A method performed by a computing system includes collecting information on transactions in a telecommunication system, using the information on transactions to create a plurality of event objects, each of the event objects associated with a telecommunication event, associating each of the event objects with a Key Performance Indicator (KPI), applying the event objects to a plurality of inference functions, each inference functions using the set of parameters as inputs and the KPIs of the event objects as outputs to create a model that infers a relationship between the set of parameters and the KPIs, and analyzing metadata from each of the inference functions to determine which of the set of parameters was used to predict an outcome leading to the KPI.

Techniques for agent-assist systems to provide context-aware, subdocument-granularity recommended answers to agents that are attempting to answer queries of users. The agent-assist system may obtain collections of documents that include information for responding to queries, and analyze those documents to identify subdocuments that are associated with different semantics or meanings. Subsequently, any queries received can be analyzed to identify their semantics, and relevant subdocuments can be identified as having similar semantics. When the agent-assist system presents the agent with the relevant documents, it may highlight or otherwise indicate the relevant subdocument within the document for quick identification by the agent. Further, the agent-assist system may collect feedback from the agent and/or user to determine a relevancy of the recommended answers. The agent-assist system can use the feedback to improve the quality of the recommended answers provided to the agents.

A computerized-method for calculating an agent skill-satisfaction-index and utilization thereof, is provided herein. The computerized-method includes operating an Agent-Skill-Satisfaction-Index (ASSI)-scoring module. The ASSI-scoring module may include: (a) operating an interaction microservice to retrieve one or more agent's interactions which were conducted during the first preconfigured-period and related interaction-level key performance indicator (KPI)s, from a data store of interactions; (b) organizing the retrieved one or more agent's interactions in one or more groups by one or more second-preconfigured-periods; (c) checking a duration of each skill from a set of skills of an agent if it is assigned to the agent above a preconfigured-period-threshold to be marked as a related-skill; (d) for each group, calculating a skill-core based on a calculated evaluation-sum of each interaction in the group that is associated with a related-skill; and (e) calculating an ASSI-score based on the calculated one or more skill scores.

Disclosed herein are system, method, and computer program product embodiments for categorizing customer complaints on social media using a model trained on customer voice calls or chats with agents. Additionally, users interested in monitoring regulatory compliance issues based on customer complaints can receive notifications regarding complaints that are linked to regulatory topic areas, without the need to manually scan vast numbers of social media postings.

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.

In the field of telecommunications, methods, systems, and tangible, non-transitory computer-readable mediums comprising program code are disclosed that comprise receiving a first agent, a second agent, a third agent, and a fourth agent available for pairing to a contact; and selecting the first agent for pairing to the contact based on a pairing strategy, wherein the pairing strategy is configured such that if the third agent and fourth agent had not been available, the second agent would have been selected for pairing to the contact, wherein the pairing strategy is configured such that if the first agent had not been available, the third agent would have been selected for pairing to the contact.

A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

A method and an apparatus for predicting satisfaction of a customer pursuant to a call between the customer and an agent is provided. The method comprises receiving a transcribed text of the call, dividing the transcribed text into a plurality of phases of a conversation, extracting at least one call feature for each of the plurality of phases, receiving call metadata, extracting metadata features from the call metadata, combining the call features and the metadata features, and generating an output, using a trained machine learning (ML) model, based on the combined features, indicating whether the customer is satisfied or not. The ML model is trained to generate an output indicating whether the customer is satisfied or not, based on an input of the combined features.

Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.

A sentiment-based score is determined for a contact center engagement between a first contact center service operator and a contact center user. The sentiment-based score is indicated within a graphical user interface displaying information associated with multiple contact center engagements at a device of a second contact center service operator. Based on a request to participate in the contact center engagement received from the device of the second contact center service operator via the graphical user interface, information associated with the contact center engagement is transmitted to the device of the second contact center service operator, and a contact center session involving a device of the contact center user and the device of the second contact center service operator is established.