Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for processing performance metric recommendations for an agent profile. In accordance with one embodiment, a method is provided that includes: generating an agent feature data object for the agent profile from communication data objects representing communications; processing the agent feature data object using an agent group identifier machine learning model to generate an agent group data object; identifying a top agent performer data object based at least in part on the agent group data object; generating an agent assessment data object representing performance of an agent represented by the agent profile; processing the agent assessment data object and the top agent performer data object using a comparison machine learning model to generate inferred performance gap data objects; and generating the performance metric recommendations based at least in part on the inferred performance gap data objects.

A method includes, receiving protocol event data from a plurality of probes within the telecommunication system, associating the protocol event data into a call, wherein the protocol event data comprises processes in a plurality of protocols, mapping the protocol event data into a per-call finite state machine, wherein the finite state machine represents possible call states in multiple protocols between call setup and termination, wherein the mapping is performed at least in part within a duration of the call, and after termination of the call, creating a call data record that includes information from the per-call finite state machine and Key Performance Indicator (KPI) information of the call.

Systems and methods are disclosed for estimating and assigning agent performance characteristics in a call routing center. Performance characteristics (e.g., sales rate, customer satisfaction, duration of call, etc.) may be assigned to an agent when the agent has made few calls relative to other agents or otherwise has a large error in their measure of one or more performance characteristics used for matching callers to agents (e.g., via a performance based or pattern matching routing method). A method includes identifying agents of a plurality of agents having a number of calls fewer than a predetermined number of calls (or an error in the performance characteristic exceeding a threshold), assigning a performance characteristic to the identified agents (that is different than the agent's actual performance characteristic), and routing a caller to one of the plurality of agents based on the performance characteristics of the plurality of agents.

Systems and methods are provided for controlling interactions between customers and agents. One embodiment is a system that includes an interface which receives a request to initiate a conversation. The system also includes a controller that acts as a message broker for the conversation by initiating a message stream between a customer device and an agent device, and records a time from receipt of each customer message to receipt of a corresponding agent response during the message stream. The controller also identifies an issue that the conversation is directed to, adjusts a permitted time period for agent responses based on a complexity of the issue, calculates an effectiveness score for the agent based on a number of agent responses that were provided within the permitted time period, updates a profile for the agent with the effectiveness score, and uses the effectiveness score as criteria for selecting the agent.

A computerized-method for gauging agent's self-assessment effectiveness, is provided herein. The computerized-method includes for each interaction (i) operating a Self-assessment Consolidation module to calculate a confidence-interval for each data-point of one or more preconfigured data-points, and (ii) operating a Self-assessment Divergence Determinant (SDD) module. The operating of the SDD includes: retrieving one or more data-points of the interaction; for each data-point retrieving the confidence interval; setting a divergence-indicator as zero, when the data point is within the confidence-interval; setting the divergence-indicator as a subtraction of the data point from the calculated lower-bound, when the data-point is lower than the lower-bound of the confidence-interval; and setting the divergence-indicator as a subtraction of the calculated upper-bound from the data-point, when the data-point is greater than the upper-bound of the confidence-interval. Then, accumulating the divergence-indicator of the data-points to yield an SDD for the interaction; and sending the SDD to one or more systems.

A computer-implemented method for providing an objective evaluation to a customer service representative regarding his performance during an interaction with a customer may include receiving a digitized data stream corresponding to a spoken conversation between a customer and a representative; converting the data stream to a text stream; generating a representative transcript that includes the words from the text stream that are spoken by the representative; comparing the representative transcript with a plurality of positive words and a plurality of negative words; and generating a score that varies according to the occurrence of each word spoken by the representative that matches one of the positive words, and/or the occurrence of each word spoken by the representative that matches one of the negative words. Tone of voice, as well as response time, during the interaction may also be monitored and analyzed to adjust the score, or generate a separate score.

The present invention is a system and method of routing incoming communications to a CSR and providing guidance to the CSR based on the incoming communication using feedback information such as sentiment feedback, survey feedback, and feedback from actions taken by CSRs based on skill level. A CEC system receives an incoming communication, analyzes the communication and creates metadata based off of the analysis. The metadata is used by the RAE routing module to route the communication to an appropriate party. The metadata is also used by the GAE guidance module to determine the guidance to provide to the CSR. The CSR then performs an action based on the guidance. The CEC system continues to monitor the interaction until the interaction is completed. The communication metadata, the communication, the guidance, and the CSRs action are all provided to a RAS rules analysis module wherein the RAS analyzes the data and automatedly updates the rules (RAR and GAR) according to the analysis.

A system and method are presented for adaptive skill level assignments of agents in contact center environments. A client and a service collaborate to automatically determine the effectiveness of an agent handling an interaction that has been routed using skills-based routing. Evaluation operations may be performed including emotion detection, transcription of audio to text, keyword analysis, and sentiment analysis. The results of the evaluation are aggregated with other information such as the interaction's duration, agent skills and agent skill levels, and call requirement skills and skill levels, to update the agent's profile which is then used for subsequent routing operations.

A method includes: receiving protocol event data from a plurality of probes within the telecommunication system; determining a most probable cause of a call event from the protocol event data; applying the most probable cause to a trained machine learning algorithm that includes the most probable cause as its input and a telecommunication system score as its output; and in response to an output score from the trained machine learning algorithm, performing a corrective action for a plurality of network users that are expected to be affected by the most probable cause.

A system and method for detecting a start and end of section keystrokes, measuring a section time between the start and end of section keystrokes; aggregating the measured section time with previously measured section times; comparing the aggregate of the measured section times with a section target time; and sending an alert when a difference between the aggregate of the measured section times and the section target time is greater than a predetermined threshold.