Abstract: A computerized-method for redirecting, inbound-voice-interactions to digital channels in a contact center, is provided herein. The computerized-method includes: (i) operating a digital-qualifier module to determine a digital-medium-transition-quotient, of an inbound-voice-interaction of a customer in an inbound-queue. The digital-medium-transition-quotient is an indication of a level of suitability of a digital-communication-channel to resolve a customer issue; (ii) operating an interaction-redirection module to determine a digital-communication-channel for redirection of the inbound-voice-interaction, based on customer-preference and the determined digital-medium-transition-quotient; and (iii) forwarding the inbound-voice-interaction and the determined digital-communication-channel to an Automatic Call Distribution (ACD) system to be carried-out by an agent via the determined digital-communication-channel when the determined digital-medium-transition-quotient is above a preconfigured threshold.

Abstract: A computerized-method for service monitoring and rate limiting for a service for a request router in a SaaS-platform-contact-center is provided herein. When a service-monitoring-module for a microservice of the SaaS-platform-contact-center is operating and a request-router is in a throttle-mode for a service-of-the-microservice, operating a Tenants Prioritization (TP) module.

Abstract: A computerized-method for identifying truthfulness of a disposition, in a contact center is provided herein.

Abstract: A computerized-method for improving queries operation in a multichannel contact center is provided herein. The computerized-method includes: (i) operating a stream processing application for each new query of a customer to store query-related data and to identify one or more queries of the customer in a cloud-contact data store. The cloud-contact data store may have one or more interactions-queue types, when one or more queries have been identified: (a) operating a repetition module on the identified one or more queries of the customer to filter out two or more queries having a common query-topic; (b) operating a Natural Language Understanding (NLU) module on the filtered two or more queries having a common topic to identify two or more identical queries. Each query of the identified two or more identical queries have a unique query identification number; and (c) handling the two or more identical.

Abstract: A computerized-method for service monitoring and rate limiting for a service for a request router in a SaaS-platform-contact-center is provided herein. When a service-monitoring-module for a microservice of the SaaS-platform-contact-center is operating and a request-router is in a throttle-mode for a service-of-the-microservice, operating a Tenants Prioritization (TP) module.

Abstract: 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.

Abstract: A computerized-method for identifying and utilizing effectiveness of an agent elevating channels during an interaction, in an Omnichannel-Session-Handling environment, is provided herein. The computerized-method may operate, during a duty-cycle, an Elevated Interaction Efficacy (EIE) module for each agent in a data-storage of agents. The EIE-module may include: (a) operating an interaction-module to retrieve one or more interactions of the agent; (b) filtering out from the retrieved interactions, one or more elevated interactions, based on one or more attributes from metadata of the retrieved interactions; (c) calculating an Elevated Interaction Handling (EIH) score for the agent based on one or more attributes from the metadata of the one or more elevated interactions; (d) storing the calculated EIH score in the data-storage of agents; and (e) sending the EIH score to one or more applications, to take one or more follow-up actions based on the EIH score and a calculated EIH threshold.

Abstract: Embodiments described herein provide an optimal communication channel recommendation engine by assessing whether the environment the customer is situated in is conducive to the channel selected by the customer. Specifically, the optimal channel recommendation engine obtains data artifacts indicative of ambient noise, motion, customer sentiment, network quality, customer focus, and/or the like to assess quality of the environment and recommend an optimal channel for the communication between the customer and the call agent. With the recommendation to switch to a different channel, the client component resumes communication with the new channel and retains the context of the interaction.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes: retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard during the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes: retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard during the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: Embodiments described herein provide an optimal communication channel recommendation engine by assessing whether the environment the customer is situated in is conducive to the channel selected by the customer. Specifically, the optimal channel recommendation engine obtains data artifacts indicative of ambient noise, motion, customer sentiment, network quality, customer focus, and/or the like to assess quality of the environment and recommend an optimal channel for the communication between the customer and the call agent. With the recommendation to switch to a different channel, the client component resumes communication with the new channel and retains the context of the interaction.

Abstract: A computerized-method for identifying and utilizing effectiveness of agent handling multiple concurrent multi-channel interactions is provided herein. The computerized-method includes operating of a Multiple Multi-Channel Effectiveness (MME) module. The MME module includes: (a) operating an interaction module to retrieve one or more concurrent interactions of an agent from the data storage of interactions, according to a time range; (b) calculating an MME score for the agent based on metadata of the one or more concurrent interactions which defines the ability of the agent to handle multiple concurrent multi-channel interactions simultaneously; (c) storing the calculated MME score in the data storage of agents; and (d) sending the MME score to the one or more applications to take one or more follow-up actions based on the MME score.

