Method and apparatus for return on investment impact reporting

Abstract

The method, apparatus and computer program product described herein is configured to train and deploy a predictive model that is configured to generate a predicted ROI value for a provider with respect to a current promotion or a future promotion. An example embodiment may comprise receiving input indicative of one or more attributes of a provider or a promotion. The example embodiment may further comprise generating at least one of a predicted return on investment (ROI) value or a predicted ROI component value based at least in part on the one or more attributes of the provider or the promotion and a ROI prediction model. The method may further still comprise generating a merchant impact report including the at least one of the predicted ROI value or the predicted ROI component value for the promotion.

1 - 78 . (canceled) 
     
     
         79 . A method for utilizing at least a processor, a memory, and a display device for rendering a graphical user interface (GUI) comprising:
 receiving, via the GUI, an input indicative of one or more metrics relating to a merchant and a promotion;   generating, by the processor, a return on investment (ROI) learning model based on the one or more metrics relating to the merchant and the promotion;   updating, by the processor, the ROI learning model by automatically inputting a second one or more metrics relating to the merchant and the promotion, wherein the second one or more metrics relating to the merchant and the promotion are determined based on at least one or more historical metrics retrieved from a promotion repository;   generating, by the processor, a repeat revenue visual metric for the promotion, wherein the repeat business revenue visual metric is indicative of revenue predicted to be generated from one or more repeat business transactions associated with the promotion based on the updated ROI learning model;   generating, by the processor, a merchant impact report, comprising the repeat business revenue visual metric; and   displaying, via the GUI, the merchant impact report.   
     
     
         80 . The method of  claim 79 , further comprising:
 generating, via the processor, a set of clusters based on one or more similar return ROI learning models previously generated.   
     
     
         81 . The method of  claim 79 , further comprising:
 determining a source-specific classifier;   inputting the one or more metrics relating to the merchant and the promotion into the source-specific classifier;   inputting the second one or more metrics relating to the merchant and the promotion into the source-specific classifier; and   determining, via the processor, an ROI or an ROI component.   
     
     
         82 . The method of  claim 79 , wherein the one or more metrics or the second one or more metrics are generated based at least in part on one or more of a survey, a marketing exposure, a financial engineering transaction, an in-store transaction, or a webpage transaction. 
     
     
         83 . The method of  claim 79 , wherein the merchant impact report comprises one or more amounts indicative of a revenue from the promotion or a cost of the promotion, wherein the one or more amounts are determined based on the one or more metrics relating to the merchant and the promotion. 
     
     
         84 . The method of  claim 79 , wherein the return on investment (ROI) learning model is a support vector machine, decision tree learning, association rule learning, artificial neural networking, inductive logic programming, or clustering. 
     
     
         85 . The method of  claim 79 , further comprising:
 generating, via the processor, a dataset based on the one or more historical metrics;   comparing, via the processor, the return on investment (ROI) learning model to the dataset;   training, via the processor, the return on investment (ROI) learning model to classify a particular metric value, determined by a combination of metrics, as indicative of a positive ROI; and   storing, via the processor and the memory, the combination of metrics used as a predictor for a number of consumers who will return to the merchant after a first visit using the promotion.   
     
     
         86 . An apparatus comprising:
 a processor;   a memory including computer program code; and   a display device for rendering a graphical user interface (GUI), the memory and the computer program code configured to, with the processor, cause the apparatus to at least:   receive, via the GUI, an input indicative of one or more metrics relating to a merchant and a promotion;   generate, by the processor, a return on investment (ROI) learning model based on the one or more metrics relating to the merchant and the promotion;   update, by the processor, the ROI learning model by automatically inputting a second one or more metrics relating to the merchant and the promotion, wherein the second one or more metrics relating to the merchant and the promotion are determined based on at least one or more historical metrics retrieved from a promotion repository;   generate, by the processor, a repeat revenue visual metric for the promotion, wherein the repeat business revenue visual metric is indicative of revenue predicted to be generated from one or more repeat business transactions associated with the promotion based on the updated ROI learning model;   generate, by the processor, a merchant impact report, comprising the repeat business revenue visual metric; and   display, via the GUI, the merchant impact report.   
     
     
         87 . The apparatus of  claim 86 , wherein the at least one memory including the computer program code is further configured to, with the at least one processor, cause the apparatus to:
 generate, via the processor, a set of clusters based on one or more similar return ROI learning models previously generated.   
     
