small cross-channel weights, statistically insignificant cross-channel weights and statistically outlying cross-channel weights.   
     
     
         14 . The computer program product as set forth in  claim 9 , wherein generating a plurality of “n” time series stimuli vectors and a plurality of “n” time series response vectors comprises introducing at least one delay between the “n” time series stimuli vectors and the “n” time series response vectors. 
     
     
         15 . The computer program product as set forth in  claim 9 , wherein generating a cross-channel predictive model using machine-learning techniques in a computer, comprises generating the cross-channel predictive model in accordance with the expression: 
       
         
           
             
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       where:
 x represents components of the time series stimuli data, 
 y represents components of the time series response data, and 
 n is the number of {x, y} pairs. 
 
     
     
         16 . The computer program product as set forth in  claim 9 , wherein generating a cross-channel predictive model comprises generating a cross-channel predictive model with only a portion of the stimuli data. 
     
     
         17 . 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, using a computer, historical data that comprises stimuli data that represent a plurality of stimuli deployed through a plurality of channels to a plurality of users, the stimuli data comprising the stimuli and a time for deployment of the stimuli, the historical data further comprising response data that measures responses to the stimuli by the users and times the responses occurred;   generating, in a computer, from the stimuli data, a plurality of “n” time series stimuli vectors, SV n , that characterizes the stimuli with a plurality of values taken over a historical time period for each of the “n” stimuli vectors;   generating, in a computer, from the response data, a plurality of “n” time series response vectors, RV n , one for each of the corresponding “n” time series stimuli vectors, SV n , that characterizes the responses to the stimuli with a plurality of values over the historical time period;   generating a cross-channel predictive model, using machine-learning techniques in a computer, by identifying, for each of the “n” time series stimuli vectors, SV i , time and value based correlations between SV i  and each of the time series response vectors, RV j , wherein “i” and “j” comprise integers from 1 to “n” and “i” is not equal to “j”, and by generating a plurality of cross-channel contributions that specify, for the time series stimuli vectors, SV n , a weight that quantifies the contribution of the time series stimuli vectors, SV i , to the time series response vectors, RV j ; and   executing, in a computer, using the cross-channel predictive model, a simulation to change at least one of the values of the stimuli, SV i , associated with a first channel, and to predict, using at least one of the weights for the cross-channel contributions for one or more time series response vectors, RV j , associated with a second channel at least one cross-channel response, wherein the first and second channels comprise different channels.   
     
     
         18 . The computer system as set forth in  claim 17 , wherein generating a cross-channel predictive model, using machine-learning techniques in a computer further comprises training a set of exogenous variables into the cross-channel predictive model to attenuate effects independent from the stimuli. 
     
     
         19 . The computer system as set forth in  claim 17 , further comprising:
 receiving, in a computer, a range of values to vary the stimuli in at least one of the channels; and   executing, in the computer, a simulation, using the cross-channel predictive model, that sweeps across the range of values as the stimuli and produces additional weights for the cross-channel contributions.   
     
     
         20 . The computer system as set forth in  claim 17 , further comprises generating a report that quantifies the return on investment for at least one of the channels that accounts for the cross-channel contributions.