Nice Ltd

Harshit Kumar Sharma

Salil Dhawan

Rahul Vyas

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 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 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.

Growing community of inventors

Maharashtra, India

Harshit Kumar Sharma