     
         88 . The apparatus of  claim 86 , wherein the at least one memory including the computer program code is further configured to, with the at least one processor, cause the apparatus to:
 determine a source-specific classifier;   input the one or more metrics relating to the merchant and the promotion into the source-specific classifier;   input the second one or more metrics relating to the merchant and the promotion into the source-specific classifier; and   determine, via the processor, an ROI or an ROI component.   
     
     
         89 . The apparatus of  claim 86 , wherein the one or more metrics or the second one or more metrics are generated based at least in part on one or more of a survey, a marketing exposure, a financial engineering transaction, an in-store transaction, or a webpage transaction. 
     
     
         90 . The apparatus of  claim 86 , wherein the merchant impact report comprises one or more amounts indicative of a revenue from the promotion or a cost of the promotion, wherein the one or more amounts are determined based on the one or more metrics relating to the merchant and the promotion. 
     
     
         91 . The apparatus of  claim 86 , wherein the return on investment (ROI) learning model is a support vector machine, decision tree learning, association rule learning, artificial neural networking, inductive logic programming, or clustering. 
     
     
         92 . The apparatus of  claim 86 , wherein the at least one memory including the computer program code is further configured to, with the at least one processor, cause the apparatus to:
 generate, via the processor, a dataset based on the one or more historical metrics;   compare, via the processor, the return on investment (ROI) learning model to the dataset;   train, via the processor, the return on investment (ROI) learning model to classify a particular metric value, determined by a combination of metrics, as indicative of a positive ROI; and   store, via the processor and the memory, the combination of metrics used as a predictor for a number of consumers who will return to the merchant after a first visit using the promotion.   
     
     
         93 . A computer program product comprising:
 at least one computer readable non-transitory memory medium having program code instructions stored thereon, the program code instructions which when executed by an apparatus, comprising a processor, a memory, and a display device for rendering a graphical user interface (GUI), cause the apparatus at least to:   receive, via the GUI, an input indicative of one or more metrics relating to a merchant and a promotion;   generate, by the processor, a return on investment (ROI) learning model based on the one or more metrics relating to the merchant and the promotion;   update, by the processor, the ROI learning model by automatically inputting a second one or more metrics relating to the merchant and the promotion, wherein the second one or more metrics relating to the merchant and the promotion are determined based on at least one or more historical metrics retrieved from a promotion repository;   generate, by the processor, a repeat revenue visual metric for the promotion, wherein the repeat business revenue visual metric is indicative of revenue predicted to be generated from one or more repeat business transactions associated with the promotion based on the updated ROI learning model;   generate, by the processor, a merchant impact report, comprising the repeat business revenue visual metric; and   display, via the GUI, the merchant impact report.   
     
     
         94 . The computer program product of  claim 93 , further comprising program code instructions, the program code instructions which when executed by the apparatus further cause the apparatus at least to:
 generate, via the processor, a set of clusters based on one or more similar return ROI learning models previously generated.   
     
     
         95 . The computer program product of  claim 93 , further comprising program code instructions, the program code instructions which when executed by the apparatus further cause the apparatus at least to:
 determine a source-specific classifier;   input the one or more metrics relating to the merchant and the promotion into the source-specific classifier;   input the second one or more metrics relating to the merchant and the promotion into the source-specific classifier; and   determine, via the processor, an ROI or an ROI component.   
     
     
         96 . The computer program product of  claim 93 , wherein the one or more metrics or the second one or more metrics are generated based at least in part on one or more of a survey, a marketing exposure, a financial engineering transaction, an in-store transaction, or a webpage transaction. 
     
     
         97 . The computer program product of  claim 93 , wherein the merchant impact report comprises one or more amounts indicative of a revenue from the promotion or a cost of the promotion, wherein the one or more amounts are determined based on the one or more metrics relating to the merchant and the promotion. 
     
     
         98 . The computer program product of  claim 93 , further comprising program code instructions, the program code instructions which when executed by the apparatus further cause the apparatus at least to:
 calculate, based on the upsell amount, a third amount indicative of revenue generated from promotion upsells;   present the merchant impact report including the first amount, the second amount, and the third amount;   generate, via the processor, a dataset based on the one or more historical metrics;   compare, via the processor, the return on investment (ROI) learning model to the dataset;   train, via the processor, the return on investment (ROI) learning model to classify a particular metric value, determined by a combination of metrics, as indicative of a positive ROI; and   store, via the processor and the memory, the combination of metrics used as a predictor for a number of consumers who will return to the merchant after a first visit using the promotion.