Intelligence tool for vendor enablement campaigns

A method for rearranging icons representing vendors, on a graphical user interface (GUI) of a computer system, the method causing at least a processor of the computer system to perform operations comprising:

• receiving historical campaign data associated with a plurality of vendors;
• segmenting the plurality of vendors into at least one group of vendors that share at least one attribute;
• inputting the received historical campaign data into an artificial intelligence (AI) tool comprising a convolutional neural network that has been trained using training data that included data of a campaign and a corresponding campaign outcome;
• determining, using the AI tool by applying a map of the convolutional neural network, likelihoods that respective vendors will accept a card offer, wherein each likelihood is represented as a propensity score based on a quantity of vendors that accepted past offers relative to a total quantity of vendors in the historical campaign data;
• identifying a set of the vendors that have accepted a past card offer based on the propensity scores and a quantity of factors in the historical campaign data;
• based on the propensity scores, automatically recommending, using the AI tool, a campaign strategy tailored for each vendor, the recommended campaign strategy including a payment acceleration based on the AI tool inferring that the payment acceleration increases the propensity score over a baseline propensity score;
• determining feedback data indicating an accuracy of the AI tool by comparing the propensity score to an outcome of the campaign strategy based on at least the payment acceleration;
• updating the historical campaign data with the outcome of the campaign strategy;
• providing the updated historical campaign data to the convolutional neural network of the AI tool;
• based on the determined feedback data indicating the accuracy, automatically adjusting at least one parameter of the convolutional neural network of the AI tool to refine the accuracy of the convolutional neural network of the AI tool by iteratively repeating the updating, the providing, and the adjusting over a plurality of campaigns, adjusting the at least one parameter comprising altering the map of the convolutional neural network;
• using the parameter-adjusted convolutional neural network of the AI tool to generate a new campaign strategy recommendation only if a likelihood of success of the new campaign strategy is more than a threshold value;
• automatically sending the recommended new campaign strategy to the at least one group of vendors;
• automatically prioritizing the vendors according to the quantity of factors associated with the set of vendors;
• displaying on a graphical user interface (GUI) icons representing the prioritized vendors in a list, the icons organized in the list based on a specific criterion, wherein the specific criterion is a quantity of factors associated with each vendor;
• and
• automatically moving an icon representing a vendor to a top of the list based on the quantity of factors associated with the vendor.

A non-transient computer readable storage medium storing computer program code, that when executed by a processor, cause the processor to:

• receive historical campaign data;
• input the historical campaign data into an artificial intelligence (AI) tool comprising a convolutional neural network that has been trained using training data that included data of a campaign and a corresponding campaign outcome;
• determine, using the AI tool a likelihood that a vendor will accept a card offer by applying a map of the convolutional neural network to determine the likelihood based on the inputted received historical campaign data, the likelihood being represented as a propensity score based on a quantity of vendors that accepted past offers relative to a total quantity of vendors in the historical campaign data;
• based on the propensity score, automatically recommend, using the AI tool, a campaign strategy tailored for a target vendor, the recommended campaign strategy including a payment acceleration based on the AI tool inferring that the payment acceleration increases the propensity score over a baseline propensity score;
• determine feedback data indicating an accuracy of the AI tool by comparing the propensity score to an outcome of the campaign strategy based on offering at least one condition to the target vendor recommended using the AI tool, the condition being at least one of the payment acceleration and a message strength;
• update the historical campaign data with the outcome of the campaign strategy;
• provide the updated historical campaign data to the convolutional neural network of the AI tool;
• based on the determined feedback data indicating the accuracy, automatically adjust at least one parameter of the convolutional neural network of the AI tool to refine the accuracy of the convolutional neural network of the AI tool by iteratively repeating the updating, the providing, and the adjusting over a plurality of campaigns, adjusting the at least one parameter comprising altering the map of the convolutional neural network;
• use the parameter-adjusted convolutional neural network of the AI tool to generate a new campaign strategy recommendation only if a likelihood of success of the new campaign strategy is more than a threshold value;
• and
• automatically send the recommended new campaign strategy.