Systems and methods for identifying a time of intervention during a customer call

A device, comprising:

• one or more processors configured to:
• receive live call data from a customer service call;
• preprocess the live call data to generate utterances;
• identify call phases associated with the utterances using natural language processing techniques,
• wherein the natural language processing techniques analyze the utterances for greeting phases, customer problem explanation phases, agent resolution phases, and customer acceptance or rejection phases;
• refine the call phases using a multilayer perceptron model to classify the utterances into classified call phases;
• generate a session vector, a customer vector, and an agent activity vector based on the classified call phases;
• estimate a time of intervention for agent intervention using the session vector, the
• customer vector, the agent activity vector, and a call phase time modifier;
• derive an optimal intervention point based on the time of intervention;
• and
• provide a recommendation for agent intervention during the customer service call at the optimal intervention point.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive live call data from a customer service call;
• preprocess the live call data to generate utterances;
• identify call phases associated with the utterances using natural language processing techniques;
• refine the call phases using a multilayer perceptron model to classify the utterances into classified call phases;
• generate a session vector, a customer vector, and an agent activity vector based on the classified call phases;
• estimate a time of intervention for agent intervention using the session vector, the customer vector, the agent activity vector, and a call phase time modifier;
• derive an optimal intervention point based on the time of intervention;
• provide a recommendation for agent intervention during the customer service call at the optimal intervention point;
• and
• provide real-time updates and feedback to an agent based on the optimal intervention point during the customer service call.