Nice Ltd

Incontact Inc.

Avaya Inc.

Avaya Management L.p.

Salil Dhawan

Rahul Vyas

Jibin George

Harsimran Jeet Singh

Sandeep Goynar

Omkar Shrihari Joglekar

Swati Kadu

Mukesh Kumar Agarwal

Harshit Kumar Sharma

Ashish Seth

Sebastien Roland Mancel

Tushar Deshmukh

Deepak Tiwari

Saiprasad Vyawahare

Harshad Ambekar

Mayura Nene

Damian Brhel

Niraj Riswadkar

Matan Keret

John A Young

Yuvraj Amrutrao Sawant

Jason Williams

Karthik Narayanan Srinivasan

Ashadeepa Debnath

Dhanendra Singh

Darren Holmes

Bhupendra Pandey

Akhilesh Suresh Mahajan

Anshu Kumar

Or Gershon

Chaitra Date

Somnath Kardak

Rajesh Katkar

Prithviraj Singh

Sourav Chauhan

Kunal Khanvilkar

Atish Sharma

Madhvi Sharma

Pramod Giri

A computerized-method for training and applying a Machine Learning (ML) textual behavioral-identification-model to authenticate an agent, in a digital multi-channel environment, is provided herein. The computerized-method may include: (i) training a ML-textual-behavioral-identification-model using retrieved textual responses of each agent in one or more historical-interactions which were conducted in a controlled environment, as a training dataset. The ML-textual-behavioral-identification-model may be configured to process the retrieved textual responses of each agent to generate a profile-identity-data for each agent to be used to authenticate identity of the agent; (ii) receiving a textual-response of each agent, when the agent starts an interaction with a customer; (iii) applying the textual-response of the agent to the ML-textual-behavioral-identification-module to authenticate an identity of the agent in real-time by calculating an imposter-probability score for the agent and; (iv) sending the imposter-probability score to a file-management-system to take one or more actions when the imposter-probability score is above a preconfigured-threshold.

Computerized-method and computerized-system for training and applying a machine learning (ML) textual behavioral identification model to authenticate an agent, in a digital multi-channel environment

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.

Systems and methods to triage contact center issues using an incident grievance score

Agent credibility systems and methods, and non-transitory computer readable media, include receiving a recorded interaction between a customer and a contact center agent; retrieving or determining a credibility divergence determinant (CDD) score of the contact center agent, wherein the CDD score is based on values of a plurality of credibility assessment factors for a time interval that are stored in a historical agent database, and wherein the lower the CDD score, the higher a credibility of the contact center agent; filtering out the recorded interaction when the CDD score is less than or equal to a defined threshold; and providing the recorded interaction to a supervisor of the contact center agent or a quality management application when the CDD score is greater than the defined threshold.

System and methods of identifying and utilizing agent credibility divergence in contact center quality processes

A system automatically prioritizes and resumes disconnected customer interactions. The system includes a processor to perform operations that include: receiving a list of disconnected customer interactions, including their metadata; using a prioritization module and the metadata, assigning a priority score to each disconnected interaction; with a queuing module and the respective priority scores, with an agent assignment module, arranging the disconnected interactions in a priority order; assigning an available agent to each disconnected interaction in the priority order; selecting a channel for each disconnected interaction in the priority order, based on a channel recommendation module and the metadata; and re-connecting a disconnected customer with the respective agent via the respective channel for each respective disconnected customer interaction in the priority order.

System and method to prioritize and resume disconnected customer interactions

A computerized system and method may analyze data representing remotely connected computer systems, which may be for example used by agents as part of their activity and/or routine within a given system or organization, to assess and/or quantify critical factors that may impact the output and/or efficiency for a given agent or a plurality of agents within the system or organization. In a computerized-system comprising one or more processors, and a memory including a data store of agents' data and metrics, embodiments of the invention may collect a plurality of data streams, which may include call and/or chat data and/or metadata; store the data streams in an appropriate database; periodically extract parts of the stored data from the database, and calculate effectiveness scores based on the extracted data; and transmit the calculated scores to potential data utilizers and/or target applications, to be presented via a user interface thereof.

System and method for identifying and utilizing agent working from home impact score

A computer-implemented method for dynamically prioritizing inbound interactions in a digital multi-channel contact center. The computer-implemented method includes for each inbound interaction via a digital channel: (i) operating an interaction analyzer module to extract one or more metadata parameters from the inbound interaction; (ii) operating a prioritization module to calculate a Digital Interaction Priority Score (DIPS) of the inbound interaction based on the one or more metadata parameters; and (iii) forwarding the DIPS to an interaction distribution module to route the inbound interaction to an agent based on the DIPS. The DIPS is periodically updated until the interaction is assigned to the agent.

Method for dynamically prioritizing inbound interactions in a digital multi-channel contact center

A method and system for data synchronization in a multi-tenant data synchronization system is provided. The method and system can allow for prioritizing events from a plurality of tenants for synchronization based at least one, a tenant prioritization score.

Systems and methods for data synchronization in a multi-tenant synchronization system

A computerized-method for recommending activities for an agent, in a contact center is provided herein. The computerized-method includes operating an activities-recommendation module, for agents in a contact center, during a work-shift of an agent. The operating of the activities-recommendation module includes: (i) operating a machine-learning-model to calculate a motivation-score; (ii) associating a preconfigured score-range for the calculated motivation score; (iii) collecting activities which were preassigned to the preconfigured score-range; (iv) retrieving one or more predetermined agent favorite activities; (v) retrieving one or more agent history activities, which were previously selected by the agent; (vi) calculating an activity-occurrence score for each activity of the collected activities, which were preassigned to the preconfigured score-range, by adding the: (a) motivation-score; (b) the one or more predetermined agent favorite activities; and (c) the one or more agent history activities; and (vii) displaying a rated list of recommended activities, according to the calculated activity-occurrence score.

System and method to formulate effective energy breaks in a contact center

A computerized-method for reducing wait-time in a multichannel-contact-center. For each query that is not resolved by a bot: a. selecting a digital-channel-type from a plurality of digital-channel-types; b. operating an aggregator module on the selected digital-channel-type to aggregate the received query and similar pending queries into a group-of-queries; c. operating a tracker-module on the group-of-queries to identify a query-resolution and to send a notification as to the query-resolution to the customer via the digital-channel-type that the query has been received; and d. repeating operations a.-c. until a query-resolution has been identified or all digital-channel-types in the plurality of digital-channel-types have been selected.

System and method for reducing wait-time in a multichannel contact center

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.

System and method for redirecting inbound-voice-interactions to digital channels in a contact center

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.

System and method to gauge agent self-assessment effectiveness in a contact center

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. The RIR score is sent to a platform by which the platform is preconfigured to distribute the interaction-recording for evaluation, based on the RIR score.

System and method for determining and utilizing repeated conversations in contact center quality processes

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.

Upfront customer time consideration along with optimized agent desktop in a contact center

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.

System and method for identifying similar queries by a customer on different digital channels in a multichannel contact center

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.

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Pune, India

Salil Dhawan