A computerized-method for measuring an Agent-Engagement-Index (AEI) and associating actions to improve thereof, is provided herein. The computerized-method may operate an AEI module for an assessment of agents. The AEI module includes: (i) retrieving data from applications to derive agent's related-data and exporting the agent's related-data into data-files; (ii) operating a data-ingest module to store the agent's related-data from the data-files; (iii) operating a transform module to transform the agent's related-data by creating relational-entities and calculating metrics; (iv) operating an analytic-engine to process the relational-entities and the calculated metrics for calculating indicators and an AEI based thereon; (v) determining actions to improve the AEI based on the calculated AEI and the indicators; (vi) storing the determined actions in the data-store of agents to improve the AEI and the indicators; and (vii) upon user's request displaying the indicators and the AEI for each agent and the determined actions for each agent.

Methods, apparatus, systems, computing devices, computing entities, and/or the like for contextually optimizing routings for interactions. This may include receiving an interaction, wherein the interaction is selected from the group consisting of a voice-based interaction and a textual-based interaction; receiving an interaction problem statement for the interaction; generating, based at least in part on the interaction problem statement, an interaction problem statement summary, wherein the interaction problem statement comprises the context of the interaction; identifying one or more features for the interaction, wherein the features are input for one or more machine learning models; predicting an optimal route for the interaction, wherein the optimality of each route, hence, the optimal route is determined by the one or more machine learning models; and routing the interaction to the optimal route.

A system obtains conversation data corresponding to conversations between users and agents of a client. The system identifies a set of intents from the conversations and identifies a set of contexts, explicit elements, and implied elements of these intents. The system identifies actions that can be performed to recognize new explicit and implied elements from new conversations and to address intents in these new conversations. Based on these actions, the system generates a set of recommendations that can be provided to the client. As agents communicate with users, the system monitors adherence to the set of recommendations.

A system, computer program product, and method are disclosed. In an approach to train customer service agent using chatbots. The method includes training a chatbot for a customer chat simulation based on a customer service conversation data, a task scenario, and a customer persona. The method also includes monitoring an interaction between a customer service agent and the chatbot. The method further includes determining an assessment of the performance of the customer service agent based on the interaction between the customer service agent and the chatbot. The method additionally includes generating feedback for the customer service agent based on the assessment of the performance of the customer service agent.

Real-time speech analytics (RTSA) provides maintaining real-time speech conditions, rules, and triggers, and real-time actions and alerts to take. A call between a user and an agent is received at an agent computing device. The call is monitored to detect in the call one of the real-time speech conditions, rules, and triggers. Based on the detection, at least one real-time action and/or alert is initiated.

Disclosed herein is a method for automated agent behavior recommendations for call quality improvement. The method performed at a server includes receiving a first data record and a second data record of a plurality of data records, each comprising communication between a first party and a second party and determining a first communication originated from the first party. The method includes determining a category for each section of the plurality of sections of the first communication, and a plurality of behavior distances between different categories associated with the plurality of sections. The method includes augmenting first metadata of the first data record and second metadata of the second data record to include associated behavior distances and determining an average performance ranking of the first party to generate a recommendation to increase the average performance ranking of the first party above a preconfigured threshold.

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.

A method for providing coachable events for agents within a call center is provided. Behavior score waveforms for interactions and behaviors can be determined. Events can be identified in the behavior score waveforms within identified durations, and a relevancy of one or more events to one or more behaviors can be determined.

A technique relates to communication resource allocation. A computer system monitors a communication between a conversational entity communication channel and a user device. A type of the communication associated with the user device is determined based on the communication. A replacement communication channel to replace the conversational entity communication channel is determined, in response to the type of the communication. The replacement communication channel is coupled to the user device in place of the conversational entity communication channel.

A method and apparatus for guiding an agent during an active call between the customer and an agent is provided. The method comprises extracting, at a call analytics server (CAS), from a transcribed text of an audio of a call between a customer and an agent, a call context. Based on at least one of the call context, a call metadata, or a customer historical data, occurrence of a qualifying event is determined. Upon occurrence of the qualifying event, an action from multiple actions is identified based on at least one of the call context, the call metadata, or the customer historical data. The identified action is sent as guidance from the CAS to a graphical user interface (GUI) accessible by the agent, while the call is active.