Cash Identification and Displacement Strategy

A method of segmenting users based on transaction activity and propensity for conducting portable financial device transactions, comprising:

• determining at least one subset of transaction data categories from a plurality of transaction data categories;
• ranking the at least one subset of transaction data categories into at least one order;
• generating, with at least one processor, at least one predictive model for determining user propensity for prospectively increasing a frequency of portable financial device transactions based at least partially on the ranking of the at least one subset of transaction data categories;
• analyzing, with at least one processor, transaction data for portable financial device transactions initiated by each user of a plurality of users to identify at least one transaction for each user that corresponds to at least one transaction data category of the at least one subset of transaction data categories;
• generating, with at least one processor, at least one subset of users of the plurality of users based at least partially on the at least one predictive model and the at least one transaction identified for each of the plurality of users;
• and
• automatically initiating, with at least one processor, a conversion action to convert at least one user in the at least one subset of users to more frequent performance of portable financial device transactions.

A computer program product for segmenting users based on transaction activity and propensity for conducting portable financial device transactions, 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:

• determine at least one subset of transaction data categories from a plurality of transaction data categories;
• rank the at least one subset of transaction data categories into at least one order;
• generate at least one predictive model for determining user propensity for prospectively increasing a frequency of portable financial device transactions based at least partially on the ranking of the at least one subset of transaction data categories;
• analyze transaction data for portable financial device transactions initiated by each user of a plurality of users to identify at least one transaction for each user that corresponds to at least one transaction data category of the at least one subset of transaction data categories;
• generate at least one subset of users of the plurality of users based at least partially on the at least one predictive model and the at least one transaction identified for each of the plurality of users;
• and
• automatically initiate a conversion action to convert at least one user in the at least one subset of users to more frequent performance of portable financial device transactions.

A system for segmenting users based on transaction activity and propensity for conducting portable financial device transactions, comprising:

• at least one database comprising user transaction data, the user transaction data comprising:
• a plurality of transaction data categories and transaction data for portable financial device transactions initiated by each user of a plurality of users;
• and
• at least one processor in communication with the at least one database, the at least one processor programmed or configured to:
• determine at least one subset of transaction data categories from the plurality of transaction data categories;
• rank the at least one subset of transaction data categories into at least one order;
• generate at least one predictive model for determining user propensity for prospectively increasing a frequency of portable financial device transactions based at least partially on the ranking of the at least one subset of transaction data categories;
• analyze the transaction data for portable financial device transactions initiated by each user of a plurality of users to identify at least one transaction for each user that corresponds to at least one transaction data category of the at least one subset of transaction data categories;
• generate at least one subset of users of the plurality of users based at least partially on the at least one predictive model and the at least one transaction identified for each of the plurality of users;
• and
• automatically initiate a conversion action to convert at least one user in the at least one subset of users to more frequent performance of portable financial device transactions.