OPTIMIZING PROMOTIONAL OFFER MIXES USING PREDICTIVE MODELING
A method, system, and computer program product for promotional offer spend management. A computer implementation commences upon performing calculations to predict future market response of presenting a particular offer to an audience. The prediction calculations include machine-learning processing of historical offer specification parameters that at least partially characterize the offer. The offer specification comprises a unique combination of parameters and respective media channels, which combination was not measured as pertains to that particular specific unique combination. A predicted market response to the unique offer delivered being over selected media channels is forecasted by using a predictive model.
1 . A computer implemented method comprising:
executing, on a computer, an offer management application to operate on at least one management interface device;
storing, in a computer, a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users across a plurality of media channels;
storing, in a computer, a plurality of historical response vectors that characterize one or more performance metrics of the users exposed to the touchpoint encounters;
processing, using machine-learning techniques in a computer, the touchpoint encounters and the historical response vectors to generate at least one stimulus attribution predictive model comprising stimulus attribution predictive model input variables, wherein the stimulus attribution predictive model predicts market performance of a future marketing message based on the stimulus attribution predictive model input variables;
executing, on one or more computers, to predict marketing performance of at least one offer as a marketing message, operations comprising:
receiving a plurality of offer specification parameters that characterize the offer to be presented over at least one of the media channels, wherein the offer specification parameters comprise a unique combination of parameters not presented in the touchpoint encounters and the historical response vectors;
mapping at least one of the offer specification parameters to at least one of the stimulus attribution predictive model input variables; and
generating a predicted response to the offer by executing the stimulus attribution predictive model using the offer specification parameters to corresponding inputs of the stimulus attribution predictive model.
2 . The computer implemented method of claim 1 , further comprising delivering the predicted response to the management interface device to be displayed to a user.
3 . The computer implemented method of claim 1 , further comprising receiving, over a network, from the management interface device, a selected media spend plan.
4 . The computer implemented method of claim 1 , further comprising selecting, responsive to receiving the plurality of offer specification parameters, at least one of, additional touchpoint encounters, or additional historical response vectors, or any combination thereof.
5 . The computer implemented method of claim 4 , wherein the selecting is based at least in part on a historical period.
6 . The computer implemented method of claim 1 , further comprising determining one or more simulated offer mix performance measurements based at least in part on the predicted response.
7 . The computer implemented method of claim 1 , further comprising determining one or more predicted media spend allocation response parameters based at least in part on the predicted response.
8 . The computer implemented method of claim 7 , wherein the predicted media spend allocation response parameters are further based at least in part on relative media spend allocation for the one or more offers.
9 . The computer implemented method of claim 1 , wherein mapping of at least one of the offer specification parameters to at least one of the stimulus attribution predictive model input variables is based at least in part on a set of offer specification rules.
10 . The computer implemented method of claim 1 , wherein at least one of the offer specification parameters is associated with at least one of, a duration, or an activation fee, or a monthly rate, or a discount, or a product, or a package, or a geography, or any combination thereof.
11 . A computer readable medium, embodied in a non-transitory computer readable medium, the non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor causes the processor to perform a set of acts, the acts comprising:
executing, on a computer, an offer management application to operate on at least one management interface device;
storing, in a computer, a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users across a plurality of media channels;
storing, in a computer, a plurality of historical response vectors that characterize one or more performance metrics of the users exposed to the touchpoint encounters;
processing, using machine-learning techniques in a computer, the touchpoint encounters and the historical response vectors to generate at least one stimulus attribution predictive model comprising stimulus attribution predictive model input variables, wherein the stimulus attribution predictive model predicts market performance of a future marketing message based on the stimulus attribution predictive model input variables;
executing, on one or more computers, to predict marketing performance of at least one offer as a marketing message, operations comprising:
receiving a plurality of offer specification parameters that characterize the offer to be presented over at least one of the media channels, wherein the offer specification parameters comprise a unique combination of parameters not presented in the touchpoint encounters and the historical response vectors;
mapping at least one of the offer specification parameters to at least one of the stimulus attribution predictive model input variables; and
generating a predicted response to the offer by executing the stimulus attribution predictive model using the offer specification parameters to corresponding inputs of the stimulus attribution predictive model.
12 . The computer readable medium of claim 11 , further comprising instructions which, when stored in memory and executed by the processor causes the processor to perform acts of delivering the predicted response to the management interface device to be displayed to a user.
13 . The computer readable medium of claim 11 , further comprising instructions which, when stored in memos and executed by the processor causes the processor to perform acts of receiving, over a network, from the management interface device, a selected media spend plan.
14 . The computer readable medium of claim 11 , further comprising instructions which, when stored in memory and executed by the processor causes the processor to perform acts of selecting, responsive to receiving the plurality of offer specification parameters, at least one of, additional touchpoint encounters, OF additional historical response vectors, or any combination thereof.
15 . The computer readable medium of claim 14 , wherein the selecting is based at least in part on a historical period.
16 . The computer readable medium of claim 11 , further comprising instructions which, when stored in memory and executed by the processor causes the processor to perform acts of determining one or more simulated offer mix performance measurements based at least in part on the predicted response.
17 . The computer readable medium of claim 11 , further comprising instructions which, when stored in memory and executed by the processor causes the processor to perform acts of determining one or more predicted media spend allocation response parameters based at least in part on the predicted response.
18 . A system comprising:
a storage medium having stored thereon a sequence of instructions; and
a processor or processors that execute the instructions to cause the processor or processors to perform a set of acts, the acts comprising,
executing an offer management application to operate on at least one management interface device;
storing, a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users across a plurality of media channels;
storing, a plurality of historical response vectors that characterize one or more performance metrics of the users exposed to the touchpoint encounters;
processing, using machine-learning techniques, the touchpoint encounters and the historical response vectors to generate at least one stimulus attribution predictive model comprising stimulus attribution predictive model input variables, wherein the stimulus attribution predictive model predicts market performance of a future marketing message based on the stimulus attribution predictive model input variables;
executing, instructions for performing acts to predict marketing performance of at least one offer as a marketing message, the acts comprising at least:
receiving a plurality of offer specification parameters that characterize the offer to be presented over at least one of the media channels, wherein the offer specification parameters comprise a unique combination of parameters not presented in the touchpoint encounters and the historical response vectors;
mapping at least one of the offer specification parameters to at least one of the stimulus attribution predictive model input variables; and
generating a predicted response to the offer by executing the stimulus attribution predictive model using the offer specification parameters to corresponding inputs of the stimulus attribution predictive model.
19 . The system of claim 18 , wherein the selecting is based at least in part on a historical period.
20 . The system of claim 18 , wherein predicted media spend allocation response parameters are based at least in part on relative media spend allocation for the one or more offers.