Method and system for determination of personality traits of agents in a contact center

A system for determination of personality traits of agents in a contact center, comprising:

• a processing circuitry;
• and
• a memory communicatively coupled to the processing circuitry, wherein the memory stores processor instructions, which when executed by the processing circuitry, cause the processing circuitry to:
• retrieve textual data corresponding to a conversation between a first agent and a first customer, wherein the textual data comprises a transcript of the conversation;
• generate a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model, wherein the natural language justification comprises one or more sentences, and wherein the one or more sentences comprise a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits;
• determine a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label;
• compute a weighted average value of the value and a subsequently determined value for each of the set of personality traits through the first ML model, wherein the subsequently determined value is based on textual data of one of:
• a conversation between the first agent and a second customer;
• or
• a subsequent conversation between the first agent and the first customer;
• and
• generate an updated value for each of the set of personality traits, wherein the updated value is the weighted average value.

A computer-readable medium comprising instructions that, when executed by processing circuitry of a computing system, cause the computing system to perform a method for determination of personality traits of agents in a contact center, the method comprising:

• retrieving textual data corresponding to a conversation between a first agent and a first customer, wherein the textual data comprises a transcript of the conversation;
• generating a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model, wherein the natural language justification comprises one or more sentences, and wherein the one or more sentences comprise a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits;
• determining a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label;
• computing a weighted average value of the value and a subsequently determined value for each of the set of personality traits through the first ML model, wherein the subsequently determined value is based on textual data of one of:
• a conversation between the first agent and a second customer;
• or
• a subsequent conversation between the first agent and the first customer;
• and
• generating an updated value for each of the set of personality traits, wherein the updated value is the weighted average value.