Predictive Modeling Platform for Real-Time Lead Scoring
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Solution Overview
Problem
For-profit education schools face challenges in rapidly deploying predictive scoring models to assess lead quality in real-time due to lack of data-science resources and budget, leading to costly and inconsistent lead generation.
Innovation Solution
A platform and method for rapidly deploying and integrating predictive scoring models, allowing schools to select and combine dynamic models for evaluating lead quality, providing a response based on combined results, and persisting models on existing lead-sourcing infrastructure, enabling efficient and cost-effective lead quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If schools use historical experience to source leads, then they can obtain leads from established providers, but the leads become expensive and quality becomes inconsistent
Solution Approach 1:
The patent replaces the manual, experience-based lead sourcing process with an automated predictive scoring system that uses machine learning models to evaluate lead quality in real-time, eliminating the need for costly trial-and-error approaches and historical dependency
Solution Approach 2:
The system enables schools to independently evaluate and score leads using their own data and custom-built predictive models, removing dependency on external lead generation firms and their pricing structures
2Loss of time
If schools build custom predictive scoring models, then they can achieve real-time lead quality assessment, but they lack data-science resources and budget
Solution Approach 1:
The patent creates a universal platform that can handle multiple predictive modeling tasks and deploy various scoring models across different schools and use cases, eliminating the need for each school to build and maintain separate data-science infrastructure
Solution Approach 2:
The patent introduces an intermediary platform that bridges the gap between schools and predictive modeling capabilities, handling the complexity of model building, deployment, and maintenance while schools simply use the scoring functionality
3Productivity
If schools deploy predictive scoring models rapidly, then they can improve lead quality assessment speed, but integration complexity increases
Solution Approach 1:
The patent segments the predictive modeling system into independent, modular components that can be deployed separately and integrated incrementally, reducing the complexity burden of rapid deployment
Solution Approach 2:
The patent enables rapid deployment through configurable parameters and settings that allow schools to customize model behavior and integration points without changing the underlying system architecture
Data Source
AI summary
A computer implemented method, comprising: selecting a plurality of dynamic models for evaluating a scoring request, wherein the dynamic models are stored on a scoring database; deploying each of the dynamic models to one of a plurality of evaluators; synchronizing the dynamic models, the synchronizing including at least determining a same version of the dynamic models is deployed and available to all evaluators; receiving, at a scoring node, a scoring request for a score of a lead from at least one requester; separating the scoring request into a plurality of scoring requests, wherein each of the scoring requests is assigned to one of the selected dynamic models wherein the request is separated by model and aggregate model results and sent to an evaluator queue; combining results from each of the dynamic models; evaluating the combined results wherein each of the evaluators sends a model evaluation response to the response queue; and providing a response to the scoring request based on the evaluation of the combined results.