Abstract: A method for customer-based outcome prediction that includes receiving recordings of interactions with customers in a customer group, analyzing the recordings of the interactions to generate interaction data, and building a predictive model using the interaction data, the predictive model identifying a variable relevant to predicting a likelihood of an identified outcome occurring in association with future interactions with customers in the customer group. The method also includes receiving a recording of a current interaction with a first customer, the first customer being in the customer group and analyzing the recording of the current interaction with the first customer to generate current interaction data. Further, the method includes adding the current interaction data to a first customer profile associated with the first customer and applying the predictive model to the first customer profile to predict the likelihood of the identified outcome occurring in association with the current interaction.

Abstract: The methods, apparatus, and systems described herein facilitate decision-making by providing predictions of outcomes and behaviors. The methods include receiving a communication between an agent and a prospect, analyzing density of keywords in a text version of the communication to determine the type of communication and amount of value time, determining if the communication is a first meaningful contact based on the type of communication and amount of value time, and predicting a likelihood of a prospect's action based on the determination.

Abstract: The methods, apparatus, and systems described herein facilitate instructor decision-making based on an analysis of communication(s) between an instructor and a student, including to provide predictions of student outcomes. The methods include receiving communication(s) posted by a student, detecting personality types along with keywords and phrases used by the student and the instructor with a psychologically-based linguistic analysis of the communication(s), scoring the student and instructor communications based on the detected keywords and phrases compared to a library of keywords and phrases, aggregating the student scores by personality type and instructor, correlating the student and instructor scores with historical student data, creating an evaluation report to provide guidance to the students for enrollment based on the correlated scores, and displaying the evaluation report on a student device.

Abstract: A method for customer-based outcome prediction that includes receiving recordings of interactions with customers in a customer group, analyzing the recordings of the interactions to generate interaction data, and building a predictive model using the interaction data, the predictive model identifying a variable relevant to predicting a likelihood of an identified outcome occurring in association with future interactions with customers in the customer group. The method also includes receiving a recording of a current interaction with a first customer, the first customer being in the customer group and analyzing the recording of the current interaction with the first customer to generate current interaction data. Further, the method includes adding the current interaction data to a first customer profile associated with the first customer and applying the predictive model to the first customer profile to predict the likelihood of the identified outcome occurring in association with the current interaction.

Abstract: The methods, apparatus, and systems described herein facilitate instructor decision-making based on an analysis of communication(s) between an instructor and a student, including to provide predictions of student outcomes. The methods include receiving communication(s) posted by a student, detecting personality types along with keywords and phrases used by the student and the instructor with a psychologically-based linguistic analysis of the communication(s), scoring the student and instructor communications based on the detected keywords and phrases compared to a library of keywords and phrases, aggregating the student scores by personality type and instructor, correlating the student and instructor scores with historical student data, creating an evaluation report to provide guidance to the students for enrollment based on the correlated scores, and displaying the evaluation report on a student device.

Abstract: The methods, apparatus, and systems described herein facilitate decision-making by providing predictions of student outcomes and behaviors. The methods include receiving a communication posted by a student, identifying keywords in text of the communication associated with one or more student metrics, scoring the communication for at least one student metric, and predicting a likelihood of a student outcome based on the score.

Abstract: The methods, apparatus, and systems described herein provide weighted promoter scores (WPS) for customers that have increased precision and/or accuracy compared to a traditional NPS®. The methods include receiving a communication between an agent and a customer, extracting a data attribute from the communication including personality type of the customer, inputting the personality type into an algorithm trained to output a WPS, and outputting a WPS for the customer.

Abstract: The methods, apparatus, and systems described herein facilitate decision-making by providing predictions of student outcomes and behaviors. The methods include receiving a communication posted by a student, identifying keywords in text of the communication associated with one or more student metrics, scoring the communication for at least one student metric, and predicting a likelihood of a student outcome based on the score.

Abstract: The methods, apparatus, and systems described herein facilitate decision-making by providing predictions of outcomes and behaviors. The methods include receiving a communication between an agent and a prospect, analyzing density of keywords in a text version of the communication to determine the type of communication and amount of value time, determining if the communication is a first meaningful contact based on the type of communication and amount of value time, and predicting a likelihood of a prospect's action based on the determination.

Abstract: The methods, apparatus, and systems described herein facilitate making hiring decisions. The methods include identifying keywords associated with one or more categories relevant to a job position, receiving the candidate's answers to interview questions, reviewing the answers for the identified keywords, and outputting a score for the one or more categories based on a density of the keywords in the answers.

Abstract: A method for customer-based outcome prediction that includes receiving recordings of interactions with customers in a customer group, analyzing the recordings of the interactions to generate interaction data, and building a predictive model using the interaction data, the predictive model identifying a variable relevant to predicting a likelihood of an identified outcome occurring in association with future interactions with customers in the customer group. The method also includes receiving a recording of a current interaction with a first customer, the first customer being in the customer group and analyzing the recording of the current interaction with the first customer to generate current interaction data. Further, the method includes adding the current interaction data to a first customer profile associated with the first customer and applying the predictive model to the first customer profile to predict the likelihood of the identified outcome occurring in association with the current interaction.

Abstract: A method for customer-based outcome prediction that includes receiving recordings of interactions with customers in a customer group, analyzing the recordings of the interactions to generate interaction data, and building a predictive model using the interaction data, the predictive model identifying a variable relevant to predicting a likelihood of an identified outcome occurring in association with future interactions with customers in the customer group. The method also includes receiving a recording of a current interaction with a first customer, the first customer being in the customer group and analyzing the recording of the current interaction with the first customer to generate current interaction data. Further, the method includes adding the current interaction data to a first customer profile associated with the first customer and applying the predictive model to the first customer profile to predict the likelihood of the identified outcome occurring in association with the current interaction.