Opportunity Scoring Algorithm for Real Estate Lead Routing
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Solution Overview
Problem
Real estate agents' income potential is limited due to random consumer choice and ineffective lead distribution, resulting in lost revenue, as existing marketing efforts fail to establish a strong connection between real estate companies and consumer leads.
Innovation Solution
A system and method for computing communication matrices based on opportunity scoring, using parameters like client device interaction time, geographic location, and number of registered devices, to determine the most qualified agent for lead assignment, thereby enhancing sales success and revenue through a hierarchical relationship matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If leads are assigned randomly to agents, then consumer choice is simple and quick, but sales success rate decreases and revenue is lost
Solution Approach 1:
The system changes the parameters of lead assignment from random selection to a scored selection based on multiple factors including agent expertise, availability, historical performance, and client preferences. This transforms the assignment mechanism into an optimized process that maintains simplicity for the consumer while improving sales success rates through data-driven decision making.
Solution Approach 2:
The patent replaces the mechanical/random system of lead assignment with an intelligent algorithmic system that automatically scores and ranks agents based on multiple criteria. This substitution eliminates the need for manual intervention while achieving both operational simplicity and improved productivity through automated optimization.
2Quantity of substance
If marketing efforts are increased to generate more leads, then consumer reach expands, but connection strength between company and leads weakens
Solution Approach 1:
The system performs preliminary actions by pre-scoring and pre-qualifying agents before lead assignment. This advance preparation ensures that when leads are generated through marketing efforts, they are immediately connected to the most suitable agents, maintaining strong connection strength even as the volume of leads increases.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor assignment outcomes and adjust scoring parameters accordingly. This feedback loop ensures that as more leads are generated, the system learns and adapts to maintain optimal connection strength between the company and its leads through improved assignment accuracy.
3Measurement precision
If a comprehensive scoring system with multiple parameters is implemented, then lead assignment accuracy improves, but system complexity increases
Solution Approach 1:
The comprehensive scoring system is segmented into multiple independent modules, each evaluating a specific parameter (agent expertise, availability, performance metrics, client preferences). This modular segmentation maintains high assignment accuracy while reducing overall system complexity by allowing independent development, testing, and maintenance of each scoring component.
4Manufacturing precision
If agents are evaluated on multiple criteria, then quality of agent selection improves, but processing time increases
Solution Approach 1:
The system performs preliminary evaluation and pre-calculates agent scores across multiple criteria before actual lead assignment occurs. This advance preparation maintains high agent selection quality by considering all relevant factors while reducing processing time during the actual assignment moment, as the scoring framework is already established and ready for rapid matching.
Data Source
AI summary
Systems and methods for computing communication matrices based on opportunity scoring of a plurality of network-connected user devices. In a preferred embodiment of the invention, a scoring algorithm may compute an opportunity score for future delivery of interactions comprising parameters such as a cumulative time comprising historic and real-time communication time between a client device with an agent; geographic location and distance between a client device and an agent device; a quantity of direct connections for one or more client devices associated to an agent device; a quantity of indirect connections for one or more client devices associated to the agent device; quantity metrics associated to an agent device, for example, a total number of registrations. An interaction manager then routs interactions based on scoring of agent devices.


