Utilizing machine learning with call histories to determine support queue positions for support calls

A device, comprising:

• one or more memories;
• and
• one or more processors, communicatively coupled to the one or more memories, to:
• receive, from a user device, a communication associated with a support issue encountered by a user of the user device;
• assign the communication to a position in a support queue based on when the communication is received,
• wherein the support queue includes:
• data identifying positions of other communications received from other users, and
• data identifying when the other communications, received from the other users, were received;
• process data identifying the communication, historical communication data describing prior communications associated with the user, and other communication data describing the other communications received from the other users, with a machine learning model, to determine different factors associated with the communication and respective weights for the different factors;
• associate the respective weights with the different factors;
• generate a score for the communication based on associating the respective weights with the different factors;
• assign the communication to a different position in the support queue based on the score for the communication;
• and
• perform one or more actions based on assigning the communication to the different position in the support queue.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:
• receive, from a user device, a communication associated with a support issue encountered by a user of the user device;
• assign the communication to a position in a support queue based on when the communication is received,
• wherein the support queue includes:
• data identifying positions of other communications received from other users, and
• data identifying when the other communications, received from the other users, were received;
• process data identifying the communication, historical communication data describing prior communications associated with the user, and other communication data describing the other communications received from the other users, with a machine learning model, to determine different factors associated with the communication and respective weights for the different factors;
• associate the respective weights with the different factors;
• generate a score for the communication based on associating the respective weights with the different factors;
• modify the position of the communication in the support queue based on the score for the communication;
• and
• perform one or more actions based on modifying the position of the communication in the support queue.