Abstract: A system includes a processor and a memory including computer program code. The memory and the computer program code are configured to, with the processor, cause the processor to: receive historical campaign data; determine a likelihood that a vendor will accept a card offer by inputting the historical campaign data into a machine learning model that has been trained for at least one campaign using training data comprising past campaign performance data; automatically recommend a campaign strategy tailored for the vendor using the machine learning model, wherein the recommended campaign strategy is an output of the machine learning model that is based on the determined likelihood that the vendor will accept the card offer; and automatically display the recommended campaign strategy output from the machine learning model on a graphical user interface (GUI).

Abstract: Systems and methods for dynamically determining an optimal baseline algorithm for calculating lift values are disclosed. The system receives data associated with a control strategy, and then randomly selects a control location, a time period, and an item that may not be associated with the control strategy but meets the one or more criteria of the control strategy such as relevance and sales volume. Using the randomly selected inputs and a plurality of null baselines values determined by a plurality of null baseline algorithms, the system iteratively calculates a plurality of null lift values for each of the applied plurality of null baselines values to determine a likelihood for a false positive lift for each of the applied plurality of null baselines values. An optimal baseline algorithm is selected from the plurality of null baselines algorithms based on their corresponding likelihood of false positive lifts.

Abstract: Systems and methods for dynamically determining an optimal baseline algorithm for calculating lift values are disclosed. The system receives data associated with a control strategy, and then randomly selects a control location, a time period, and an item that may not be associated with the control strategy but meets the one or more criteria of the control strategy such as relevance and sales volume. Using the randomly selected inputs and a plurality of null baselines values determined by a plurality of null baseline algorithms, the system iteratively calculates a plurality of null lift values for each of the applied plurality of null baselines values to determine a likelihood for a false positive lift for each of the applied plurality of null baselines values. An optimal baseline algorithm is selected from the plurality of null baselines algorithms based on their corresponding likelihood of false positive lifts.

Abstract: Systems and methods for dynamically determining an optimal baseline algorithm for calculating lift values are disclosed. The system receives data associated with a control strategy, and then randomly selects a control location, a time period, and an item that may not be associated with the control strategy but meets the one or more criteria of the control strategy such as relevance and sales volume. Using the randomly selected inputs and a plurality of null baselines values determined by a plurality of null baseline algorithms, the system iteratively calculates a plurality of null lift values for each of the applied plurality of null baselines values to determine a likelihood for a false positive lift for each of the applied plurality of null baselines values. An optimal baseline algorithm is selected from the plurality of null baselines algorithms based on their corresponding likelihood of false positive lifts.

Abstract: Systems and methods for dynamically determining an optimal baseline algorithm for calculating lift values are disclosed. The system receives data associated with a control strategy, and then randomly selects a control location, a time period, and an item that may not be associated with the control strategy but meets the one or more criteria of the control strategy such as relevance and sales volume. Using the randomly selected inputs and a plurality of null baselines values determined by a plurality of null baseline algorithms, the system iteratively calculates a plurality of null lift values for each of the applied plurality of null baselines values to determine a likelihood for a false positive lift for each of the applied plurality of null baselines values. An optimal baseline algorithm is selected from the plurality of null baselines algorithms based on their corresponding likelihood of false positive lifts.