Machine learning system for routing optimization based on historical performance data

US11128754B1 · kind B1 · utility

Assignee

• Allstate Insurance Company · US

Inventors

• Soren K. Lundsgaard · Inverness, US
• Anthony Pham · Irvine, US
• Ryan P Dunn · Brooklyn, US

Key dates

• Filing date: Nov 16, 2020
• Grant date: Sep 21, 2021
• Expiry date: Nov 16, 2040

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04M2203/551
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

Aspects of the disclosure relate to using machine learning for optimized call routing. A computing platform may receive requests to establish a voice call session. Based on corresponding phone numbers, the computing platform may identify demographic information for corresponding clients. Using a machine learning model and based on the demographic information and representative performance data, the computing platform may score potential client-representative combinations to indicate likelihoods of a successful outcome resulting from establishing a voice call session between the respective client and representative. Scoring the potential client-representative combinations may be based on fall off rates, indicating changes in representative effectiveness as hold time increases. The computing platform may adjust the scores based on a historical frequency of interaction between each representative and clients corresponding to the identified demographic information. Based on the adjusted scores, the computing platform may select at least one of the potential client-representative combinations.