System, method, and computer program product for generating a synthetic control group

A system comprising a server comprising at least one processor, the server being programmed and/or configured to:

• receive transaction account data of a plurality of transaction accounts in a first time period;
• receive, via a transaction service provider system, transaction data associated with at least one transaction completed by a first set of transaction accounts of the plurality of transaction accounts with at least one target merchant in a second time period;
• generate a synthetic control group comprising a subset of transaction accounts sampled from the first set of transaction accounts;
• determine, using a machine learning model, for each transaction account of the synthetic control group, a propensity score representative of a likelihood of being associated with a test group;
• assign, based at least partly on the propensity score of each transaction account and historic transaction data, an entropy balancing weight to each transaction account of the synthetic control group;
• alter, based on the transaction data and the synthetic control group, at least one operational parameter of a computer-implemented advertisement program to be executed in a third time period, the at least one operational parameter of the computer-implemented advertisement program comprising at least (i) a number of communications to be transmitted, and (ii) a time of communications to be transmitted;
• and
• execute the computer-implemented advertisement program in the third time period by allocating computing resources at least partly based on the at least one operational parameter, wherein allocating the computing resources comprises determining a proportional amount of processing capacity and memory storage for the number of communications to be transmitted and determining which computing resources to use based on the time of communications to be transmitted in comparison to server uptime.

A computer program product comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to:

• receive transaction account data of a plurality of transaction accounts in a first time period;
• receive, via a transaction service provider system, transaction data associated with at least one transaction completed by a first set of transaction accounts of the plurality of transaction accounts with at least one target merchant in a second time period;
• generate a synthetic control group comprising a subset of transaction accounts sampled from the first set of transaction accounts;
• determine, using a machine learning model, for each transaction account of the synthetic control group, a propensity score representative of a likelihood of being associated with a test group;
• assign, based at least partly on the propensity score of each transaction account and historic transaction data, an entropy balancing weight to each transaction account of the synthetic control group;
• alter, based on the transaction data and the synthetic control group, at least one operational parameter of a computer-implemented advertisement program to be executed in a third time period, the at least one operational parameter of the computer-implemented advertisement program comprising at least (i) a number of communications to be transmitted, and (ii) a time of communications to be transmitted;
• and
• execute the computer-implemented advertisement program in the third time period by allocating computing resources at least partly based on the at least one operational parameter, wherein allocating the computing resources comprises determining a proportional amount of processing capacity and memory storage for the number of communications to be transmitted and determining which computing resources to use based on the time of communications to be transmitted in comparison to server uptime.