Enhanced processing for communication workflows using machine-learning techniques

A system, comprising:

• one or more processors;
• and
• a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more processors, cause the one or more processors to perform operations including:
• accessing a communication workflow including one or more tasks arranged in a sequential order, the communication workflow being configured to facilitate interactions with a set of user devices, each task of the one or more tasks including executable code that, upon execution, performs a function associated with the set of user devices, and the communication workflow being associated with one or more parameters that characterize each task of the one or more tasks of the communication workflow;
• generating a composite feature vector representing the communication workflow, the composite feature vector being generated using a feature vector of each task of the one or more tasks of the communication workflow, and the feature vector of each task of the one or more tasks being generated by executing one or more machine-learning techniques using the one or more parameters that characterize the task;
• inputting the composite feature vector of the communication workflow into a trained machine-learning model, the trained machine-learning model having been trained to generate predictions of performance values of communication workflows, and the trained machine-learning model having been trained using a training data set representing one or more previously-executed communication workflows and a corresponding previous performance value of each of the one or more previously-executed communication workflows;
• and
• generating an output using the trained machine-learning model, the output being predictive of a performance value of the communication workflow.

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a processing apparatus to perform operations including:

• accessing a communication workflow including one or more tasks arranged in a sequential order, the communication workflow being configured to facilitate interactions with a set of user devices, each task of the one or more tasks including executable code that, upon execution, performs a function associated with the set of user devices, and the communication workflow being associated with one or more parameters that characterize each task of the one or more tasks of the communication workflow;
• generating a composite feature vector representing the communication workflow, the composite feature vector being generated using a feature vector of each task of the one or more tasks of the communication workflow, and the feature vector of each task of the one or more tasks being generated by executing one or more machine-learning techniques using the one or more parameters that characterize the task;
• inputting the composite feature vector of the communication workflow into a trained machine-learning model, the trained machine-learning model having been trained to generate predictions of performance values of communication workflows, and the trained machine-learning model having been trained using a training data set representing one or more previously-executed communication workflows and a corresponding previous performance value of each of the one or more previously-executed communication workflows;
• and
• generating an output using the trained machine-learning model, the output being predictive of a performance value of the communication workflow.