Liveperson, Inc.

Leor Gruendlinger

Shlomo Lahav

Ofer Ron

Liran Shaked

Yehiel Cohen

Tomer Ben-David

Amihay Zer-Kavod

Yariv Lukach

Vadim Milman

Itamar Keller

Shachar Hendel

Irit Sella

Matan Mandelbrod

Navot Akiva

Systems, methods, and computer program products include smart capacity workload routing with workload modeling. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is processed and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model. An agent with capacity that is available to handle the requested user communication is identified. A communication slot for the identified agent is activated and defined by the identified workload measurement, and the request is routed to the identified agent and updating available workload capacity in the system.

Smart capacity for workload routing

The present disclosure relates generally to facilitating routing of communications. One example includes a communication server determining capacities associated with a terminal devices based on workloads for agents associated with the terminal devices. Historical acceptance data is accessed for past interaction invitations to user devices associated with one or more criteria. Current data is then used to determine available interactions and to facilitate interactions using interaction invitations based on the historical data and the current number of available interactions.

Systems and methods for managing interaction invitations

Systems and methods are provided for dynamic generation of staff analytics and forecasts based on skill and service level. Dynamic forecasting allows for forecast generation in real-time and may be based on historical data regarding skills and results, as well as data science to identify patterns and make predictions. The resulting staffing forecast may therefore provide for efficient management of messaging staff costs while preserving the desired service quality. The staffing forecast may include a volume forecast that is tailored to the unique nature of asynchronous messaging, as well as the unique messaging needs of the entity so as to efficiently manage messaging operations and make data-driven staffing decisions that take service level into account. An exemplary embodiment may include dynamic analytics tools that may use specified target and/or resource numbers (e.g., desired service level) for an existing messaging operation and get a detailed per-skill staffing forecast.

Dynamic analytics and forecasting for messaging staff

Examples are described of smart capacity workload routing. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is process and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model, and identifying an agent with capacity that is available to handle the requested user communication. A communication slot for the identified agent is activated and defined by the identified workload measurement. The request is then routed to the identified agent and updating available workload capacity in the system.

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Tel Aviv, Israel

Leor Gruendlinger