Intent analysis for call center response generation

A computer-implemented method, comprising:

• obtaining conversation data, wherein the conversation data corresponds to one or more conversations between customers and agents associated with a client, and wherein the one or more conversations correspond to a set of intents;
• calculating one or more performance metrics associated with the agents, wherein the one or more performance metrics are calculated based on an evaluation of the conversation data, and wherein the one or more performance metrics correspond to performance of the agents in addressing the set of intents;
• using a trained machine learning algorithm to generate one or more insights, wherein the trained machine learning algorithm uses the conversation data and the one or more performance metrics as input;
• generating a set of predictions corresponding to adherence to the one or more insights, wherein the set of predictions indicate a predicted measure of improvement in performance of the agents if the one or more insights are implemented;
• and
• providing the one or more insights and the set of predictions.

A system, comprising:

• one or more processors;
• and
• memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
• obtain conversation data, wherein the conversation data corresponds to one or more conversations between customers and agents associated with a client, and wherein the one or more conversations correspond to a set of intents;
• calculate one or more performance metrics associated with the agents, wherein the one or more performance metrics are calculated based on an evaluation of the conversation data, and wherein the one or more performance metrics correspond to performance of the agents in addressing the set of intents;
• use a trained machine learning algorithm to generate one or more insights, wherein the trained machine learning algorithm uses the conversation data and the one or more performance metrics as input;
• generate a set of predictions corresponding to adherence to the one or more insights, wherein the set of predictions indicate a predicted measure of improvement in performance of the agents if the one or more insights are implemented;
• and
• provide the one or more insights and the set of predictions.

A non-transitory computer-readable storage medium, storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:

• obtain conversation data, wherein the conversation data corresponds to one or more conversations between customers and agents associated with a client, and wherein the one or more conversations correspond to a set of intents;
• calculate one or more performance metrics associated with the agents, wherein the one or more performance metrics are calculated based on an evaluation of the conversation data, and wherein the one or more performance metrics correspond to performance of the agents in addressing the set of intents;
• use a trained machine learning algorithm to generate one or more insights, wherein the trained machine learning algorithm uses the conversation data and the one or more performance metrics as input;
• generate a set of predictions corresponding to adherence to the one or more insights, wherein the set of predictions indicate a predicted measure of improvement in performance of the agents if the one or more insights are implemented;
• and
• provide the one or more insights and the set of predictions.