Massachusetts Mutual Life Insurance‘s patent involves a system and method for automatically calling back customers based on predictive models. The processor-based method includes training a logistic regression model to determine the likelihood of repeated call-backs and interactions during future calls. The system aims to reduce the risk of missed call-backs and improve customer service efficiency. GlobalData’s report on Massachusetts Mutual Life Insurance gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Massachusetts Mutual Life Insurance, Predictive modeling techniques was a key innovation area identified from patents. Massachusetts Mutual Life Insurance's grant share as of April 2024 was 92%. Grant share is based on the ratio of number of grants to total number of patents.

Automated customer call-back system using predictive model

Source: United States Patent and Trademark Office (USPTO). Credit: Massachusetts Mutual Life Insurance Company

A recently granted patent (Publication Number: US11948153B1) outlines a processor-based method for managing customer call-backs in a call center setting. The method involves training a logistic regression model using historical call data, monitoring inbound calls to detect call terminations, and classifying customers into call-back groups based on likelihood indicators. The system includes a processor, a call-back management module, and a logistic regression model that applies regularization to feature selection. By analyzing IVR data and call history, the system can predict repeated call-back likelihood and future interactions, directing call-back procedures accordingly. The method also involves retrieving customer information from a CRM database and selecting preferred agents for call-backs based on classification results.

Additionally, the patent describes a system that includes a telephone calling device, an IVR unit, and a CRM database storing advisor records and product history data. The system utilizes a logistic regression model to determine call-back metrics for each advisor record and classify customers into call-back groups. By applying regularization to feature selection, the model generates likelihood indicators for repeated call-backs and future interactions. The system can initiate call-backs with preferred agents or execute automated call-back procedures based on classification results. The method also involves monitoring inbound calls, updating call records, and retrieving call history data to enhance the call-back process. Overall, the system aims to optimize call-back management in call center operations by leveraging predictive analytics and historical data analysis.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.