A computerized-method for determining an agent-proficiency when addressing concurrent customer sessions via one or more channel types and utilization thereof is provided herein. The computerized-method includes operating a Concurrent-Sessions-Handling-Agent-Proficiency (CSHAP) module. The CSHAP-module includes: (a) operating an interactions-module to retrieve one or more interactions and metadata thereof of the agent; (b) for each interaction, determining if the interaction has been handled with concurrent interactions; (c) for each determined interaction as handled with concurrent interactions, checking in the metadata if the interaction has one or more defocused-events; (d) calculating a CSHAP-score for the agent based on one or more attributes from the metadata of the interaction to provide an indication as to an ability of the agent to address concurrent customer sessions; (e) storing the calculated CSHAP-score in a data-store; and (f) sending the CSHAP-score to one or more applications, to take one or more follow-up actions based on the CSHAP-score.

A device monitors a communication between a user associated with a user device and a service representative associated with a service representative device, and causes a natural language processing model to perform a natural language processing analysis of a user input of the communication to identify a topic associated with the communication. The device determines a first score associated with the topic, and determines a second score associated with enabling the communication, where the first score and second score indicate a service performance score of an entity. The device causes a sentiment analysis model to perform a sentiment analysis of the communication to determine a sentiment score indicating a level of satisfaction the user has relative to the topic. The device updates a transaction protocol associated with the topic based on the service performance score, and/or updates a communication processing protocol associated with the communication based on the sentiment score.

Computer-implemented techniques for answer time prediction in a contact center service of a provider network. While a delayed processing timing has not been met, a set of contact queuing context-actual answer time data for a set of contact inquiries serviced is received as a first set of contact queuing context-actual answer time data. When the delayed processing timing has been met, a new queuing model is learned based on the first set of contact queuing context-actual answer time data and a previous set of contact queuing context-actual answer time data for a previous set of contact inquiries serviced or a previous version of the queuing model. A request to predict an answer time for a target contact queuing context is received and an answer time for the target contact queuing context is predicted based on the new queuing model. The predicted answer time is provided to a contact via a contact communications channel.

An adaptive conversational system may simultaneously monitor multiple active calls or ongoing voice or telephone conversations, may extract a different set of conversation elements from a current point in each conversation in real-time as each conversation proceeds, may determine different rules that apply to current points of different ongoing conversations based on the extracted conversation elements satisfying different rule triggers, and may control different conversations at different times according to actions of different rules that are applied at different times to different conversations. The system may selectively control the conversations when the conversations become non-compliant, deviate from best practices, or can be controlled to more effectively reach a positive disposition than when allowing a telephone agent to independently control the conversation. The system may use machine learning and/or artificial intelligence to define rules based on tracked actions that are produce a positive disposition more effectively than existing rules.

A heuristic engine includes capabilities to collect an unstructured data set and detect instances of transaction fraud in a financial account. By providing a heuristic algorithm with unstructured transaction sets and indications of particular instances of transactions that correlate with past fraudulent activity allows prevention of future occurrences of fraud. Such heuristic algorithms may learn from past indications of fraudulent activity and improve accuracy of detection of future fraud detections.

A system for improving conversational skills using a virtual speech agent is disclosed, including a virtual speech agent to execute a phone call between the virtual agent and a user. The virtual speech agent and user engage in a back-and-forth conversation, wherein the virtual speech agent generates a summary and a feedback report in view of the conversation.

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 determination is made that a real-time communication session (e.g., a voice call) between a first agent terminal and a customer communication endpoint has been placed on hold by a first contact center agent. An estimated hold time for the real-time communication is determined, based on a rule, in response to determining that the real-time communication session has been placed on hold. It is determined that the estimated hold time for the real-time communication session has expired. In response to determining that the estimated hold time for the real-time communication session has expired, one or more actions occur. For example, the real-time communication session is automatically transferred to a second agent terminal.

A method, device, and computer-readable medium provide for receiving, via a chatbot access channel, a chat message from a user device associated with a customer chat session; determining that the chat message includes a customer intent that corresponds to a chat flow for the customer chat session; generating one or more suggested response messages based on the chat message, wherein at least one of the one or more suggested response messages includes a previously stored chat message response corresponding to the customer intent and approved by a service agent; presenting, via a display, a transcript of a messaging sequence for the customer chat session concurrently with a user interface that enables the service agent to perform an action with respect to the one or more suggested response messages; and sending, via the chatbot access channel, a selected one of the one or more suggested response messages to the user device.

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.