Abstract: A platform implements tensor-based reinforcement learning for communication channel optimization. Using an audio data feed, the platform generates a set of alphanumeric input features associated with an entity interaction. Using the features, a neural network generates a first node parameter set for a first node of a channel optimization map. The platform instantiates a communication session with a computing device via a communication channel indicated by communication channel parameter(s) in the first node parameter set. Using a timing parameter, a tone parameter, a content parameter, and/or a nudge parameter, an electronic message can be generated for transmission via the communication channel. Using the neural network, the platform processes feedback data received via the channel to generate a second node for the channel optimization map. Configuration of the neural network can be updated, using the feedback data, by applying reinforcement learning or another suitable technique.

Abstract: A method, apparatus, system, and computer program code for digitally presenting a comparison of labor resource allocations between organizations. A computer system identifies employee data for a set of employees. The employee data includes job titles, and job descriptions. Using a set of machine learning models, the computer system extracts skills data from the job descriptions. Based on the skills data extracted from the job descriptions and using the set of machine learning models, the computer system maps the job titles to normalized titles within a job taxonomy that governs relationships between business functions, subfunctions, and the normalized titles. The computer system determines a resource allocation for an organization over each of the normalized titles. The computer system generates a granular comparison of the granular resource allocation to a set of benchmark allocations. The computer system digitally presents the granular comparison in a graphical user interface.