Merchant incremental electronic impact value prediction and ranking using multiple machine learning models

Abstract

Methods, apparatus, systems, and computer program products are disclosed for utilizing specially configured machine learning models to generate incremental currency value(s) associated with one or more target merchant data objects. Some embodiments, based on one or more market record sets, identify an actual electronic currency value for a total merchant data object set, and include a counterfactual model configured to generate a counterfactual electronic currency value for use in determining a counterfactual incremental electronic currency impact, and in some embodiments for ranking other models. Embodiments, additionally or alternatively, include an incrementality-trained ensemble model for generating a predictive incremental electronic currency impact. The incrementality-trained ensemble model may be trained to predict based on the rankings of the outputs of the counterfactual model. Embodiments may further rank target merchant data objects and perform one or more additional actions, including assigning the target merchant data objects to sales account data structures for management.

1 - 39 . (canceled) 
     
     
         40 . An apparatus comprising at least one processor and at least one memory, the at least one memory including computer-coded instructions therein, wherein the computer-coded instructions are configured to, in execution with the at least one processor, cause the apparatus to:
 receive a merchant market identifier;   receive a target merchant identifier associated with a target merchant data object;   receive a predictive input set comprising at least the merchant market identifier;   generate, using a predictive model, a predictive electronic currency value associated with the merchant market identifier;   identify a dependent score set associated with the merchant market identifier; and   generate, using an incrementality-trained ensemble model, a predictive incremental electronic currency impact associated with the target merchant identifier based on the predictive electronic currency value and the dependent score set.   
     
     
         41 . The apparatus according to  claim 40 , further caused to:
 receive a second target merchant identifier associated with a second target merchant data object;   receive a second predictive input set comprising at least the second merchant market identifier;   generate, using the predictive model, a second predictive electronic currency value associated with the second merchant market identifier;   identify a second dependent score set associated with the second merchant market identifier; and   generate, using the incrementality-trained ensemble model, a second predictive incremental electronic currency impact associated with the second target merchant identifier based on the second predictive electronic currency value and the second dependent score set.   
     
     
         42 . The apparatus according to  claim 40 , wherein the incrementality-trained ensemble model is trained based at least in part on a counterfactual model. 
     
     
         43 . The apparatus according to  claim 40 , the apparatus further caused to:
 train the predictive model based at least in part on the predictive electronic currency value associated with the merchant market identifier.   
     
     
         44 . The apparatus according to  claim 40 , the apparatus further caused to:
 train the incrementality-trained ensemble model based at least in part on the predictive incremental electronic currency impact associated with the target merchant identifier.   
     
     
         45 . The apparatus according to  claim 40 , the apparatus further caused to:
 identify an ensemble training record set, the ensemble training record set associated with an ensemble training feature set; and   train the incrementality-trained ensemble model based at least in part on the ensemble training record set   
     
     
         46 . The apparatus according to  claim 40 , the apparatus further caused to:
 generate a model ranking list based at least in part on the predictive incremental electronic currency impact; and   configure at least one device based on a top ranked model of the model ranking list.   
     
     
         47 . A computer-implemented method comprising:
 receiving a merchant market identifier;   receiving a target merchant identifier associated with a target merchant data object;   receiving a predictive input set comprising at least the merchant market identifier;   generating, using a predictive model, a predictive electronic currency value associated with the merchant market identifier;   identifying a dependent score set associated with the merchant market identifier; and   generating, using an incrementality-trained ensemble model, a predictive incremental electronic currency impact associated with the target merchant identifier based on the predictive electronic currency value and the dependent score set.   
     
     
         48 . The computer-implemented method according to  claim 47 , further comprising:
 receiving a second target merchant identifier associated with a second target merchant data object;   receiving a second predictive input set comprising at least the second merchant market identifier;   generating, using the predictive model, a second predictive electronic currency value associated with the second merchant market identifier;   identifying a second dependent score set associated with the second merchant market identifier; and   generating, using the incrementality-trained ensemble model, a second predictive incremental electronic currency impact associated with the second target merchant identifier based on the second predictive electronic currency value and the second dependent score set.   
     
     
         49 . The computer-implemented method according to  claim 47 , wherein the incrementality-trained ensemble model is trained based at least in part on a counterfactual model. 
     
     
         50 . The computer-implemented method according to  claim 47 , further comprising:
 training the predictive model based at least in part on the predictive electronic currency value associated with the merchant market identifier.   
     
     
         51 . The computer-implemented method according to  claim 47 , further comprising:
 training the incrementality-trained ensemble model based at least in part on the predictive incremental electronic currency impact associated with the target merchant identifier.   
     
     
         52 . The computer-implemented method according to  claim 47 , further comprising:
 identifying an ensemble training record set, the ensemble training record set associated with an ensemble training feature set; and   training the incrementality-trained ensemble model based at least in part on the ensemble training record set   
     
     
         53 . The computer-implemented method according to  claim 47 , further comprising:
 generating a model ranking list based at least in part on the predictive incremental electronic currency impact; and   configuring at least one device based on a top ranked model of the model ranking list.   
     
     
         54 . A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor, configures the computer program product to:
 receive a merchant market identifier;   receive a target merchant identifier associated with a target merchant data object;   receive a predictive input set comprising at least the merchant market identifier;   generate, using a predictive model, a predictive electronic currency value associated with the merchant market identifier;   identify a dependent score set associated with the merchant market identifier; and   generate, using an incrementality-trained ensemble model, a predictive incremental electronic currency impact associated with the target merchant identifier based on the predictive electronic currency value and the dependent score set.   
     
     
         55 . The computer program product according to  claim 54 , further configured to:
 receive a second target merchant identifier associated with a second target merchant data object;   receive a second predictive input set comprising at least the second merchant market identifier;   generate, using the predictive model, a second predictive electronic currency value associated with the second merchant market identifier;   identify a second dependent score set associated with the second merchant market identifier; and   generate, using the incrementality-trained ensemble model, a second predictive incremental electronic currency impact associated with the second target merchant identifier based on the second predictive electronic currency value and the second dependent score set.   
     
     
         56 . The computer program product according to  claim 54 , further configured to:
 train the predictive model based at least in part on the predictive electronic currency value associated with the merchant market identifier.   
     
     
         57 . The computer program product according to  claim 54 , further configured to:
 train the incrementality-trained ensemble model based at least in part on the predictive incremental electronic currency impact associated with the target merchant identifier.   
     
     
         58 . The computer program product according to  claim 54 , further configured to:
 identify an ensemble training record set, the ensemble training record set associated with an ensemble training feature set; and   train the incrementality-trained ensemble model based at least in part on the ensemble training record set.   
     
     
         59 . The computer program product according to  claim 54 , further configured to:
 generate a model ranking list based at least in part on the predictive incremental electronic currency impact; and   configure at least one device based on a top ranked model of the model ranking list.