Systems, apparatuses, methods, and computer program products for initiating performance of one or more field expansion actions

An apparatus comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:

• receive item feature data representative of a plurality of item configuration features associated with an item, wherein a first part of the item feature data is received from an internal item feature database and a second part of the item feature data is received from an external item feature database;
• determine one or more field item predictions by applying at least a portion of the item feature data to a first portion of a composite machine learning model;
• generate a field item feature structure, wherein the field item feature structure comprises the portion of the item feature data and the one or more field item predictions;
• determine one or more available field spaces using the field item feature structure;
• generate one or more field expansion programs using a second portion of the composite machine learning model and in response to determining the one or more available field spaces;
• and
• initiate performance of one or more field expansion actions based on the one or more field expansion programs.

A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor, configures the computer program product for:

• receiving item feature data representative of a plurality of item configuration features associated with an item, wherein a first part of the item feature data is received from an internal item feature database and a second part of the item feature data is received from an external item feature database;
• determining one or more field item predictions by applying at least a portion of the item feature data to a first portion of a composite machine learning model;
• generating a field item feature structure, wherein the field item feature structure comprises the portion of the item feature data and the one or more field item predictions;
• determining one or more available field spaces using the field item feature structure;
• generating one or more field expansion programs using a second portion of the composite machine learning model and in response to determining the one or more available field spaces;
• and
• initiating performance of one or more field expansion actions based on the one or more field expansion programs.