Abstract: There is thus provided a computerized-method for calculating a Repeated-Information-Request (RIR) score of an interaction in a contact center, by which a related interaction-recording is filtered for evaluation. The computerized-method is operating in a computerized-system which includes a processor, a data-storage and a memory to store the data-storage. The processor is operating a RIR score calculation module. The operating of the RIR score calculation module includes: (i) retrieving from the data-storage an interaction-recording and an interaction-recording-length thereof; (ii) operating a module on the retrieved interaction-recording to count a number of requests to repeat information; (iii) calculating a RIR score according to the number of requests to repeat information and the interaction-recording-length; and (iv) storing the RIR score of the retrieved interaction-recording in the data-storage.

Abstract: A computerized-method for prioritizing tenants for a service for a request-router in a cloud-based Software-as-a-Service-platform contact-center, is provided herein. When a service-monitoring module for a microservice is operating and a request-router is in a throttle-mode for each service of the microservice, the computerized-method may be operating a Tenants-Prioritization (TP) module for each tenant.

Abstract: A computerized-method for calculating an After-Call-Work (ACW) factor of an interaction in a contact center, by which a related recording may be filtered for evaluation is provided herein. The method includes an After-Call-Work (ACW) factor calculation module. The operating of the ACW factor calculation module includes: (i) receiving agent recording of the interaction. (ii) aggregating data fields associated with: (a) the interaction; and (b) the customer; (iii) retrieving ACW time of the interaction; (iv) forwarding the aggregated data fields to a machine learning model; (v) operating the machine learning model to calculate a predicted ACW time, based on the aggregated data fields; (vi) calculating an ACW factor based on the received time of ACW and the calculated predicted ACW time; and (vii) sending the calculated ACW factor to a platform by which the platform is preconfigured to distribute the interaction for evaluation, based on the ACW factor.

Abstract: Multiple vendors often contribute a portion of their hardware and/or software to a customer to enable a particular feature. When issues occur, a contact center for more than one vendor may be required to perform an action in order to identify a resolution to the issue and apply the resolution. By coordinating multiple contact centers with smart contracts via a distributed leger, rules may be developed for the creation of blocks for notifications, escalations, tasks, and other actions or informational notifications to resolve the issue. Additionally, the blockchain may serve as a permanent record provide a history of actions taken, by whom, and when.

Abstract: Electronic conferences are a common method of conducting a meeting when the participants are not at the same location. When a conference drops a portion of the audio, such carrying speech from a speaker, participants may be excluded from the full content of the conference. By having a device associated with a speaking participant convey both audio and generated text from the speech provided by the speaking participant, a server may be able to determine that speech is missing from the audio portion and automatically insert text content. As a result, poor audio quality may be mitigated with text and omitting unwanted text when the audio quality is sufficient.

Abstract: Embodiments described herein provide an optimal communication channel recommendation engine by assessing whether the environment the customer is situated in is conducive to the channel selected by the customer. Specifically, the optimal channel recommendation engine obtains data artifacts indicative of ambient noise, motion, customer sentiment, network quality, customer focus, and/or the like to assess quality of the environment and recommend an optimal channel for the communication between the customer and the call agent. With the recommendation to switch to a different channel, the client component resumes communication with the new channel and retains the context of the interaction.

Abstract: Systems for and methods of assessing the priority of a customer reported issue include receiving input regarding a customer issue experienced by a customer; calculating an incident grievance score by inserting the received input into a machine learning model; assigning a priority to the customer issue based on the calculated incident grievance score; receiving updated input regarding the customer issue; periodically recalculating the incident grievance score for the customer issue by inserting the received input and the updated input into the machine learning model; changing the priority of the customer issue when the recalculated incident grievance score differs from the calculated incident grievance score; and notifying a team assigned to fix the customer issue when the priority of the customer issue changes.

Abstract: A computerized-method for calculating an After-Call-Work (ACW) factor of an interaction in a contact center, by which a related recording may be filtered for evaluation is provided herein. The method includes an After-Call-Work (ACW) factor calculation module. The operating of the ACW factor calculation module includes: (i) receiving agent recording of the interaction; (ii) aggregating data fields associated with: (a) the interaction; and (b) the customer; (iii) retrieving ACW time of the interaction; (iv) forwarding the aggregated data fields to a machine learning model; (v) operating the machine learning model to calculate a predicted ACW time, based on the aggregated data fields; (vi) calculating an ACW factor based on the received time of ACW and the calculated predicted ACW time; and (vii) sending the calculated ACW factor to a platform by which the platform is preconfigured to distribute the interaction for evaluation, based on the ACW factor.