Intelligent prediction systems and methods for conversational outcome modeling frameworks for sales predictions

An intelligent prediction system for conversational outcome prediction, comprising:

• one or more processors;
• one or more memory components communicatively coupled to the one or more processors;
• and
• machine-readable instructions stored in the one or more memory components that cause the intelligent prediction system to perform at least the following when executed by the one or more processors:
• receive text data comprising a plurality of speaker turn segments of a transcription of a conversation between two or more individuals regarding a sale offer, each speaker turn segment of the plurality of speaker turn segments of the transcription representative of a turn in the conversation associated with speech data of one of the two or more individuals, the plurality of speaker turn segments collectively representative of the conversation up to a point of time;
• vectorize each speaker turn segment of the text data to assign an associated numerical value to each segment;
• apply a loss function configured to minimize error to each speaker turn segment post vectorization;
• generate, via a neural network prediction model, a point in time bind probability representative of a likelihood of a successful outcome of the sale offer at the point in time based on (i) a speaker turn segment bind probability of a speaker turn segment at the point in time and (ii) memory data associated with the plurality of speaker turn segments up to the point in time;
• and
• generate, via the neural network prediction model, a speaker turn segment impact score at the point in time by subtracting an immediately preceding point in time bind probability from the point in time bind probability.

A method of conversational outcome prediction using an intelligent prediction system, the method comprising:

• receiving, with one or more processors of the intelligent prediction system, text data comprising a plurality of speaker turn segments of a transcription of a conversation between two or more individuals regarding a sale offer, each speaker turn segment of the plurality of speaker turn segments of the transcription representative of a turn in the conversation associated with speech data of one of the two or more individuals, the plurality of speaker turn segments collectively representative of the conversation up to a point of time;
• vectorizing, with the one or more processors, each speaker turn segment of the text data to assign an associated numerical value to each speaker turn segment;
• generating, via a neural network prediction model, a point in time bind probability representative of a likelihood of a successful outcome of the sale offer at the point in time based on (i) a speaker turn segment bind probability of a speaker turn segment at the point in time and (ii) memory data associated with the plurality of speaker turn segments up to the point in time;
• and
• generating, via the neural network prediction model, a speaker turn segment impact score at the point in time by subtracting an immediately preceding point in time bind probability from the point in time bind probability.