Nice Ltd

Incontact Inc.

Rahul Vyas

Salil Dhawan

Omkar Shrihari Joglekar

Harshit Kumar Sharma

Ashish Seth

Sebastien Roland Mancel

Mukesh Kumar Agarwal

Saiprasad Vyawahare

Tushar Deshmukh

Deepak Tiwari

Piyush Shirke

Damian Brhel

Matan Keret

Jason Williams

LeAnn Hopkins

Or Gershon

Pankul Gupta

Mayur Bhalke

Sourav Chauhan

Kunal Khanvilkar

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

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

Agent evaluation 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 an interaction divergence range for each of a plurality of interaction parameters for the recorded interaction; calculating a form divergence determinant (FDD) score for each of a plurality of evaluation forms, wherein the lower the FDD score, the more suitable an evaluation form is for the recorded interaction; filtering out evaluation forms having an FDD score greater than a predefined threshold; ranking evaluation forms having an FDD score lower than the predefined threshold based on their FDD score; and providing a list of the ranked evaluation forms to a supervisor of the contact center agent.

System and method of efficient selection of evaluation form

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.

Systems and methods for optimal channel selection

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.

System and method for determining and utilizing after-call-work factor in contact center quality processes

A computerized-method for enhancing customers satisfaction by assigning abandoned inbound-interactions to agents each on a satisfactory-digital-channel type. The computerized-method may include operating an Abandoned-Interactions-Input-Utilization (AIIU) module. The AIIU-module may include: (a) operating an accumulator-module to: (i) retrieve abandoned inbound-interactions from the data store of abandoned inbound-interactions and (ii) forward details of the abandoned inbound-interactions to an analyzer-module; (b) for each abandoned inbound-interaction of the abandoned inbound-interactions, a. operating the analyzer-module on the forwarded details of abandoned inbound-interactions to: (i) extract query context and metadata; and (ii) identify a satisfactory-digital-channel type; b. operating a processor-module to: (i) identify an agent having skills to handle the extracted query and (ii) prepare a task based on the extracted query context and metadata and the identified satisfactory digital-channel type; and (c) sending the task to a routing-system to be assigned to the identified agent for operation thereof via the identified satisfactory-digital-channel type.

System and method enhancing customers satisfaction by assigning abandoned inbound-interactions to agents each on a satisfactory digital-channel type

A computerized-method for reducing wait-time in a multichannel-contact-center is provided herein. For each query that is not resolved by a hot, operating a Channel-Agnostic-Queuing (CAQ) module. The CAQ-module includes: a. selecting a digital-channel-type in a predetermined order; b. operating an aggregator module on the selected digital-channel-type to: (i) identify similar pending queries in a queue-database related to the digital-channel-type: and (ii) aggregate the received query and the identified 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 and to related customers of the identified similar pending queries; and d. when a total-parking-period of the plurality of digital-channel-types is lower than an expected wait-time of the received query and the received query hasn't been resolved, repeating operations a.-c. until a query-resolution has been identified or all digital-channel-types have been selected.

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.

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Jodhpur-Rajasthan, India

Rahul Vyas