Real-time and computationally efficient prediction of values for a quote variable in a pricing application

A non-transitory computer-readable medium comprising a computer program, that, when executed by a computer system having a customer relationship management (CRM) system running a customer-specific instance of a quote-creation application and a machine-learning server that executes a machine-learning module, enables the computer system to perform the following method for real-time and computationally-efficient calculation of a recommended value range for a quote variable, the method comprising:

• in response to a user of the CRM system engaging in a quote-creation process with the quote variable, the machine-learning module performing the following steps:
• retrieving attributes of a customer from a customer database associated with the CRM system for which the quote is being created;
• creating inputs for a probability density function (PDF) for the quote variable from the attributes;
• reducing the inputs using a dimension reduction algorithm;
• identifying an applicable PDF for the quote variable from a plurality of precomputed PDFs, wherein the applicable PDF is identified using the reduced inputs and a PDF look-up table stored on the storage device of the machine-learning server that associates input values with a PDF;
• retrieving a stored array from the storage device of the machine-learning server that estimates the identified PDF, wherein creating the stored PDF array comprises:
• retrieving attributes of customers in a set of training data (“training customers”),
• for each training customer, creating inputs for a PDF for the quote variable from the attributes and reducing the inputs using a dimension reduction algorithm,
• grouping training customers with similar reduced input values,
• creating a PDF for each group,
• summarizing each PDF as an array of data points, and
• storing the arrays;
• calculating a highest-density interval for values for the quote variable using the stored array, wherein the highest-density interval is calculated for a given probability;
• and
• displaying the calculated highest-density interval in a user interface of the customer-specific instance of the quote-creation application, wherein the highest-density interval is a prediction of values for the quote variable that the customer will accept with a given probability.

A computer system having a customer relationship management (CRM) system running a customer-specific instance of a quote-creation application and a machine-learning server that executes a machine-learning module for real-time and computationally-efficient calculation of a recommended value range for a quote variable, the computer system comprising:

• one or more processors;
• one or more memory units coupled to the one or more processors, wherein the one or more memory units store instructions that, when executed by the one or more processors, cause the computer system to perform the operations of:
• in response to a user of the CRM system engaging in a quote-creation process with the quote variable, the machine-learning module performing the following steps:
• retrieving attributes of a customer from a customer database associated with the CRM system for which the quote is being created;
• creating inputs for a probability density function (PDF) for the quote variable from the attributes;
• reducing the inputs using a dimension reduction algorithm;
• identifying an applicable PDF for the quote variable from a plurality of precomputed PDFs, wherein the applicable PDF is identified using the reduced inputs and a PDF look-up table stored on the storage device of the machine-learning server that associates input values with a PDF;
• retrieving a stored array from the storage device of the machine-learning server that estimates the identified PDF, wherein creating the stored PDF array comprises:
• retrieving attributes of customers in a set of training data (“training customers”),
• for each training customer, creating inputs for a PDF for the quote variable from the attributes and reducing the inputs using a dimension reduction algorithm,
• grouping training customers with similar reduced input values,
• creating a PDF for each group,
• summarizing each PDF as an array of data points, and
• storing the arrays;
• calculating a highest-density interval for values for the quote variable using the stored array, wherein the highest-density interval is calculated for a given probability;
• and
• displaying the calculated highest-density interval in a user interface of the customer-specific instance of the quote-creation application, wherein the highest-density interval is a prediction of values for the quote variable that the customer will accept with a given probability.