Regression-Based Insurance Lead Prioritization
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Solution Overview
Problem
Insurance providers face challenges in efficiently managing and prioritizing multiple insurance re-quotations to maximize conversion rates from previous unclosed quotations, as existing methods lack a systematic approach to optimize follow-up and re-engagement with potential leads.
Innovation Solution
A computer-assisted method using statewide and national regression models to calculate the probability of insurance purchase based on differences in quotations, such as increased coverage or reduced costs, allowing for ranked lists to be generated and used to prioritize follow-up efforts by insurance agents and agencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If insurance providers manually manage and follow up on unclosed quotations without a systematic prioritization approach, then agents can contact potential leads, but the conversion rate remains low due to inefficiency in managing multiple re-quotations
Solution Approach 1:
The patent applies parameter changes by using regression models to calculate probability scores based on multiple variables (demographic data, quotation characteristics, timing factors). These probability parameters prioritize leads systematically, transforming manual intuition-based prioritization into data-driven automated scoring that increases conversion rates without proportionally increasing system complexity
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a mediator between raw quotation data and agent actions. The regression model serves as an intermediary that processes multiple input variables and outputs prioritized lead lists, enabling agents to focus on high-probability prospects without manually analyzing all unclosed quotations
2Productivity
If insurance providers use a uniform process to manage all unclosed quotations equally, then all leads receive equal attention, but resources are wasted on low-probability leads reducing overall efficiency
Solution Approach 1:
The patent applies local quality by treating each lead individually with customized probability scoring based on their specific characteristics (demographics, quotation details, timing). Instead of uniform treatment, the system calculates unique probability scores for each lead using regression models that consider individual-specific variables, thereby optimizing resource allocation without losing lead-specific information
Solution Approach 2:
The patent segments the unclosed quotation population into prioritized groups based on calculated probability scores. The system divides leads into high-probability, medium-probability, and low-probability segments, enabling differential resource allocation. This segmentation transforms the homogeneous treatment of all leads into targeted strategies for different lead categories, improving efficiency while preserving individual characteristics through the scoring mechanism
Data Source
AI summary
A computer-assisted method for providing re-quotations for insurance coverage may include receiving a list of insurance leads corresponding to individuals who received a previous quotation for insurance coverage but did not purchase the insurance coverage and identifying a difference between the previous quotation and a new quotation. This difference may include an increase in offered insurance coverage and/or a reduction in cost. A computing device may calculate a probability for each of the individuals on the list using a regression model based, at least in part, on the identified difference. In some cases, the regression model may be associated with individual states. In other cases, the regression model may correspond to a plurality of states. The regression model may output a probability that a resident of a particular state will purchase insurance in response to a re-quotation for insurance coverage, where individuals may then be ranked based on the probability.