Systems and methods for tailoring marketing

A system comprising:

• a computing device comprising a processor and a memory;
• and
• machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:
• receive a first set of data from a first source and a second set of data from a second source, the first set of data comprising a plurality of items available from the first source for a first set of users and the second set of data comprising transaction purchase data for a second set of users that have reward accounts;
• utilize a predictive data model that determines a propensity score for a user from only behavior data that is not attributed to the user, the behavior data comprising the second set of data, wherein the user is absent from the second set of users and the propensity score represents a likelihood that the user will act on a recommendation or an offer for one or more of the plurality of items from the first source;
• receive a third set of data from a third source, the third set of data comprising social media channel data for a third set of users, wherein the third set of users are absent from the second set of users;
• update the predictive data model to determine the propensity score for the user based at least in part on the third set of data;
• generate, with the predictive data model, the propensity score for the recommendation or the offer for the user;
• and
• provide, over a computing network, a graphical user interface to the user having the recommendation or the offer in response to the propensity score meeting or exceeding a predefined threshold, wherein a positioning of the recommendation or the offer in the graphical user interface with respect to another recommendation or another offer in the graphical user interface is determined based on social media channel data of the user.

A non-transitory, computer-readable medium comprising machine-readable instructions that, when executed by a processor, cause a computing device to at least:

• receive a first set of data from a first source and a second set of data from a second source, the first set of data comprising a plurality of items available from the first source for a first set of users and the second set of data comprising transaction purchase data for a second set of users that have reward accounts;
• utilize a predictive data model that determines a propensity score for a user from only behavior data that is not attributed to the user, the behavior data comprising the second set of data, wherein the user is absent from the second set of users and the propensity score represents a likelihood that the user will act on a recommendation or an offer for one or more of the plurality of items from the first source;
• receive a third set of data from a third source, the third set of data comprising social media channel data for a third set of users, wherein the third set of users are absent from the second set of users;
• update the predictive data model to determine the propensity score for the user based at least in part on the third set of data;
• generate, with the predictive data model, the propensity score for the recommendation or the offer for the user;
• and
• provide, over a computing network, a graphical user interface to the user having the recommendation or the offer in response to the propensity score meeting or exceeding a predefined threshold, wherein a positioning of the recommendation or the offer in the graphical user interface with respect to another recommendation or another offer in the graphical user interface is determined based on social media channel data of the user.